Deep learning for the classification of medical kidney disease: a pilot study for electron microscopy. (4th March 2021)

• Record Type: Journal Article
• Authors: Hacking, Sean; Bijol, Vanesa
• Is Part Of: Ultrastructural pathology. Volume 45:Number 2(2021)
• Page Start: 118
• Page End: 127
• Publication Date: 2021-03-04
• Subjects: Deep Learning -- Medical Kidney Disease -- Electron Microscopy -- Cloud Computing -- 21st Century Medicine; Pathology, Cellular -- Periodicals; Ultrastructure (Biology) -- Periodicals; Diagnosis, Electron microscopic -- Periodicals; Microscopy, Electron; Pathology; 616.07582
• DOI: 10.1080/01913123.2021.1882628
• Languages: English
• ISSNs: 0191-3123

Abstract

ABSTRACT: Artificial intelligence (AI) is a new frontier and often enigmatic for medical professionals. Cloud computing could open up the field of computer vision to a wider medical audience and deep learning on the cloud allows one to design, develop, train and deploy applications with ease. In the field of histopathology, the implementation of various applications in AI has been successful for whole slide images rich in biological diversity. However, the analysis of other tissue medias, including electron microscopy, is yet to be explored. The present study aims to evaluate deep learning for the classification of medical kidney disease on electron microscopy images: amyloidosis, diabetic glomerulosclerosis, membranous nephropathy, membranoproliferative glomerulonephritis (MPGN), and thin basement membrane disease (TBMD). We found good overall classification with the MedKidneyEM-v1 Classifier and when looking at normal and diseased kidneys, the average area under the curve for precision and recall was 0.841. The average area under the curve for precision and recall on the disease only cohort was 0.909. Digital pathology will shape a new era for medical kidney disease and the present study demonstrates the feasibility of deep learning for electron microscopy. Future approaches could be used by renal pathologists to improve diagnostic concordance, determine therapeutic strategies, and optimize patient outcomes in a true clinical environment.