Pioneering AI-guided fluorescence-like navigation in urological surgery: real-time ureter segmentation during robot-assisted radical cystectomy using convolutional neural network.

Artificial intelligence (AI)-driven intraoperative navigation in urological surgery can enhance surgical precision through real-time structure identification and tracking. This study describes a novel AI solution that enables real-time fluorescence-like navigation (FLN) for robot-assisted radical cystectomy (RARC) with an initial focus on ureter, potentially enhancing outcomes and training efficacy. We established a new AI model using convolutional neural network (CNN) to achieve real-time intraoperative ureter recognition using 730 images from 17 RARC cases. Quantitative and qualitative analyses were performed for each procedure (phases I-V: identifying, exposing, elevating, retracting, distal separation). For quantitative evaluation, we calculated precision, recall, intersection over union (IoU), and Dice coefficients by comparing AI-inferred images with surgeons' annotations on 41 test images. In addition, 18 surgeons participated in a qualitative assessment, answering questions on identification, misidentification, and clinical utility. The CNN-based prediction model has been successfully established and validated. The AI model achieved a Dice score of 0.71 (phases I-V, respectively: 0.75/0.69/0.71/0.69/0.72), IoU of 0.55 (0.60/0.53/0.55/0.52/0.57), recall of 0.90 (0.91/0.92/0.92/0.89/0.89), precision of 0.60 (0.67/0.55/0.58/0.61/0.64), and accuracy of 0.99 (1.00/0.99/0.99/0.99/0.99). For identification performance, the AI system scored an average of 4.74 on a scale of 0 to 5 (4.94/4.61/4.94/4.89/4.33), indicating that most images achieved > 80% recognition accuracy. The average score for misidentification of other tissues as ureter was low at 0.60 on a scale of 0 to 5 (phases I-V: 0.11/0.56/0.39/0.44/1.50). In the clinical utility assessment, 62.22% (60.00%-64.44%) of the AI-inferred images were correctly distinguished from the ground truth. Our AI model reliably annotated the ureter in real-time during RARC, achieving high accuracy and acceptable precision. This technology has the potential to reduce the risk of ureter misrecognition by surgeons, thereby enhancing surgical accuracy and safety.