Deep learning applications for kidney histology analysis

Current Opinion in Nephrology & Hypertension, vol. 33, pp. 291–297

Abstract

PURPOSE OF REVIEW: Nephropathology is increasingly incorporating computational methods to enhance research and diagnostic accuracy. The widespread adoption of digital pathology, coupled with advancements in deep learning, will likely transform our pathology practices. Here, we discuss basic concepts of deep learning, recent applications in nephropathology, current challenges in implementation and future perspectives. RECENT FINDINGS: Deep learning models have been developed and tested in various areas of nephropathology, for example, predicting kidney disease progression or diagnosing diseases based on imaging and clinical data. Despite their promising potential, challenges remain that hinder a wider adoption, for example, the lack of prospective evidence and testing in real-world scenarios. SUMMARY: Deep learning offers great opportunities to improve quantitative and qualitative kidney histology analysis for research and clinical nephropathology diagnostics. Although exciting approaches already exist, the potential of deep learning in nephropathology is only at its beginning and we can expect much more to come.

Authors 3

1. Pourya Pilva corresponding
   Affiliation as printed

   Institute of Pathology

2. Roman David Bülow corresponding
   Affiliation as printed

   Institute of Pathology

3. Peter Boor Aachen Department of Nephrology and Clinical Immunology

   RWTH Aachen University · Universitätsklinikum Aachen

   Affiliation as printed

   Department of Nephrology and Clinical Immunology, RWTH Aachen University Hospital, Aachen, Germany

   Institute of Pathology