ISO/IEC PRF 8801

Information technology — 3D printing and scanning — Data standard operating procedure (SOP)

This document describes a standard operating procedure (SOP) of a test dataset for the evaluation of modelling from 3D scanned data in the aspect of Information Technology. This document classifies the main task phases and defines the required tasks in each phase on test data SOP. This document defines essential requirements of test data for evaluation of modelling from 3D scanned data and describing how to check them.

Technologies de l'information — Impression et numérisation 3D — Mode opératoire normalisé (SOP) pour les données

General Information

• Status: Not Published
• ICS: 35.020 - Information technology (IT) in general
• Technical Committee: ISO/IEC JTC 1 - Information technology
• Drafting Committee: ISO/IEC JTC 1 - Information technology
• Current Stage: 5020 - FDIS ballot initiated: 2 months. Proof sent to secretariat
• Start Date: 25-Feb-2026
• Completion Date: 28-Feb-2026

Overview

ISO/IEC PRF 8801: Information Technology - 3D Printing and Scanning - Data Standard Operating Procedure (SOP) is a draft international standard developed by the ISO/IEC Joint Technical Committee 1. The standard specifies a comprehensive, structured standard operating procedure (SOP) for managing test datasets in the context of 3D scanning and 3D printing, with a particular focus on the evaluation of modeling from 3D scanned data. By defining essential requirements and classifying tasks across key quality management phases, ISO/IEC PRF 8801 aims to support consistent, high-quality evaluation in 3D modeling workflows, especially where test data informs the quality and reliability of 3D models used in technological, industrial, and commercial applications.

Key Topics

• Test Dataset Quality Management
  ISO/IEC PRF 8801 outlines a full data quality management system for test datasets, including articulated roles, responsibilities, and mandatory documentation. It stresses the importance of maintaining process effectiveness and thorough record-keeping to ensure compliance.

• Data Lifecycle Phases (PDCA Cycle)
  The standard structures the SOP into the Plan-Do-Check-Act (PDCA) cycle, guiding users through:

  • Plan - Developing test data policies and planning for quality objectives
  • Do - Data acquisition, curation, pre-processing, augmentation, annotation, and de-identification
  • Check - Quality control measures and validation against requirements
  • Act - Final data structuring, reporting, and process improvement

• Data Preparation and Annotation
  Guidance covers:

  • Handling raw scanned data and ensuring de-identification for privacy
  • Documentation and execution of data processing steps such as normalization and interpolation
  • Establishing clear procedures for labeling/annotation, personnel qualifications, and quality assurance

• Quality Control and Reporting
  The standard defines how organizations should validate data and labeling quality, document results, and improve processes through continuous feedback and quality checks.

Applications

ISO/IEC PRF 8801 is designed for organizations engaged in:

• 3D Model Evaluation in R&D
  Enables research teams and engineers to maintain rigorous data standards when evaluating modeling algorithms, particularly those leveraging AI or machine learning on 3D scan data.

• Quality Assurance for 3D Printing
  Supports manufacturers and service providers with clear SOPs for test data management, promoting traceability and repeatability in 3D printing quality checks.

• Regulated and Sensitive Data Handling
  Provides frameworks for data anonymization and documentation, aligning with privacy and compliance requirements in sectors like healthcare or industrial inspection.

• Collaborative Consortia and Data Providers
  Establishes common terminology, test dataset requirements, and reporting structures that facilitate interoperability and data sharing between organizations, partners, and regulatory agencies.

Related Standards

• ISO/IEC 12207:2008 - Systems and software engineering - Software lifecycle processes
• ISO/IEC 25040:2011 - Systems and software engineering - Systems and software Quality Requirements and Evaluation (SQuaRE)
• ISO 9000:2005 - Quality management systems - Fundamentals and vocabulary
• ISO/IEC 14598-1:1999 - Software product evaluation - General overview
• ISO/IEC 15939:2007 - Systems and software engineering - Measurement process

Practical Value

The adoption of ISO/IEC PRF 8801 helps organizations:

• Standardize and document their approach to managing 3D scanned test data
• Enhance reproducibility and credibility of modeling evaluations
• Minimize errors and support continuous improvement in 3D printing and scanning workflows
• Meet regulatory and quality management requirements with confidence

Keywords: ISO/IEC 8801, 3D printing standard, 3D scanning, test dataset SOP, data quality management, 3D model evaluation, information technology standard, 3D data annotation, PDCA cycle, data preprocessing, quality assurance for 3D printing.

Standards Content (Sample)

