Provenance of specimen and data – A prerequisite for AI
development in computational pathology

J 2023

Provenance of specimen and data – A prerequisite for AI development in computational pathology

PLASS, Markus, Rudolf WITTNER, Petr HOLUB, Francesca FREXIA, Cecilia MASCIA et. al.

Basic information

Original name

Provenance of specimen and data – A prerequisite for AI development in computational pathology

Authors

PLASS, Markus, Rudolf WITTNER (703 Slovakia, belonging to the institution), Petr HOLUB (203 Czech Republic, guarantor, belonging to the institution), Francesca FREXIA, Cecilia MASCIA, Matej GALLO (703 Slovakia, belonging to the institution), Heimo MÜLLER and Jörg GEIGER

Edition

NEW BIOTECHNOLOGY, NETHERLANDS, ELSEVIER, 2023, 1871-6784

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

Germany

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

URL

Impact factor

Impact factor: 5.400 in 2022

RIV identification code

RIV/00216224:14610/23:00131767

Organization unit

Institute of Computer Science

DOI

http://dx.doi.org/10.1016/j.nbt.2023.09.006

UT WoS

001084774500001

Keywords in English

Artificial intelligence; Provenance; Biological material; Traceability

Abstract

V originále

AI development in biotechnology relies on high-quality data to train and validate algorithms. The FAIR principles (Findable, Accessible, Interoperable, and Reusable) and regulatory frameworks such as the In Vitro Diagnostic Regulation (IVDR) and the Medical Device Regulation (MDR) specify requirements on specimen and data provenance to ensure the quality and traceability of data used in AI development. In this paper, a framework is presented for recording and publishing provenance information to meet these requirements. The framework is based on the use of standardized models and protocols, such as the W3C PROV model and the ISO 23494 series, to capture and record provenance information at various stages of the data generation and analysis process. The framework and use case illustrate the role of provenance information in supporting the development of high-quality AI algorithms in biotechnology. Finally, the principles of the framework are illustrated in a simple computational pathology use case, showing how specimen and data provenance can be used in the development and documentation of an AI algorithm. The use case demonstrates the importance of managing and integrating distributed provenance information and highlights the complex task of considering factors such as semantic interoperability, confidentiality, and the verification of authenticity and integrity.

Links

MUNI/A/1339/2022, interní kód MU

Name: Rozvoj technik pro zpracování dat pro podporu vyhledávání, analýz a vizualizací rozsáhlých datových souborů s využitím umělé inteligence

Investor: Masaryk University, Development of data processing techniques to support search, analysis and visualization of large datasets using artificial intelligence

Citovat

PLASS, Markus, Rudolf WITTNER, Petr HOLUB, Francesca FREXIA, Cecilia MASCIA, Matej GALLO, Heimo MÜLLER and Jörg GEIGER. Provenance of specimen and data – A prerequisite for AI development in computational pathology. NEW BIOTECHNOLOGY. NETHERLANDS: ELSEVIER, 2023, vol. 78, DEC, p. 22-28. ISSN 1871-6784. Available from: https://dx.doi.org/10.1016/j.nbt.2023.09.006.

@article{2317118,
   author = {Plass, Markus and Wittner, Rudolf and Holub, Petr and Frexia, Francesca and Mascia, Cecilia and Gallo, Matej and Müller, Heimo and Geiger, Jörg},
   article_location = {NETHERLANDS},
   article_number = {DEC},
   doi = {http://dx.doi.org/10.1016/j.nbt.2023.09.006},
   keywords = {Artificial intelligence; Provenance; Biological material; Traceability},
   language = {eng},
   issn = {1871-6784},
   journal = {NEW BIOTECHNOLOGY},
   title = {Provenance of specimen and data – A prerequisite for AI development in computational pathology},
   url = {https://www.sciencedirect.com/science/article/pii/S1871678423000493},
   volume = {78},
   year = {2023}
}

TY  - JOUR
ID  - 2317118
AU  - Plass, Markus - Wittner, Rudolf - Holub, Petr - Frexia, Francesca - Mascia, Cecilia - Gallo, Matej - Müller, Heimo - Geiger, Jörg
PY  - 2023
TI  - Provenance of specimen and data – A prerequisite for AI development in computational pathology
JF  - NEW BIOTECHNOLOGY
VL  - 78
IS  - DEC
SP  - 22-28
EP  - 22-28
PB  - ELSEVIER
SN  - 18716784
KW  - Artificial intelligence
KW  - Provenance
KW  - Biological material
KW  - Traceability
UR  - https://www.sciencedirect.com/science/article/pii/S1871678423000493
N2  - AI development in biotechnology relies on high-quality data to train and validate algorithms. The FAIR principles (Findable, Accessible, Interoperable, and Reusable) and regulatory frameworks such as the In Vitro Diagnostic Regulation (IVDR) and the Medical Device Regulation (MDR) specify requirements on specimen and data provenance to ensure the quality and traceability of data used in AI development. In this paper, a framework is presented for recording and publishing provenance information to meet these requirements. The framework is based on the use of standardized models and protocols, such as the W3C PROV model and the ISO 23494 series, to capture and record provenance information at various stages of the data generation and analysis process. The framework and use case illustrate the role of provenance information in supporting the development of high-quality AI algorithms in biotechnology. Finally, the principles of the framework are illustrated in a simple computational pathology use case, showing how specimen and data provenance can be used in the development and documentation of an AI algorithm. The use case demonstrates the importance of managing and integrating distributed provenance information and highlights the complex task of considering factors such as semantic interoperability, confidentiality, and the verification of authenticity and integrity.
ER  -

PLASS, Markus, Rudolf WITTNER, Petr HOLUB, Francesca FREXIA, Cecilia MASCIA, Matej GALLO, Heimo MÜLLER and Jörg GEIGER. Provenance of specimen and data – A prerequisite for AI development in computational pathology. \textit{NEW BIOTECHNOLOGY}. NETHERLANDS: ELSEVIER, 2023, vol.~78, DEC, p.~22-28. ISSN~1871-6784. Available from: https://dx.doi.org/10.1016/j.nbt.2023.09.006.

Detailed Information on Publication Record