Banff Digital Pathology Working Group: Going Digital in Transplant Pathology
Open accessAbstract
The Banff Digital Pathology Working Group (DPWG) was formed in the time leading up to and during the joint American Society for Histocompatibility and Immunogenetics/Banff Meeting, September 23-27, 2019, held in Pittsburgh, Pennsylvania. At the meeting, the 14th Banff Conference, presentations directly and peripherally related to the topic of "digital pathology" were presented; and discussions before, during, and after the meeting have resulted in a list of issues to address for the DPWG. Included are practice standardization, integrative approaches for study classification, scoring of histologic parameters (eg, interstitial fibrosis and tubular atrophy and inflammation), algorithm classification, and precision diagnosis (eg, molecular pathways and therapeutics). Since the meeting, a survey with international participation of mostly pathologists (81%) was conducted, showing that whole slide imaging is available at the majority of centers (71%) but that artificial intelligence (AI)/machine learning was only used in ≈12% of centers, with a wide variety of programs/algorithms employed. Digitalization is not just an end in itself. It also is a necessary precondition for AI and other approaches. Discussions at the meeting and the survey highlight the unmet need for a Banff DPWG and point the way toward future contributions that can be made.
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Commercial digital pathology slide scanners for high-throughput imaging have only recently become available.
“Commercial digital pathology slide scanners for high-throughput imaging have only recently become available (44, 45).”
From Artificial Intelligence: Present and Future Potential for Solid Organ Transplantation (2022) · cites this paper as [45]
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Artificial Intelligence: Present and Future Potential for Solid Organ Transplantation
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- Authors
- Farris, A. B., Moghe, I., Wu, S., Hogan, J., Cornell, L. D., Alexander, M. P., Kers, J., Demetris, A. J., Levenson, R. M., Tomaszewski, J., Barisoni, L., Yagi, Y., & Solez, K.
- Affiliations
- Emory University (US)
- University of Alberta (CA)
- Mayo Clinic (US)
- Leiden University Medical Center (NL)
- University of Pittsburgh (US)
- University of California Davis Medical Center (US)
- University at Buffalo, State University of New York (US)
- Duke University (US)
- Memorial Sloan Kettering Cancer Center (US)
- Corresponding author
- Farris, Alton B.
- ORCID
- Venue
- American Journal of Transplantation
- Publisher
- Elsevier BV
- Published
- 2020-09
- Volume
- 20
- Issue
- 9
- Pages
- 2392-2399
- ISSN
- 1600-6135 (ISSN-L)
- PMC
- PMC7496838
- OpenAlex
- W3011545441
- Cited by
- 48
- References
- 42
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 6 · 2023: 16 · 2022: 10 · 2021: 6 · 2020: 5
- FWCI
- 3.64
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- American Journal of Transplantation (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Pure Amsterdam UMC (acceptedVersion, cc-by)
- Leiden Repository (Leiden University) (submittedVersion, cc-by-nc)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Data Archiving and Networked Services (DANS) (submittedVersion, cc-by-nc)
- eScholarship (California Digital Library) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by-nc)
- Funders
- National Cancer Institute (R33CA202881, P30CA008748)
- National Institute of Biomedical Imaging and Bioengineering (R01EB028635)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- digital pathology, kidney transplantation, whole slide imaging, artificial intelligence in pathology, machine learning, Banff classification, interstitial fibrosis and tubular atrophy, precision diagnostics, molecular pathways, standardization of practice
- MeSH terms
- Artificial Intelligence, Biopsy, Graft Rejection, Humans, Kidney Diseases, Pennsylvania, Kidney Transplantation
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