DRAFT
International
Standard
ISO/IEC DIS 8801
ISO/IEC JTC 1
Information Technology — 3D
Secretariat: ANSI
Printing and Scanning — Data
Voting begins on:
Standard Operating Procedure
2024-07-01
(SOP)
Voting terminates on:
ICS: 35.020
2024-09-23
THIS DOCUMENT IS A DRAFT CIRCULATED
FOR COMMENTS AND APPROVAL. IT
IS THEREFORE SUBJECT TO CHANGE
AND MAY NOT BE REFERRED TO AS AN
INTERNATIONAL STANDARD UNTIL
PUBLISHED AS SUCH.
IN ADDITION TO THEIR EVALUATION AS
BEING ACCEPTABLE FOR INDUSTRIAL,
TECHNOLOGICAL, COMMERCIAL AND
USER PURPOSES, DRAFT INTERNATIONAL
STANDARDS MAY ON OCCASION HAVE TO
This document is circulated as received from the committee secretariat.
BE CONSIDERED IN THE LIGHT OF THEIR
POTENTIAL TO BECOME STANDARDS TO
WHICH REFERENCE MAY BE MADE IN
NATIONAL REGULATIONS.
RECIPIENTS OF THIS DRAFT ARE INVITED
TO SUBMIT, WITH THEIR COMMENTS,
NOTIFICATION OF ANY RELEVANT PATENT
RIGHTS OF WHICH THEY ARE AWARE AND TO
PROVIDE SUPPORTING DOCUMENTATION.
Reference number
ISO/IEC DIS 8801:2024(en)
© ISO/IEC 2024
DRAFT
ISO/IEC DIS 8801:2024(en)
International
Standard
ISO/IEC DIS 8801
ISO/IEC JTC 1
Information Technology — 3D
Secretariat: ANSI
Printing and Scanning — Data
Voting begins on:
Standard Operating Procedure (SOP)
ICS: 35.020
Voting terminates on:
THIS DOCUMENT IS A DRAFT CIRCULATED
FOR COMMENTS AND APPROVAL. IT
IS THEREFORE SUBJECT TO CHANGE
AND MAY NOT BE REFERRED TO AS AN
INTERNATIONAL STANDARD UNTIL
PUBLISHED AS SUCH.
IN ADDITION TO THEIR EVALUATION AS
BEING ACCEPTABLE FOR INDUSTRIAL,
© ISO/IEC 2024
TECHNOLOGICAL, COMMERCIAL AND
USER PURPOSES, DRAFT INTERNATIONAL
All rights reserved. Unless otherwise specified, or required in the context of its implementation, no part of this publication may
STANDARDS MAY ON OCCASION HAVE TO
be reproduced or utilized otherwise in any form or by any means, electronic or mechanical, including photocopying, or posting on
This document is circulated as received from the committee secretariat. BE CONSIDERED IN THE LIGHT OF THEIR
the internet or an intranet, without prior written permission. Permission can be requested from either ISO at the address below
POTENTIAL TO BECOME STANDARDS TO
WHICH REFERENCE MAY BE MADE IN
or ISO’s member body in the country of the requester.
NATIONAL REGULATIONS.
ISO copyright office
RECIPIENTS OF THIS DRAFT ARE INVITED
CP 401 • Ch. de Blandonnet 8
TO SUBMIT, WITH THEIR COMMENTS,
CH-1214 Vernier, Geneva
NOTIFICATION OF ANY RELEVANT PATENT
Phone: [phone]
RIGHTS OF WHICH THEY ARE AWARE AND TO
PROVIDE SUPPORTING DOCUMENTATION.
Email: [e-mail]
Website: www.iso.org
Published in Switzerland Reference number
ISO/IEC DIS 8801:2024(en)
© ISO/IEC 2024
© ISO/IEC 2024 – All rights reserved
ii
ISO/IEC DIS 8801:2024(en)
Contents Page
Foreword .iv
Introduction .v
1 Scope . 1
2 Normative references . 1
3 Terms, definitions and abbreviated terms . 1
3.1 Terms and definitions .1
3.2 Abbreviated terms .2
4 3D scanned and labeled data Standard Operating Procedure (SOP) . 2
4.1 Background .2
4.2 General requirements .3
4.2.1 Organization . . .3
4.2.2 For each quality management system process.3
4.3 Documentation requirements .4
4.3.1 General .4
4.3.2 Data Quality manual .4
4.4 Data level .4
4.5 PDCA (Plan Do Check Act) cycle for test data SOP .5
5 Plan phase . 6
5.1 General .6
5.2 Test data policy .6
5.3 Test data planning .6
6 Do phase . 7
6.1 General .7
6.2 Test data acquisition.7
6.2.1 Sample size .7
6.2.2 Data source .7
6.2.3 Data curation .7
6.2.4 Data augmentation .7
6.3 Test data de-identification .8
6.4 Test data transfer .8
6.5 Test data pre-processing .8
6.6 Test data labeling/annotation .9
6.6.1 Reference standard .9
6.6.2 Intra-observer variability .9
6.6.3 Inter-observer variability.10
7 Check phase .10
7.1 General .10
7.2 Test data quality control .10
7.3 Test data requirements .10
7.3.1 Test data acquisition requirements .10
7.3.2 Test data labeling requirements .10
8 Act phase .11
8.1 General .11
8.2 Test data formatting and structuring .11
8.2.1 Test original data formatting .11
8.2.2 Test labeling and annotation data formatting .11
8.3 Test data reporting .11
8.4 Test data improvement .11
Bibliography .12

© ISO/IEC 2024 – All rights reserved
iii
ISO/IEC DIS 8801:2024(en)
Foreword
ISO (the International Organization for Standardization) is a worldwide federation of national standards
bodies (ISO member bodies). The work of preparing International Standards is normally carried out through
ISO technical committees. Each member body interested in a subject for which a technical committee
has been established has the right to be represented on that committee. International organizations,
governmental and non-governmental, in liaison with ISO, also take part in the work. ISO collaborates closely
with the International Electrotechnical Commission (IEC) on all matters of electrotechnical standardization.
The procedures used to develop this document and those intended for its further maintenance are described
in the ISO/IEC Directives, Part 1. In particular the different approval criteria needed for the different types
of ISO documents should be noted. This document was drafted in accordance with the editorial rules of the
ISO/IEC Directives, Part 2 (see www.iso.org/directives).
Attention is drawn to the possibility that some of the elements of this document may be the subject of patent
rights. ISO shall not be held responsible for identifying any or all such patent rights. Details of any patent
rights identified during the development of the document will be in the Introduction and/or on the ISO list of
patent declarations received (see www.iso.org/patents).
Any trade name used in this document is information given for the convenience of users and does not
constitute an endorsement.
For an explanation on the meaning of ISO specific terms and expressions related to conformity assessment,
as well as information about ISO's adherence to the World Trade Organization (WTO) principles in the
Technical Barriers to Trade (TBT) see the following URL: www.iso.org/iso/foreword.html.
The committee responsible for this document is ISO/IEC JTC 1.

