Galileo-an Artificial Intelligence tool for evaluating pre-implantation kidney biopsies.

Autor: Eccher A; Department of Medical and Surgical Sciences for Children and Adults, University of Modena and Reggio Emilia, University Hospital of Modena, Modena, Italy., L'Imperio V; Department of Medicine and Surgery, Pathology, IRCCS Fondazione San Gerardo dei Tintori, University of Milano-Bicocca, Monza, Italy. vincenzo.limperio@unimib.it., Pantanowitz L; Department of Pathology, University of Pittsburgh, Pittsburgh, PA, USA., Cazzaniga G; Department of Medicine and Surgery, Pathology, IRCCS Fondazione San Gerardo dei Tintori, University of Milano-Bicocca, Monza, Italy., Del Carro F; Department of Medicine and Surgery, Pathology, IRCCS Fondazione San Gerardo dei Tintori, University of Milano-Bicocca, Monza, Italy., Marletta S; Division of Pathology Humanitas Cancer Center, Catania, Italy., Gambaro G; Division of Nephrology, Department of Medicine, University of Verona, Verona, Italy., Barreca A; Pathology Unit, Città della Salute e della Scienza di Torino University Hospital, Turin, Italy., Becker JU; Institute of Pathology, University Hospital of Cologne, Cologne, Germany., Gobbo S; Department of Translational Medicine, University of Ferrara, Ferrara, Italy., Della Mea V; Department of Mathematics, Computer Science and Physics, University of Udine, Udine, Italy., Alberici F; Division of Nephrology and Dialysis, Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia and ASST-Spedali Civili of Brescia, Brescia, Italy., Pagni F; Department of Medicine and Surgery, Pathology, IRCCS Fondazione San Gerardo dei Tintori, University of Milano-Bicocca, Monza, Italy., Dei Tos AP; Surgical Pathology and Cytopathology Unit, Department of Medicine-DIMED, University of Padua School of Medicine, Padua, Italy.
Jazyk: angličtina
Zdroj: Journal of nephrology [J Nephrol] 2024 Oct 02. Date of Electronic Publication: 2024 Oct 02.
DOI: 10.1007/s40620-024-02094-4
Abstrakt: Background: Pre-transplant procurement biopsy interpretation is challenging, also because of the low number of renal pathology experts. Artificial intelligence (AI) can assist by aiding pathologists with kidney donor biopsy assessment. Herein we present the "Galileo" AI tool, designed specifically to assist the on-call pathologist with interpreting pre-implantation kidney biopsies.
Methods: A multicenter cohort of whole slide images acquired from core-needle and wedge biopsies of the kidney was collected. A deep learning algorithm was trained to detect the main findings evaluated in the pre-implantation setting (normal glomeruli, globally sclerosed glomeruli, ischemic glomeruli, arterioles and arteries). The model obtained on the Aiforia Create platform was validated on an external dataset by three independent pathologists to evaluate the performance of the algorithm.
Results: Galileo demonstrated a precision, sensitivity, F1 score and total area error of 81.96%, 94.39%, 87.74%, 2.81% and 74.05%, 71.03%, 72.5%, 2% in the training and validation sets, respectively. Galileo was significantly faster than pathologists, requiring 2 min overall in the validation phase (vs 25, 22 and 31 min by 3 separate human readers, p < 0.001). Galileo-assisted detection of renal structures and quantitative information was directly integrated in the final report.
Conclusions: The Galileo AI-assisted tool shows promise in speeding up pre-implantation kidney biopsy interpretation, as well as in reducing inter-observer variability. This tool may represent a starting point for further improvements based on hard endpoints such as graft survival.
(© 2024. The Author(s).)
Databáze: MEDLINE
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