Development and validation of a collaborative framework for assessment of peripheral facial paralysis using facial image regions of interest
Year: 2025
Type: article
Abstract: BACKGROUND: 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. OBJECTIVE: This study aimed to develop and validate a collaborative framework for evaluating PFP based on regions of interest in facial images. METHODS: 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. RESULTS: 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. CONCLUSIONS: These findings demonstrate that our collaborative framework-based automated diagnosis system can effectively assist clinicians in PFP diagnosis.
Source: Acta Oto-Laryngologica
Authors: Xiaoyan Guo, Jiyue Chen, Pingju Lin, Qi Lu, Ting Kou, Kun Li, Shiming Yang, Weidong Shen
Language: English
Subfield: Neurology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W4410192602