Renal failure · 2026
Artificial intelligence-powered renal pathology for glomerular disease classification: a systematic review and meta-analysis.
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Tong Min, Jiang Yingying, Zhu Rongxin, Ding Zichao, Ma Diya, Yin Jingdan, Zhang Yatong, Yuan Zhenhua, Ren Xianqing
Аннотация
BACKGROUND: Diagnostic performance varies across glomerular disease (GD) subtypes, and whether artificial intelligence (AI) outperforms pathologists with different experience levels remains uncertain.
OBJECTIVE: To evaluate pathology-based AI models for GD classification and compare their performance with pathologists.
METHODS: PubMed, Embase, Web of Science, and Cochrane Library were searched through 15 July 2026. Studies using pathology images and pathology diagnosis as the reference standard were included. Random-effects models pooled sensitivity, precision, accuracy, F1 score and area under the curve (AUC).
RESULTS: Fifteen studies comprising 39,536 validation sample units, not necessarily unique patients, were included. For subtypes with at least 10 validation datasets, AI achieved high performance for membranous nephropathy (MN; sensitivity 0.96, precision 0.94, accuracy 0.96, F1 score 0.95, AUC 0.98), IgA nephropathy (IgAN; sensitivity 0.92, precision 0.91, accuracy 0.94, F1 score 0.90, AUC 0.96), and minimal change disease (MCD; sensitivity 0.92, precision 0.87, accuracy 0.96, F1 score 0.89, AUC 1.00). AI also showed higher accuracy than senior pathologists for IgAN, MN, and MCD; however, comparator evidence was sparse and should be interpreted cautiously. Most included studies were retrospective, and substantial heterogeneity was observed across datasets, imaging modalities, model architectures, and validation strategies.
CONCLUSIONS: Pathology-based AI shows strong potential for GD classification, but current head-to-head evidence is insufficient to establish superiority over pathologists, particularly senior pathologists. Prospective multicenter studies integrating multimodal clinical data and standardized external validation are needed.
OBJECTIVE: To evaluate pathology-based AI models for GD classification and compare their performance with pathologists.
METHODS: PubMed, Embase, Web of Science, and Cochrane Library were searched through 15 July 2026. Studies using pathology images and pathology diagnosis as the reference standard were included. Random-effects models pooled sensitivity, precision, accuracy, F1 score and area under the curve (AUC).
RESULTS: Fifteen studies comprising 39,536 validation sample units, not necessarily unique patients, were included. For subtypes with at least 10 validation datasets, AI achieved high performance for membranous nephropathy (MN; sensitivity 0.96, precision 0.94, accuracy 0.96, F1 score 0.95, AUC 0.98), IgA nephropathy (IgAN; sensitivity 0.92, precision 0.91, accuracy 0.94, F1 score 0.90, AUC 0.96), and minimal change disease (MCD; sensitivity 0.92, precision 0.87, accuracy 0.96, F1 score 0.89, AUC 1.00). AI also showed higher accuracy than senior pathologists for IgAN, MN, and MCD; however, comparator evidence was sparse and should be interpreted cautiously. Most included studies were retrospective, and substantial heterogeneity was observed across datasets, imaging modalities, model architectures, and validation strategies.
CONCLUSIONS: Pathology-based AI shows strong potential for GD classification, but current head-to-head evidence is insufficient to establish superiority over pathologists, particularly senior pathologists. Prospective multicenter studies integrating multimodal clinical data and standardized external validation are needed.