© ISO/IEC 2024 – All rights reserved
iv
ISO/IEC DIS 8801:2024(en)
Introduction
This standard was developed in response to the needs of quality management of 3D printing and scanning
technology by taking full advantage of information and communication technology (ICT).
From scanned data to 3D printing, devices are quite a complex journey with complicated management of
cascades of software. Despite the emerging market for 3D printing, there are quite a lot of points requiring
standardization for their manufacture especially for modelling from 3D scanned data. 3D printing requires
many tasks and stages from 3D scanned data acquisition to its application, and a review process is required
to minimize errors and improve quality in this process.
However, there is currently no standardized evaluation procedure for the process of turning scanned data
into consistent and accurate 3D printed objects, making continuous quality assessment and improvement
activities impossible.
For evaluation of the modelling from 3D scanned data, minimal requirement is needed for the test data to
achieve rigorous and comprehensive evaluation. Thus, it is very important to discuss how to organize and
collect the test data.
In the definition of the standard evaluation procedure for the modelling process from 3D scanned data,
the standard for the test data for this should also be specified. In this context, this document describes the
standard operating procedure (SOP) and necessary requirements for curation and quality control of test
data for evaluation of modelling from 3D scanned data.

© ISO/IEC 2024 – All rights reserved
v
DRAFT International Standard ISO/IEC DIS 8801:2024(en)
Information Technology — 3D Printing and Scanning — Data
Standard Operating Procedure (SOP)
1 Scope
This document describes a standard operating procedure (SOP) of a test data for the evaluation of modelling
from 3D scanned data. And the test data consists of 3D scanned data and its corresponding label data.
This document classifies the main task phases and defines the required tasks in each phase on 3D scanned
and labeled data SOP.
This document defines essential requirements for test data for evaluating modelling from 3D scanned data
and describing how to check them.
2 Normative references
The following documents are referred to in the text in such a way that some or all of their content constitutes
requirements of this document. For dated references, only the edition cited applies. For undated references,
the latest edition of the referenced document (including any amendments) applies.
3 Terms, definitions and abbreviated terms
ISO and IEC maintain terminological databases for use in standardization at the following addresses:
— ISO Online browsing platform: available at https: //www .iso .org/obp
— IEC Electropedia: available at http: //www .electropedia .org/
3.1 Terms and definitions
3.1.1
evaluation
systematic determination of the extent to which an entity meets its specified criteria
[SOURCE: ISO/IEC 12207:2008, 4.12]
3.1.2
measure
variable to which a value is assigned as the result of measurement
Note 1 to entry: The term “measures” is used to refer collectively to base measures, derived measures, and indicators.
Note 2 to entry: Adapted from ISO/IEC 14598-1:1999.
[SOURCE: ISO/IEC 25040:2011, 4.38]
3.1.3
measure
make a measurement
[SOURCE: ISO/IEC 14598-1:1999]

© ISO/IEC 2024 – All rights reserved
ISO/IEC DIS 8801:2024(en)
3.1.4
measurement
set of operations having the object of determining a value of a measure
[SOURCE: ISO/IEC 15939:2007, 3.17]
3.1.5
process
system of activities, which uses resources to transform inputs into outputs
Note 1 to entry: Adapted from ISO 9000:2005.
[SOURCE: ISO/IEC 25040:2011, 4.47]
3.1.6
annotation
process of adding additional information to a data object to improve its interpretation, organization, or use
3.1.7
oversampling
method of increasing the number of observations in a dataset by adding additional data points
3.2 Abbreviated terms
AI Artificial Intelligence
CT Computed Tomography
DICOM Digital Imaging and Communications in Medicine
ML Machine Learning
SOP Standard Operating Procedure
4 3D scanned and labeled data Standard Operating Procedure (SOP)
4.1 Background
In spite of the expansion of 3D printing applications, there are inaccurate modelling from 3D scanned data
and precision evaluation issues. Normally, dimensional precision of under 1mm is assumed to be inevitable,
but there is still no agreed standard to practically acceptable measurement accuracy and evaluation process
for modelling from 3D scanned data. The modelling can be attempted with Artificial Intelligence (AI)/
Machine Learning (ML) technology with 3D scanned data for 3D printing applications.
As such, there remains a need to understand how model assessment can be affected by each activity within
the 3D scanned and labeled data acquisition workflow.
The AI model finds a pattern from data itself to meet a desired goal, and it is also verified using data. For
training data of ML-based (specifically, deep learning) model, rule of thumb is that collecting as much data
as possible to improve the performance of the model. However, for test data of ML-based model, testing with
big data is not practical. Thus, minimal requirement is needed for rigorous and comprehensive evaluation of
ML-based model. It is very important to discuss how to organize and collect the test data.
In this regard, a 3D scanned and labeled data SOP composing a test data quality management system can be
defined.
© ISO/IEC 2024 – All rights reserved
ISO/IEC DIS 8801:2024(en)
4.2 General requirements
4.2.1 Organization
The organization shall document a test data quality management system and maintain its effectiveness in
accordance with the requirements of this SOP. The organization shall establish, implement and maintain any
requirement, procedure, activity, or arrangement required to be documented by this SOP.
The organizat
...

International
Standard
ISO/IEC 8801
First edition
Information technology — 3D
printing and scanning — Data
standard operating procedure
(SOP)
Technologies de l'information — Impression et numérisation 3D
— Mode opératoire normalisé (SOP) pour les données
PROOF/ÉPREUVE
Reference number
ISO/IEC 8801:2026(en) © ISO/IEC 2026

