Search for: All records

ABSTRACT A three‐dimensional convolutional neural network (3D‐CNN) was developed for the analysis of volumetric optical coherence tomography (OCT) images to enhance endoscopic guidance during percutaneous nephrostomy. The model was performance‐benchmarked using a 10‐fold nested cross‐validation procedure and achieved an average test accuracy of 90.57% across a dataset of 10 porcine kidneys. This performance significantly exceeded that of 2D‐CNN models that attained average test accuracies ranging from 85.63% to 88.22% using 1, 10, or 100 radial sections extracted from the 3D OCT volumes. The 3D‐CNN (~12 million parameters) was benchmarked against three state‐of‐the‐art volumetric architectures: the 3D Vision Transformer (3D‐ViT, ~45 million parameters), 3D‐DenseNet121 (~12 million parameters), and the Multi‐plane and Multi‐slice Transformer (M3T, ~29 million parameters). While these models achieved comparable inferencing accuracy, the 3D‐CNN exhibited lower inference latency (33 ms) than 3D‐ViT (86 ms), 3D‐DenseNet121 (58 ms), and M3T (93 ms), representing a critical advantage for real‐time surgical guidance applications. These results demonstrate the 3D‐CNN's capability as a powerful and practical tool for computer‐aided diagnosis in OCT‐guided surgical interventions.