A large-scale retrospective study enabled deep-learning based pathological assessment of frozen procurement kidney biopsies to predict graft loss and guide organ utilization
Yi Z, Xi C, Menon M, Cravedi P, Tedla F, Soto A, Sun Z, Liu K, Zhang J, Wei C, Chen M, Wang W, Veremis B, Garcia-Barros M, Kumar A, Haakinson D, Brody R, Azeloglu E, Gallon L, O'Connell P, Naesens M, Shapiro R, Colvin R, Ward S, Salem F, Zhang W. A large-scale retrospective study enabled deep-learning based pathological assessment of frozen procurement kidney biopsies to predict graft loss and guide organ utilization. Kidney International 2023, 105: 281-292. PMID: 37923131, PMCID: PMC10892475, DOI: 10.1016/j.kint.2023.09.031.Peer-Reviewed Original ResearchArterial intimal fibrosisGraft lossOrgan utilizationIntimal fibrosisSclerotic glomeruliLarge-scale retrospective studyGlomerular filtration rateDeceased donor kidneysPathologist scoresTransplant outcomesBiopsy findingsSurvival prediction modelWedge biopsyRisk stratificationRetrospective studyFiltration ratePathological assessmentDonor biopsiesNeedle biopsySimilar survivalHistological scoringUnnecessary organsDiscovery setKidneyLesion scores