OpenLB
Open Library of Bioscience

Deep learning applications in digital pathology.

Abstract

No abstract text available.

References

1. H��lscher, D. L. et al. Next-generation morphometry for pathomics-data mining in histopathology. Nat. Commun. 14, 470 (2023). [DOI: 10.1038/s41467-023-36173-0]
2. Zhu, Z. et al. Finerenone added to RAS/SGLT2 blockade for CKD in Alport syndrome. Results of a randomized controlled trial with Col4a3 mice. J. Am. Soc. Nephrol. 34, 1513���1520 (2023). [DOI: 10.1681/ASN.0000000000000186]
3. Hanna, M. G. et al. Implementation of digital pathology offers clinical and operational increase in efficiency and cost savings. Arch. Pathol. Lab. Med. 143, 1545���1555 (2019). [DOI: 10.5858/arpa.2018-0514-OA]
4. Romberg, D. et al. EMPAIA app interface: an open and vendor-neutral interface for AI applications in pathology. Comput. Methods Programs Biomed. 215, 106596 (2022). [DOI: 10.1016/j.cmpb.2021.106596]
5. Yu, F. et al. Heterogeneity and predictors of the effects of AI assistance on radiologists. Nat. Med. 30, 837���849 (2024). [DOI: 10.1038/s41591-024-02850-w]
6. Lennerz, J. K. et al. Diagnostic quality model (DQM): an integrated framework for the assessment of diagnostic quality when using AI/ML. Clin. Chem. Lab. Med. 61, 544���557 (2023). [DOI: 10.1515/cclm-2022-1151]
7. Kers, J. et al. Deep learning-based classification of kidney transplant pathology: a retrospective, multicentre, proof-of-concept study. Lancet Digit. Health 4, e18���e26 (2022). [DOI: 10.1016/S2589-7500(21)00211-9]
8. Yi, Z. et al. A large-scale retrospective study enabled deep-learning based pathological assessment of frozen procurement kidney biopsies to predict graft loss and guide organ utilization. Kidney Int. 105, 281���292 (2024). [DOI: 10.1016/j.kint.2023.09.031]
9. Smerkous, D. et al. Development of an automated estimation of foot process width using deep learning in kidney biopsies from patients with Fabry, minimal change, and diabetic kidney diseases. Kidney Int. 105, 165���176 (2024). [DOI: 10.1016/j.kint.2023.09.011]
10. Vafaei Sadr, A. et al. Operational greenhouse-gas emissions of deep learning in digital pathology: a modelling study. Lancet Digit. Health 6, e58���e69 (2024). [DOI: 10.1016/S2589-7500(23)00219-4]