ISO/IEC 8801:2026(en)
© ISO/IEC 2026
All rights reserved. Unless otherwise specified, or required in the context of its implementation, no part of this publication may
be reproduced or utilized otherwise in any form or by any means, electronic or mechanical, including photocopying, or posting on
the internet or an intranet, without prior written permission. Permission can be requested from either ISO at the address below
or ISO’s member body in the country of the requester.
ISO copyright office
CP 401 • Ch. de Blandonnet 8
CH-1214 Vernier, Geneva
Phone: [phone]
Email: [e-mail]
Website: www.iso.org
Published in Switzerland
PROOF/ÉPREUVE
© ISO/IEC 2026 – All rights reserved
ii
ISO/IEC 8801:2026(en)
Contents Page
Foreword .iv
Introduction .v
1 Scope . 1
2 Normative references . 1
3 Terms, definitions and abbreviated terms . 1
3.1 Terms and definitions .1
3.2 Abbreviated terms .2
4 3D scanned and labelled data standard operating procedure (SOP) . 2
4.1 General requirements .2
4.1.1 Entity .2
4.1.2 Test data quality assurance procedures .3
4.2 Documentation requirements .4
4.2.1 General .4
4.2.2 Data quality manual .4
4.3 Data level .4
4.4 Data process flow for test data SOP .4
5 Define and design stage. 6
5.1 General .6
5.2 Test data policy .6
5.3 Test data planning .6
6 Acquire and pre-process stage . 7
6.1 General .7
6.2 Test data acquisition.7
6.2.1 Sample size .7
6.2.2 Data source .7
6.2.3 Data curation .7
6.2.4 Data augmentation .7
6.3 Test data de-identification .8
6.4 Test data transfer .8
6.5 Test data preprocessing .8
6.5.1 General .8
6.5.2 Intensity normalization .8
6.5.3 Resampling and interslice interpolation .9
6.6 Test data labelling/annotation .9
6.6.1 Reference standard .9
6.6.2 Intra-observer variability .10
6.6.3 Inter-observer variability.10
7 Validate and analyze stage .10
7.1 General .10
7.2 Test data quality control .10
7.3 Test data requirements .10
7.3.1 Test data acquisition requirements .10
7.3.2 Test data labelling requirements .11
8 Optimize and update stage .11
8.1 General .11
8.2 Test data formatting and structuring .11
8.2.1 Test original data formatting .11
8.2.2 Test labelling and annotation data formatting . .11
8.3 Test data reporting .11
8.4 Test data improvement .11
Bibliography .12
PROOF/ÉPREUVE
© ISO/IEC 2026 – All rights reserved
iii
ISO/IEC 8801:2026(en)
Foreword
ISO (the International Organization for Standardization) and IEC (the International Electrotechnical
Commission) form the specialized system for worldwide standardization. National bodies that are
members of ISO or IEC participate in the development of International Standards through technical
committees established by the respective organization to deal with particular fields of technical activity.
ISO and IEC technical committees collaborate in fields of mutual interest. Other international organizations,
governmental and non-governmental, in liaison with ISO and IEC, also take part in the work.
The procedures used to develop this document and those intended for its further maintenance are described
in the ISO/IEC Directives, Part 1. In particular, the different approval criteria needed for the different types
of document should be noted. This document was drafted in accordance with the editorial rules of the ISO/
IEC Directives, Part 2 (see www.iso.org/directives or www.iec.ch/members_experts/refdocs).
ISO and IEC draw attention to the possibility that the implementation of this document may involve the
use of (a) patent(s). ISO and IEC take no position concerning the evidence, validity or applicability of any
claimed patent rights in respect thereof. As of the date of publication of this document, ISO and IEC had not
received notice of (a) patent(s) which may be required to implement this document. However, implementers
are cautioned that this may not represent the latest information, which may be obtained from the patent
database available at www.iso.org/patents and https://patents.iec.ch. ISO and IEC shall not be held
responsible for identifying any or all such patent rights.
Any trade name used in this document is information given for the convenience of users and does not
constitute an endorsement.
For an explanation of the voluntary nature of standards, the meaning of ISO specific terms and expressions
related to conformity assessment, as well as information about ISO's adherence to the World Trade
Organization (WTO) principles in the Technical Barriers to Trade (TBT) see www.iso.org/iso/foreword.html.
In the IEC, see www.iec.ch/understanding-standards.
This document was prepared by Joint Technical Committee ISO/IEC JTC 1, Information technology.
Any feedback or questions on this document should be directed to the user’s national standards
body. A complete listing of these bodies can be found at www.iso.org/members.html and
www.iec.ch/national-committees.
PROOF/ÉPREUVE
© ISO/IEC 2026 – All rights reserved
iv
ISO/IEC 8801:2026(en)
Introduction
This document was developed in response to the quality management needs of 3D printing and scanning
technology, with the aim of taking full advantage of information and communication technology (ICT) in
this context.
However, there is currently no standardized evaluation procedure for the process of turning scanned data
into consistent and accurate 3D printed objects, making continuous quality assessment and improvement
activities impossible. Issues with precision evaluation and inaccurate modelling from 3D scanned data exist,
but no agreed standard specifies practically acceptable measurement accuracy or an evaluation process.
Artificial intelligence/machine learning (AI/ML) technology can be used for generating and optimizing 3D
models from 3D scanned data, particularly for enhancing design accuracy and efficiency. For the training
data of an ML-based model, the collection of as much data as possible (big data) is considered to improve
the performance of the model. However, it is not practical to use big data as test data. Therefore, minimal
requirements are necessary for achieving rigorous and comprehensive evaluation.
In this context, this document describes the standard operating procedure (SOP) and necessary requirements
for curation and quality control of test data for evaluation of modelling from 3D scanned data.
PROOF/ÉPREUVE
© ISO/IEC 2026 – All rights reserved
v
International Standard ISO/IEC 8801:2026(en)
Information technology — 3D printing and scanning — Data
standard operating procedure (SOP)
1 Scope
This document describes a standard operating procedure (SOP) for the curation and quality control of test
data, consisting of 3D scanned data and its corresponding label data, for the evaluation of modelling from 3D
scanned data.
This document classifies the main task phases and defines the required tasks in each phase of the 3D
scanned and labelled data SOP.
This document defines essential requirements for test data for evaluating modelling from 3D scanned data
and describes how to verify conformance to these requirements.
