Volumetric laser endomicroscopy (VLE) is an advanced imaging modality used to detect Barrett’s esophagus (BE) dysplasia. However, real-time interpretation of VLE scans is complex and time-consuming. Computer-aided detection (CAD) may aid in the process of VLE image interpretation. Our aim was to train and validate a CAD algorithm for VLE-based detection of BE neoplasia.
The multicenter, VLE PREDICT study, prospectively enrolled 47 BE patients. In total, 229 nondysplastic BE, and 89 neoplastic (HGD/EAC) targets were laser marked under VLE guidance and subsequently biopsied for histological diagnosis. Deep convolutional neural networks were used to construct a CAD algorithm for differentiation between nondysplastic and neoplastic BE tissue. The CAD algorithm was trained on a set consisting of the first 22 patients (134 NDBE and 38 neoplastic targets) and validated on a separate test set of patients 23 to 47 (95 NDBE and 51 neoplastic targets). Finally, algorithm performance was benchmarked against the performance of 10 VLE experts.
Using the Training set to construct the algorithm resulted in an accuracy of 92%, sensitivity of 95% and specificity of 92%. When performance was assessed on the Test set, accuracy, sensitivity, and specificity were 85%, 91%, and 82%, respectively. The algorithm outperformed all 10 VLE experts, who demonstrated an overall accuracy of 77%, sensitivity of 70%, and specificity of 81%.
We developed, validated, and benchmarked a VLE CAD algorithm for detection of BE neoplasia using prospectively collected and biopsy-correlated VLE targets. The algorithm detected neoplasia with high accuracy and outperformed 10 VLE experts.