Hirschsprung disease (HD) is a congenital disorder characterized by the absence of ganglion cells in the colonic nervous plexuses, resulting in bowel obstruction and various complications. The diagnosis of HD demands expertise, experience, and ancillary tests. Our objective was to design an artificial intelligence (AI) tool to facilitate the diagnostic process of HD and evaluate its performance in terms of sensitivity, specificity, and average area under the curve (AUC). Using a camera-equipped microscope, we digitized 222 high-power fields of view obtained from H&E-stained slides. Nuclei were segmented using a deep learning algorithm and manually annotated by an expert pathologist. From these, 2076 nuclei were selected, including 346 ganglion cells and 1730 non-ganglion cells. A set of 100 features related to shape, color, and texture was extracted from the nuclei. To evaluate their utility in distinguishing the two categories, a cross-validation scheme was employed. The nuclei were randomly divided into training (70%) and validation sets (30%). The Wilcoxon signed-rank test was employed to identify the top features in the training set, which were then used to train an AI classifier to distinguish between the two categories: Ganglion cell and non-ganglion cell. The classifier's performance was assessed using the validation set, yielding an average AUC = 0.98. The classifier's performance was assessed using the validation set, yielding an average AUC = 0.98. Taken together, our findings indicate that this AI-driven framework may serve as a valuable support tool for pathologists, facilitating the diagnosis of HD in routine clinical practice.
Siabatto-Cleves et al. (Tue,) studied this question.