2 Normative references
There are no normative references in this document.
3 Terms, definitions and abbreviated terms
For the purposes of this document, the following terms and definitions apply.
ISO and IEC maintain terminology databases for use in standardization at the following addresses:
— ISO Online browsing platform: available at https:// www .iso .org/ obp
— IEC Electropedia: available at https:// www .electropedia .org/
3.1 Terms and definitions
3.1.1
measure
variable to which a value is assigned as the result of measurement
Note 1 to entry: The plural form “measures” is used to refer collectively to base measures, derived measures, and
indicators.
[SOURCE: ISO/IEC/IEEE 15939:2017, 3.15]
3.1.2
measure
make a measurement
[SOURCE: ISO/IEC/IEEE 15939:2017, 3.16]
3.1.3
measurement
set of operations having the object of determining a value of a measure
[SOURCE: ISO/IEC/IEEE 15939:2017, 3.17, modified — Note 1 to entry has been removed.]
PROOF/ÉPREUVE
© ISO/IEC 2026 – All rights reserved
ISO/IEC 8801:2026(en)
3.1.4
process
system of activities, which uses resources to transform inputs into outputs
[SOURCE: ISO/IEC 25000:2014, 4.22]
3.1.5
annotation
process of adding additional information to a data object to improve its interpretation, organization or use
3.1.6
oversampling
method of increasing the number of observations in a dataset by adding additional data points
3.1.7
standard operating procedure
SOP
authorized, documented procedure, or set of procedures, work instructions and test instructions
[SOURCE: ISO 15378:2017, 3.7.10]
3.2 Abbreviated terms
AI artificial intelligence
CNN convolutional neural network
CT computed tomography
DICOM digital imaging and communications in medicine
EHR electronic health records
GAN generative adversarial network
MAE mean absolute error
ML machine learning
MSE mean squared error
PACS picture archiving and communication system
SOP standard operating procedure
4 3D scanned and labelled data standard operating procedure (SOP)
4.1 General requirements
4.1.1 Entity
An entity may act as a data provider, data management institution, or software developer, and represents
a legal or organizational body as commonly defined in ISO data-related standards. An entity implementing
the SOP specified in this document shall document the test data quality assurance procedures and maintain
its effectiveness in accordance with the requirements of the SOP. The entity shall establish, implement and
maintain any requirement, procedure, activity or arrangement required to be documented by this SOP.
PROOF/ÉPREUVE
© ISO/IEC 2026 – All rights reserved
ISO/IEC 8801:2026(en)
The entity shall document the role(s) undertaken by the entity under the applicable regulatory requirements.
NOTE Roles undertaken by the entity can include manufacturer, data provider, labelling data provider, data
manager or software developer.
The entity shall:
a) determine the processes needed for the data quality assurance procedures and the application of these
processes throughout the entity, taking into account the roles undertaken by the entity;
b) apply a risk-based approach to the control of the appropriate processes needed for the data quality
assurance procedures;
c) determine the sequence and interaction of these processes;
d) set and manage the competence of the data manager.
4.1.2 Test data quality assurance procedures
For each test data quality assurance procedure, the entity shall:
a) determine criteria and methods needed to ensure that both the operation and control of these processes
are effective;
b) ensure the availability of resources and information necessary to support the operation and monitoring
of these processes;
c) implement actions necessary to achieve planned results and maintain the effectiveness of these
processes;
d) monitor, measure as appropriate, and analyze these processes;
e) establish and maintain records needed to demonstrate conformance to this document and compliance
with applicable regulatory requirements (see 5.3).
The entity shall manage these data quality assurance procedures in accordance with the requirements of
this document and applicable regulatory requirements. Changes to be made to these processes shall be:
a) evaluated for their impact on the data quality assurance procedures;
b) evaluated for their impact on the 3D printing produced under these data quality assurance procedures;
c) controlled in accordance with the requirements of this SOP.
When the entity chooses to outsource any process that affects product conformity to requirements, it shall
monitor and ensure control over such processes. The entity shall retain the responsibility of conformity
to this document and to customers and applicable regulatory requirements for outsourced processes.
The controls shall be proportionate to the risk involved and the ability of the external party to meet the
requirements in accordance with 5.3. The controls shall include written quality agreements.
The entity shall document procedure
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2025-12-08
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ISO/IEC DISPRF 8801:2025(en)
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ISO/IEC JTC 1/WG 12
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Secretariat: ANSI
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Date: 2026-02-25
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Information technology — 3D printing and scanning — Data
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St l D fi iti
ISO/IEC JTC 1 - N:####(XPRF 8801:2026(en)
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ii © ISO #### /IEC 2026 – All rights reserved
ii
ISO/IEC DISPRF 8801:20252026(en) Formatted: Font: 11 pt
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Contents
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Foreword . v
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Introduction . vi
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1 Scope . 1
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2 Normative references . 1
3 Terms, definitions and abbreviated terms . 1
3.1 Terms and definitions . 1
3.2 Abbreviated terms . 2
4 3D scanned and labelled data standard operating procedure (SOP) . 3
4.1 General requirements . 3
4.2 Documentation requirements . 4
4.3 Data level . 4
4.4 Data process flow for test data SOP . 5
5 Define and design stage . 8
5.1 General. 8
5.2 Test data policy . 8
5.3 Test data planning . 8
6 Acquire and pre-process stage . 8
6.1 General. 8
6.2 Test data acquisition . 8
6.3 Test data de-identification . 9
6.4 Test data transfer . 9
6.5 Test data preprocessing . 10
6.6 Test data labelling/annotation . 11
7 Validate and analyze stage . 12
7.1 General. 12
7.2 Test data quality control . 12
7.3 Test data requirements . 12
8 Optimize and update stage . 13
8.1 General. 13
8.2 Test data formatting and structuring . 13
8.3 Test data reporting. 13
8.4 Test data improvement . 13
Bibliography . 14

