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Banff Digital Pathology Working Group: Image Bank, Artificial Intelligence Algorithm, and Challenge Trial Developments
Emory Univ, GA 30322 USA.
Mayo Clin, MN USA.
Univ Michigan, MI USA.
Duke Univ, NC USA.
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2023 (English)In: Transplant International, ISSN 0934-0874, E-ISSN 1432-2277, Vol. 36, article id 11783Article in journal (Refereed) Published
Abstract [en]

The Banff Digital Pathology Working Group (DPWG) was established with the goal to establish a digital pathology repository; develop, validate, and share models for image analysis; and foster collaborations using regular videoconferencing. During the calls, a variety of artificial intelligence (AI)-based support systems for transplantation pathology were presented. Potential collaborations in a competition/trial on AI applied to kidney transplant specimens, including the DIAGGRAFT challenge (staining of biopsies at multiple institutions, pathologists visual assessment, and development and validation of new and pre-existing Banff scoring algorithms), were also discussed. To determine the next steps, a survey was conducted, primarily focusing on the feasibility of establishing a digital pathology repository and identifying potential hosts. Sixteen of the 35 respondents (46%) had access to a server hosting a digital pathology repository, with 2 respondents that could serve as a potential host at no cost to the DPWG. The 16 digital pathology repositories collected specimens from various organs, with the largest constituent being kidney (n = 12,870 specimens). A DPWG pilot digital pathology repository was established, and there are plans for a competition/trial with the DIAGGRAFT project. Utilizing existing resources and previously established models, the Banff DPWG is establishing new resources for the Banff community.

Place, publisher, year, edition, pages
FRONTIERS MEDIA SA , 2023. Vol. 36, article id 11783
Keywords [en]
Banff; digital pathology; artificial intelligence; machine learning; image analysis
National Category
Surgery
Identifiers
URN: urn:nbn:se:liu:diva-199572DOI: 10.3389/ti.2023.11783ISI: 001103156700001PubMedID: 37908675OAI: oai:DiVA.org:liu-199572DiVA, id: diva2:1820606
Note

Funding Agencies|PB is supported by the German Research Foundation (DFG, Project IDs 322900939 amp; 445703531), European Research Council (ERC Consolidator Grant No 101001791), the Federal Ministries of Education and Research [BMBF, STOP-FSGS-01GM2202C; Economic Affairs a [322900939, 445703531]; German Research Foundation (DFG) [101001791]; European Research Council [STOP-FSGS-01GM2202C]; Federal Ministries of Education and Research [BMBF] [01MK2002A, ZMVI1-2520DAT111]; Economic Affairs and Climate Action [01VSF21048]; Innovation Fund of the Federal Joint Committee [U01CA242936]; National Institutes of Health [945358]; Innovative Medicines Initiative 2 Joint Undertaking; European Union; EFPIA [17OKG23, 21OK+012]; Dutch Kidney Foundation; Human(e) AI research priority area of the University of Amsterdam [S003422N, G087620N]; Research Foundation Flanders (FWO) [C32/17/049, 1844019N]; KU Leuven C3 internal grant

Available from: 2023-12-18 Created: 2023-12-18 Last updated: 2024-09-13

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