Romanchikova, M; Thomas, S A; Dexter, A; Shaw, M; Partarrieu, I; Smith, N; Venton, J; Adeogun, M; Brettle, D; Turpin, R J (2022) Need for measurement science in digital pathology. Journal of Pathology Informatics, 13. 100157

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Abstract

Background: Pathology services experienced a surge in demand during the COVID-19 pandemic. Digitalisation of pathology workflows can help to increase throughput, yet many existing digitalisation solutions use non-standardised workflows captured in proprietary data formats and processed by black-box software, yielding data of varying quality. This study presents the views of a UK-led expert group on the barriers to adoption and the required input of measurement science to improve current practices in digital pathology.

Methods: With an aim to support the UK’s efforts in digitalisation of pathology services, this study comprised 1) a review of existing evidence, 2) an online survey of domain experts and 3) a workshop with 42 representatives from healthcare, regulatory bodies, pharmaceutical industry, academia, equipment and software manufacturers. The discussion topics included sample processing, data interoperability, image analysis, equipment calibration, and use of novel imaging modalities.

Findings: The lack of data interoperability within the digital pathology workflows hinders data lookup and navigation, according to 80 % of attendees. All participants stressed the importance of integrating imaging and non-imaging data for diagnosis, while 80 % saw data integration as a priority challenge. 90 % identified the benefits of artificial intelligence and machine learning, but identified the need for training and sound performance metrics.
Methods for calibration and providing traceability were seen as essential to establish harmonised, reproducible sample processing and image acquisition pipelines. Vendor-neutral data standards were seen as a “must-have” for providing meaningful data for downstream analysis. Users and vendors need good practice guidance on evaluation of uncertainty, fitness-for-purpose, and reproducibility of artificial intelligence/machine learning tools. All of the above needs to be accompanied by an upskilling of the pathology workforce.

Interpretation: Measurement science expertise is required to improve reliability of digital pathology workflows and provide confidence in diagnosis.

Item Type:	Article
Keywords:	Digital pathology; DICOM; FAIR principles; Metrology; Calibration Whole slide imaging; Artificial intelligence; Machine learning; Metadata; Standards
Subjects:	Biotechnology > Bio-Diagnostics
Divisions:	Data Science
Identification number/DOI:	10.1016/j.jpi.2022.100157
Last Modified:	09 Jun 2023 13:17
URI:	https://eprintspublications.npl.co.uk/id/eprint/9752