Closing the data gap: leveraging pretrained neural networks for robotic surgical assessment on limited clinical data

Background: In robot-assisted surgery (RAS), surgical assessment is critical for ensuring competence and achieving optimal surgical outcomes. Artificial intelligence (AI)-based assessment offers an alternative to expert-based assessment but often requires large datasets, which are challenging to obtain. Transfer learning with pretrained algorithms may offer a potential solution and could reduce the need for clinical data. This study explores the use of transfer learning with preclinical porcine data to reduce the clinical data needed for action recognition (AC) and skills assessment (SA) in RAS. Methods: Abdominal, thoracic and urologic RAS procedures were video recorded. A convolutional neural network (CNN) with a Long Short-Term Memory (LSTM) layer, initially trained using preclinical data, was applied to the clinical dataset through three strategies; (1) direct application on the clinical dataset, (2) only training the LSTM and dense layers, and (3) retraining the entire network. For comparison, a baseline model was trained from scratch on clinical data. Results: Recordings from 15 procedures were included. The baseline clinical model achieved accuracies of 82.7% (AC) and 40.8% (SA). Direct application of the pretrained network resulted in accuracies of 84.8% (AC) and 51.6% (SA). Fine-tuning the LSTM and dense layers of the pretrained network yielded accuracies of 90.1% (AC) and 60.4 (SA), while retraining all layers achieved 90.5% (AC) and 57.6% (SA). Ablation analysis demonstrated higher accuracies with less data using transfer learning, 87.9% vs. 81.6%. Conclusions: Using pretrained preclinical AI models increases the accuracy of models trained on limited clinical data and reduces the need for clinical data. Public trial registry: www.clinicaltrials.gov (ID: NCT06612606).