Abstract
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.
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.
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.
Digital pathology requires interoperable data formats, reproducible and comparable laboratory workflows, and trustworthy computer analysis software. Despite high interest in the use of novel imaging techniques and artificial intelligence tools, their adoption is slowed down by the lack of guidance and evaluation tools to assess the suitability of these techniques for specific clinical question. Measurement science expertise in uncertainty estimation, standardisation, reference materials, and calibration can help establishing reproducibility and comparability between laboratory procedures, yielding high quality data and providing higher confidence in diagnosis.
•A change to vendor-neutral open standard for images and annotations is essential to improve data management, sharing and re-use.•Lack of standardised data hinders development of AI/ML tools for pathology.•Frequency and scope of instrument calibration vary a lot between laboratories.•Standardised calibration tools are needed to yield consistent comparable images.•Pathology community needs own metrics to assess AI/ML performance.