Learning where to look: scaling parkland grade prediction from surgical videos.

PURPOSE: The Parkland Grading Scale (PGS) is widely used to quantify operative difficulty in cholecystectomy, with higher grades associated with worse post-operative outcomes. However, consistent, scalable PGS assessment is limited by the reliance on two manual steps: determining where to look in the surgical video for key evidence, and assigning a grade. Previous machine learning approaches have either depended on manual selection of where to look, or approximated it with fixed-duration video segments, leaving it unclear whether models can accurately predict PGS without explicit guidance on where to look. METHODS: To address this, we evaluate 287 robotic cholecystectomy videos annotated with PGS and a standardized key-segment. Using a temporal convolution network and attention-based framework, we compare the performance of a fully automated model using full surgical videos without key-segment supervision to a model provided with the key-segment (where to look). RESULTS: Providing the key-segment yields substantial performance gains (weighted F1 +0.25 and Krippendorff's α (KA) +0.29). We further introduce ParkNet LEARN , which learns to where to look and predicts PGS from full surgical videos, achieving significant improvements over the no-supervision automation (weighted F1 +0.18 and KA +0.23), and a KA = 0.60-within 0.06 of the model with key-segment provided. CONCLUSION: These findings highlight the importance of attending to where to look for automating operative difficulty assessment, and is a valuable step toward supporting large-scale research on surgical performance and post-operative outcomes.