期刊
CURRENT OPINION IN NEPHROLOGY AND HYPERTENSION
卷 26, 期 6, 页码 450-459出版社
LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.1097/MNH.0000000000000360
关键词
computational disease; convolutional neural network; deep learning; focal segmental glomerulosclerosis; morphometry; nephrotic syndrome; podocytes; structural feature extraction
资金
- Nephrotic Syndrome Study Network Consortium (NEP-TUNE), part of the National Center for Advancing Translational Sciences (NCATS)
- Rare Disease Clinical Research Network (RDCRN)
- Office of Rare Diseases Research (ORDR)
- NCATS
- National Institute of Diabetes, Digestive, and Kidney Diseases
- University of Michigan
- NephCure Kidney International
- Halperin Foundation
Purpose of review In this review, we will discuss (i) how the recent advancements in digital technology and computational engineering are currently applied to nephropathology in the setting of clinical research, trials, and practice; (ii) the benefits of the new digital environment; (iii) how recognizing its challenges provides opportunities for transformation; and (iv) nephropathology in the upcoming era of kidney precision and predictive medicine. Recent findings Recent studies highlighted how new standardized protocols facilitate the harmonization of digital pathology database infrastructure and morphologic, morphometric, and computer-aided quantitative analyses. Digital pathology enables robust protocols for clinical trials and research, with the potential to identify previously underused or unrecognized clinically useful parameters. The integration of digital pathology with molecular signatures is leading the way to establishing clinically relevant morpho-omic taxonomies of renal diseases. Summary The introduction of digital pathology in clinical research and trials, and the progressive implementation of the modern software ecosystem, opens opportunities for the development of new predictive diagnostic paradigms and computer-aided algorithms, transforming the practice of renal disease into a modern computational science.
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