Foreword . v
Introduction . vi
1 Scope . 1
2 Normative references . 1
3 Terms, definitions and abbreviated terms . 1
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3.1 Terms and definitions . 1
3.2 Abbreviated terms . 2
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4 3D scanned and labelled data Standard Operating Procedure (SOP) . 2
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4.1 General requirements . 2
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4.1.1 Entity . 2
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© ISO/IEC 2024 2026 – All rights reserved
iii
ISO/IEC JTC 1 - N:####(XPRF 8801:2026(en)
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4.1.2 Test data quality assurance procedures . 3
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4.2 Documentation requirements . 4
4.2.1 General. 4
4.2.2 Data quality manual . 4
4.3 Data level . 4
4.4 Data process flow for test data SOP . 4
5 Define and Design stage. 6
5.1 General. 6
5.2 Test data policy . 6
5.3 Test data planning . 7
6 Acquire and Preprocess stage . 7
6.1 General. 7
6.2 Test data acquisition . 7
6.2.1 Sample size . 7
6.2.2 Data source . 7
6.2.3 Data curation . 7
6.2.4 Data augmentation . 7
6.3 Test data de-identification . 8
6.4 Test data transfer . 8
6.5 Test data pre-processing . 8
6.5.1 General. 8
6.5.2 Intensity normalization: . 8
6.5.3 Resampling and interslice interpolation: . 9
6.6 Test data labelling/annotation . 9
6.6.1 Reference standard . 9
6.6.2 Intra-observer variability . 10
6.6.3 Inter-observer variability . 10
7 Validate and Analyze stage . 10
7.1 General. 10
7.2 Test data quality control . 10
7.3 Test data requirements . 10
7.3.1 Test data acquisition requirements . 10
7.3.2 Test data labelling requirements . 11
8 Optimize and Update stage . 11
8.1 General. 11
8.2 Test data formatting and structuring . 11
8.2.1 Test original data formatting . 11
8.2.2 Test labelling and annotation data formatting . 11
8.3 Test data reporting. 11
8.4 Test data improvement . 11
Bibliography . 12

