The Future of Surgical Diagnostics: AI-Enhanced Detection of Ganglion Cells for Hirschsprung Disease.

Abstract

Hirschsprung disease (HD), a congenital disease characterized by the absence of ganglion cells, presents significant surgical challenges. Addressing a critical gap in intraoperative diagnostics, we introduce transformative artificial-intelligence (AI) approach that significantly enhances the detection of ganglion cells in frozen-sections (FSs). The dataset comprises 366 frozen and 302 formalin-fixed-paraffin-embedded (FFPE) hematoxylin and eosin-stained slides obtained from 164 patients from three centers. Three pathologists annotated the ganglion cells on the whole-slide-images (WSIs) using bounding boxes. Tissue regions within WSIs were segmented and split into patches of 2000x2000 pixels. A deep-learning pipeline utilizing ResNet-50 model for feature extraction and Grad-CAM algorithm to generate heatmaps for ganglion cell localization was employed. The binary classification performance of the model was evaluated on independent test cohorts. In the multi-reader study, 10-pathologists assessed 50-frozen WSIs, with 25-slides containing ganglion cells, and 25-slides without. In the first phase of the study, pathologists evaluated the slides as in their routine practice. After two-week washout period, pathologists reevaluated the same WSIs along with the four patches with the highest probability of containing ganglion cells. The proposed deep-learning approach achieved an accuracy of 91.3%, 92.8%, 90.1% in detecting ganglion cells within WSIs in the test dataset obtained from centers. In the reader study, on average, the pathologists' diagnostic accuracy increased from 77% to 85.8% with the model's heatmap support while the diagnosis time decreased from an average of 139.7 seconds to 70.5 seconds. Notably, when applied in real-world settings with group of pathologists, our model's integration brought about substantial improvement in diagnosis precision and reduced the time required for diagnoses by half. This notable advance in AI-driven diagnostics not only sets new-standard for surgical decision-making in HD but also creates opportunities for its wider implementation in various clinical settings, highlighting its pivotal role in enhancing the efficacy and accuracy of FS analyses.