The need for measurement science in digital pathology.

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. Conclusions: 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.

Building confidence in digital health through metrology.

Healthcare is increasingly and routinely generating large volumes of data from different sources, which are difficult to handle and integrate. Confidence in data can be established through the knowledge that the data are validated, well-curated and with minimal bias or errors. As the National Measurement Institute of the UK, the National Physical Laboratory (NPL) is running an interdisciplinary project on digital health data curation. The project addresses one of the key challenges of the UK's Measurement Strategy, to provide confidence in the intelligent and effective use of data. A workshop was organised by NPL in which important stakeholders from NHS, industry and academia outlined the current and future challenges in healthcare data curation. This paper summarises the findings of the workshop and outlines NPL's views on how a metrological approach to the curation of healthcare data sets could help solve some of the important and emerging challenges of utilising healthcare data.

Enabling the Advanced Bioeconomy through Public Policy Supporting Biofoundries and Engineering Biology.

The bioeconomy concept is proliferating globally. However, the enabling roles of biotechnology may be getting sidelined in the strategies of some countries. A goal for engineering biology is alignment with the engineering design cycle to enable more rapid commercialization. This paper considers several policy options to remove critical technical barriers to commercialization.