ArticleCureus2025
Enhancing Organ Allocation Efficiency: A Pilot Study Evaluating Artificial Intelligence-Assisted Assessment of Donor Kidney Pathology.
Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
3 authors.
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Abstract
purposeThe purpose of this study is to evaluate the effectiveness of an artificial intelligence (AI)-assisted review (AAR) system in improving diagnostic accuracy, efficiency, and concordance with expert assessments during the evaluation of donor kidney viability.
methodsSixty H&E-stained frozen-section kidney biopsy slides from explant kidneys obtained for organ donation were evaluated. A board-certified renal pathologist established ground truth (GT) through manual digital evaluation on the Techcyte Fusion Platform. The slides were independently reviewed by an AI algorithm, a board-certified pathologist (Reviewer 2 (R2)), and a board-certified transplant surgeon (Reviewer 1 (R1)). After a washout period, AI-assisted reads were performed. The performance of AAR and manual digital review (MDR) was compared to the GT for total and sclerotic glomeruli (SG) counts, as well as concordance with kidney viability thresholds (using a 20% SG cutoff rate). Secondary outcomes included comparisons of review times and concordance rates for AAR, MDR, and AI analysis alone with the GT.
resultsAAR demonstrated concordance with GT across parameters. For R1, coefficient of determination (COD) values for SG counts improved with AAR (0.833) compared to MDR (0.81). Agreement at the 20% SG threshold for kidney viability was 98.33% for both AAR and MDR. AAR reduced mean review times (minutes) by 54.83% compared to MDR, with per-slide review times decreasing from 17:09 (MDR) to 8:35 (AAR). Pearson correlation coefficients (PCC) and concordance correlation coefficients (CCC) for AAR were generally higher than MDR, particularly for the percentage of SG, indicating improved alignment with GT. Analyses revealed no systematic bias, with AAR aligning more closely with GT compared to MDR for both reviewers.
conclusionThe Techcyte algorithm reduces review time while maintaining accuracy and concordance with experts, promoting AI adoption to improve workflow efficiency and expedite transplantation decisions.
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