#3070 Machine learning glomerulonephritis diagnosis on single glomeruli

Abstract

Abstract Background and Aims Diagnostic applications of machine learning in nephropathology are only beginning to emerge. We hypothesized that we could develop a machine learning classifier for 12 different classes of glomerulonephritis with CNN and self-attention-based architectures, following the nephropathology paradigm that globally sclerosed glomeruli are not useful for diagnostic purposes. Method The dataset contains 11,000 PAS-stained glomerular crops from 350 biopsies (four institutions). Each crop retained the diagnosis label from the 12 classes ABMGN, ANCA, C3-GN, CryoGN, DDD, Fibrillary, infection-associated GN (IAGN), IgAGN, MPGN, Membranous, PGNMID, SLEGN-IV; globally sclerotic glomerular crops were stripped from this diagnostic label and were just labeled as the 13th class Sclerotic. This dataset was divided into 75% of samples for training, 15% for validation and 10% testing, avoiding information leak within biopsies. Moreover, a hold-out validation set of another 50 biopsies with 2,000 new crops that were taken from another three centres. A classifier was trained in a fully supervised fashion for 13 classes (12 GN classes and Sclerotic), based on an ensemble of multiple transformer-based classification networks, including Swin-Transformer and ConvNext. This allows classification of each glomerular crop by different network instances. Since each network was trained under different conditions, the whole system acquired a more global knowledge understanding rather than a relying on a single method. For the final decision, the system takes the prediction with the largest confidence threshold, which is also predicted along with the class. Results The Table and the Figure list the metrics for classification performance calculated as Precision, Sensitivity, Specificity, F1 Score and balanced Accuracy. Balanced accuracy was between 0.4797 for CryoGN and 0.5949 for Membranous, it was 0.6892 for Sclerotic. AUCs for ROCs were between 0.40 for PGNMID and 0.82 for Membranous, with 0.81 for Sclerotic. Conclusion This proof-of-concept study establishes a baseline for this challenging classification task, which usually requires immunostains, electron microscopy and even clinical data. Our classification results even on single PAS glomerular crops appear promising. Combined with our automatic glomerular segmentation models, we could rapidly expand the training cohorts sizes and even add more classes of GN.