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

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.

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).

Providing the key-segment yields substantial performance gains (weighted F1 +0.25 and Krippendorff's $\alpha$ (KA) +0.29). We further introduce ParkNet $_{\mathrm{LEARN}}^{}$ , 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.

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.