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iv © ISO #### /IEC 2026 – All rights reserved
iv
ISO/IEC DISPRF 8801:20252026(en) Formatted: Font: 11 pt
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Foreword
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ISO (the International Organization for Standardization) and IEC (the International Electrotechnical Formatted: HeaderCentered, Left, Space After: 0 pt
Commission) form the specialized system for worldwide standardization. National bodies that are members
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of ISO or IEC participate in the development of International Standards through technical committees
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established by the respective organization to deal with particular fields of technical activity. ISO and IEC
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technical committees collaborate in fields of mutual interest. Other international organizations, governmental
and non-governmental, in liaison with ISO and IEC, also take part in the work.
The procedures used to develop this document and those intended for its further maintenance are described
in the ISO/IEC Directives, Part 1. In particular, the different approval criteria needed for the different types of
document should be noted. This document was drafted in accordance with the editorial rules of the ISO/IEC
Directives, Part 2 (see www.iso.org/directiveswww.iso.org/directives or
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www.iec.ch/members_experts/refdocswww.iec.ch/members_experts/refdocs).
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ISO and IEC draw attention to the possibility that the implementation of this document may involve the use of
(a) patent(s). ISO and IEC take no position concerning the evidence, validity or applicability of any claimed
patent rights in respect thereof. As of the date of publication of this document, ISO and IEC [had/had not]
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received notice of (a) patent(s) which may be required to implement this document. However, implementers
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are cautioned that this may not represent the latest information, which may be obtained from the patent
database available at www.iso.org/patents and https://patents.iec.ch.www.iso.org/patents and
https://patents.iec.ch. ISO and IEC shall not be held responsible for identifying any or all such patent rights.
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Any trade name used in this document is information given for the convenience of users and does not
constitute an endorsement.
For an explanation of the voluntary nature of standards, the meaning of ISO specific terms and expressions
related to conformity assessment, as well as information about ISO's adherence to the World Trade
Organization (WTO) principles in the Technical Barriers to Trade (TBT) see
www.iso.org/iso/foreword.html.www.iso.org/iso/foreword.html. In the IEC, see www.iec.ch/understanding-
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standardswww.iec.ch/understanding-standards.
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This document was prepared by Joint Technical Committee ISO/IEC JTC 1, Information technology.
Any feedback or questions on this document should be directed to the user’s national standards body. A
complete listing of these bodies can be found at www.iso.org/members.htmlwww.iso.org/members.html and
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www.iec.ch/national-committeeswww.iec.ch/national-committees.
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© ISO/IEC 2024 2026 – All rights reserved
v
ISO/IEC JTC 1 - N:####(XPRF 8801:2026(en)
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Introduction
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This document was developed in response to the quality management needs of 3D printing and scanning
technology, with the aim of taking full advantage of information and communication technology (ICT) in this
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context. Adjust space between Asian text and numbers
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However, there is currently no standardized evaluation procedure for the process of turning scanned data into
consistent and accurate 3D printed objects, making continuous quality assessment and improvement
activities impossible. Issues with precision evaluation and inaccurate modelling from 3D scanned data exist,
but no agreed standard specifies practically acceptable measurement accuracy or an evaluation process.
Artificial Intelligenceintelligence/machine learning (AI) /Machine Learning (/ML) technology can be
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utilizedused for generating and optimizing 3D models from 3D scanned data, particularly for enhancing design
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accuracy and efficiency. For the training data of an ML-based model, the collection of as much data as possible
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(big data) is considered to improve the performance of the model. However, it is not practical to use big data
as test data. Therefore, minimal requirements are necessary for achieving rigorous and comprehensive
evaluation.
In this context, this document describes the standard operating procedure (SOP) and necessary requirements
for curation and quality control of test data for evaluation of modelling from 3D scanned data.

