Development and validation of a collaborative framework for assessment of peripheral facial paralysis using facial image regions of interest.

While accurate evaluation of PFP is crucial for determining optimal treatment strategies, current clinical assessments rely heavily on subjective evaluations, leading to considerable variability between inter- and intra-observer ratings. This study aimed to develop and validate a collaborative framework for evaluating PFP based on regions of interest in facial images. We developed and tested two approaches: (1) a collaborative framework integrating image interpretation techniques (representation learning via CNN) with predefined handcrafted features based on regions of interest in facial images, and (2) a convolutional neural network (CNN) model trained exclusively on full-face patient images. The diagnostic accuracy of both systems was evaluated using a test set and compared with otologists' assessments. The collaborative framework achieved a mean Area Under the Curve (AUC) of 0.92 for PFP prediction in the test set, surpassing the 0.76 AUC achieved by the CNN trained on full-face images. The framework's performance matched that of experienced otologists (accuracy: 80.0% vs. 77.2%; sensitivity: 85.3% vs. 77.7%). Moreover, system assistance improved primary clinicians' mean accuracy by 17.7 percentage points. These findings demonstrate that our collaborative framework-based automated diagnosis system can effectively assist clinicians in PFP diagnosis.