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vi © ISO #### /IEC 2026 – All rights reserved
vi
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ISO/IEC DISPRF 8801:20252026(en)
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Information Technology technology — 3D Printingprinting and
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Scanningscanning — Data standard operating procedure (SOP)
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1 Scope
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This document describes a standard operating procedure (SOP) for the curation and quality control of test
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data, consisting of 3D scanned data and its corresponding label data, for the evaluation of modelling from 3D
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scanned data.
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This document classifies the main task phases and defines the required tasks in each phase of the 3D scanned Formatted
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and labelled data SOP.
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This document defines essential requirements for test data for evaluating modelling from 3D scanned data .
and describes how to verify compliance withconformance to these requirements.
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2 Normative references
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There are no normative references in this document.
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3 Terms, definitions and abbreviated terms
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For the purposes of this document, the following terms and definitions apply.
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ISO and IEC maintain terminology databases for use in standardization at the following addresses:
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— — ISO Online browsing platform: available at https://www.iso.org/obphttps://www.iso.org/obp
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— — IEC Electropedia: available at https://www.electropedia.org/https://www.electropedia.org/ .
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3.1 Terms and definitions
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3.1.1 3.1.1
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measure, noun
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variable to which a value is assigned as the result of measurement
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Note 1 to entry: The termplural form “measures” is used to refer collectively to base measures, derived measures, and
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indicators.
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Note 2 to entry: Adapted from ISO/IEC 14598-1:1999.
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[SOURCE: ISO/IEC 25040:2011, 4.38]
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/IEEE 15939:2017, 3.1.215]
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measure, verb
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make a measurement
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[SOURCE: ISO/IEC 14598-1:1999]
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/IEEE 15939:2017, 3.1.316]
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© ISO/IEC 2024 2026 – All rights reserved
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ISO/IEC JTC 1 - N:####(XPRF 8801:2026(en)
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3.1.3
measurement
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set of operations having the object of determining a value of a measure
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[SOURCE: ISO/IEC/IEEE 15939:2017, 3.17], modified — Note 1 to entry has been removed.]
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3.1.4 3.1.4
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process
system of activities, which uses resources to transform inputs into outputs
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Note 1 to entry: Adapted from ISO 9000:2005.
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[SOURCE: ISO/IEC 25040:201125000:2014, 4.4722]
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3.1.5 3.1.5 .
annotation
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process of adding additional information to a data object to improve its interpretation, organization, or use
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3.1.6 3.1.6
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oversampling
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method of increasing the number of observations in a dataset by adding additional data points
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3.1.7 3.1.7
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standard operating procedure
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SOP
authorized, documented procedure, or set of procedures, work instructions and test instructions Formatted
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[SOURCE: ISO 15378:2017(en),, 3.7.10]
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3.2 Abbreviated terms
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AI artificial intelligence
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CNN convolutional neural network
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CT computed tomography
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DICOM digital imaging and communications in medicine .
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EHR electronic health records
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GAN generative adversarial network
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MAE mean absolute error
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ML machine learning
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MSE mean squared error
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PACS picture archiving and communication system
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SOP standard operating procedure
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2 © ISO #### /IEC 2026 – All rights reserved
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4 3D scanned and labelled data Standard Operating Procedurestandard operating
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procedure (SOP)
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4.1 General requirements
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4.1.1 Entity and numbers
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An entity may act as a data provider, data management institution, or software developer, and represents a
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legal or organizational body as commonly defined in ISO data-related standards. An entity implementing the
SOP specified in this document shall document the test data quality assurance procedures and maintain its Formatted: Adjust space between Latin and Asian text,
effectiveness in accordance with the requirements of the SOP. The entity shall establish, implement and Adjust space between Asian text and numbers, Tab
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maintain any requirement, procedure, activity, or arrangement required to be documented by this SOP.
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The entity shall document the role(s) undertaken by the entity under the applicable regulatory requirements.
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NOTE Roles undertaken by the entity can include manufacturer, data provider, labelling data provider, data manager
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or software developer.
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The entity shall:
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a) a) determine the processes needed for the data quality assurance procedures and the application
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of these processes throughout the entity, taking into account the roles undertaken by the entity;
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b) b) apply a risk-based approach to the control of the appropriate processes needed for the data
quality assurance procedures;
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c) c) determine the sequence and interaction of these processes;
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d) d) set and manage the competence of the data manager.
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4.1.2 Test data quality assurance procedures
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For each test data quality assurance proceduresprocedure, the entity shall:
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a) a) determine criteria and methods needed to ensure that both the operation and control of these
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processes are effective;
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b) b) ensure the availability of resources and information necessary to support the operation and
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monitoring of these processes;
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c) c) implement actions necessary to achieve planned results and maintain the effectiveness of these
processes;
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d) d) monitor, measure as appropriate, and analyze these processes;
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e) e) establish and maintain records needed to demonstrate conformance to this document and
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compliance with applicable regulatory requirements (see 5.3).5.3).
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The entity shall manage these data quality assurance procedures in accordance with the requirements of this Formatted
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document and applicable regulatory requirements. Changes to be made to these processes shall be:
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a) a) evaluated for their impact on the data quality assurance procedures;
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ISO/IEC JTC 1 - N:####(XPRF 8801:2026(en)
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b) b) evaluated for their impact on the 3D printing produced under thisthese data quality assurance
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procedures;
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c) c) controlled in accordance with the requirements of this SOP.
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When the entity chooses to outsource any process that affects product conformity to requirements, it shall Formatted: Font color: Auto
monitor and ensure control over such processes. The entity shall retain the responsibility of conformity to this
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document and to customers and applicable regulatory requirements for outsourced processes. The controls
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shall be proportionate to the risk involved and the ability of the external party to meet the requirements in
accordance with 5.3.5.3. The controls shall include written quality agreements.
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The entity shall document procedures for the validation of the application of computer software used in the
data quality assurance procedures. Such software applications shall be validated prior to initial use and, as
appropriate, after changes to such software or its application.
The specific approach and activities associated with software validation and revalidation shall be
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proportionate to the risk associated with the use of the software.
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Records of such activities shall be maintained (see 5.3).5.3).
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4.2 Documentation requirements
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4.2.1 General
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The data quality assurance procedures documentation shall include:
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a) a) documented statements of a data quality policy and quality objectives; Formatted: Font color: Auto
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b) b) a data quality manual;
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c) c) documented procedures and records required by this SOP;
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numbers, Tab stops: Not at 0.7 cm + 1.4 cm + 2.1 cm
d) d) documents, including records, determined by the entity to be necessary to ensure the effective
+ 2.8 cm + 3.5 cm + 4.2 cm + 4.9 cm + 5.6 cm + 6.3
planning, operation and control of its processes.
cm + 7 cm
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4.2.2 Data quality manual
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The entity shall document a data quality manual that includes:
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a) a) the scope of the data quality assurance procedures, including details of and justification for any
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exclusion or non-application;
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b) b) the documented procedures for the data quality assurance procedures, or reference to them;
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c) c) a description of the interaction between the processes of the data quality assurance
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procedures.
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4.3 Data level
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By proceeding with each task of the SOP, the level of the test data curated and qualified can be defined as
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a) a) Raw data: Original data retrieved from storage systems may include electronic health records
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(EHR), non-destructive testing datasets such as DICON-DE, or medical images maintained in a picture
archiving and communication system (PACS).
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b) b) De-identified data: Anonymized or pseudo-anonymized data for data sharing and utilization to
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ensure privacy.
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c) c) Pre-processed data: Data that have undergone pre-processing such as histogram equalization
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or inter-slice interpolation after extracting necessary information from the raw data to apply to the
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desired ML-model.
d) d) Labelled data: Data annotated with ground truth information by domain experts to evaluate
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the performance of an ML-model. Technically, it is feasible to utilize data at this level for evaluation tasks.
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e) e) Qualified data: Data that have passed the quality check against the test data requirements. With
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this level of data, it is possible to secure the reliability of the results for ML-model evaluation.
f) f) Structured data: Data that have been refined into a form that can be shared and utilized for
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evaluation of an ML-model.
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4.4 Data process flow for test data SOP
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For systematic and reproducible data handling, the test data Standard Operating Procedure (SOP) is defined
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in a stepwise data process flow, consisting of four major procedural stages. Each stage represents a distinct
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technical objective in the preparation and verification of 3D scanned data for model evaluation and printing
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accuracy. This structure provides a traceable path from initial data definition to refinement and improvement,
ensuring the reliability and consistency of test data.
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The four stages are as follows: Formatted: Font color: Auto
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a) a) Define and Designdesign stage — In this stage, the objectives, requirements, and specifications for test
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data acquisition are identified and structured. Strategies for data collection, quality criteria, and
applicable standards are determined, forming the basis of the test data plan.
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b) b) Acquire and Prepre-process stage — In this stage, the defined action plans are implemented to collect,
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pre-process, and refine the 3D scanned data. Activities include data acquisition, de-identification, transfer,
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pre-processing (e.g. normalization, resampling),) and labelling/annotation, following the criteria defined
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in the previous stage.
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c) c) Validate and Analyzeanalyze stage — In this stage, the curated data are reviewed and validated against
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the predefined requirements and quality metrics. The entity performs quantitative and qualitative
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analyses to confirm data integrity, completeness, and conformance to labelling and acquisition standards.
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d) d) Optimize and Updateupdate stage — In this stage, results from the validation stage are reviewed to
identify opportunities for enhancement of data quality, structure, and procedural efficiency. Standard Formatted
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formats for data structuring and reporting are defined, and improvements are incorporated into the
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updated SOP for continuous technical refinement.
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© ISO/IEC 2024 2026 – All rights reserved
ISO/IEC JTC 1 - N:####(XPRF 8801:2026(en)
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At the end of the Optimize“optimize and Updateupdate” stage, the data process flow may be repeated to
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incorporate additional datasets, refine quality control parameters, or verify new validation results. This allows
for sustained technical improvement of the test data repository in a process-driven manner (see Figure
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1).Figure 1).
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Figure 1 — Data process flow for test data SOP
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numbers
Detailed tasks and expected outputs corresponding to each stage and the resulting test data levels are defined
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in Figure 2.Figure 2. The final output of the test data SOP consists of a validated test dataset and its
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corresponding dataset report, which can be directly applied to 3D modelling and printing evaluations.
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Figure 2 — Detailed tasks and outputs belonging to each stage
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© ISO/IEC 2024 2026 – All rights reserved
ISO/IEC JTC 1 - N:####(XPRF 8801:2026(en)
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5 Define and Designdesign stage
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5.1 General
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In this stage, the overall framework and policy for test data preparation are defined. The objectives, scope, and
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essential requirements for evaluating whether the model meets its intended design or functional purpose are
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specified. Key tasks include defining the test data policy, determining quality objectives, and establishing
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measurable criteria and planning methods. A structured checklist for subsequent stages is also developed in
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this stage.
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5.2 Test data policy
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The top data manager shall ensure that data quality objectives, including those needed to meet applicable
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operating requirements and requirements for the development, are established at relevant functional points
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and levels within the entity. The quality objectives shall be measurable and consistent with the data quality
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policy.
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5.3 Test data planning
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The top data manager shall ensure that:
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a) a) the planning of the data quality assurance procedures is carried out in order to meet the
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requirements given in 5.2,5.2, as well as the data quality objectives;
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b) b) the integrity of the data quality assurance procedures is maintained when changes to the data
quality assurance procedures are planned and implemented.
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6 Acquire and Preprocesspre-process stage
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6.1 General
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This stage specifies the technical procedures for acquiring, refining, and preparing the test data according to
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the requirements defined in the previous stage. It includes activities su
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