Towards a Reliable and Rapid Automated Grading System in Facial Palsy Patients: Facial Palsy Surgery Meets Computer Science

  • Background: Reliable, time- and cost-effective, and clinician-friendly diagnostic tools are cornerstones in facial palsy (FP) patient management. Different automated FP grading systems have been developed but revealed persisting downsides such as insufficient accuracy and cost-intensive hardware. We aimed to overcome these barriers and programmed an automated grading system for FP patients utilizing the House and Brackmann scale (HBS). Methods: Image datasets of 86 patients seen at the Department of Plastic, Hand, and Reconstructive Surgery at the University Hospital Regensburg, Germany, between June 2017 and May 2021, were used to train the neural network and evaluate its accuracy. Nine facial poses per patient were analyzed by the algorithm. Results: The algorithm showed an accuracy of 100%. Oversampling did not result in altered outcomes, while the direct form displayed superior accuracy levels when compared to the modular classification form (n = 86; 100% vs. 99%). The Early Fusion technique was linked to improved accuracy outcomes in comparison to the Late Fusion and sequential method (n = 86; 100% vs. 96% vs. 97%). Conclusions: Our automated FP grading system combines high-level accuracy with cost- and time-effectiveness. Our algorithm may accelerate the grading process in FP patients and facilitate the FP surgeon’s workflow.

Export metadata

Additional Services

Share in Twitter Search Google Scholar Statistics
Author:Leonard Knoedler, Helena Baecher, Martin Kauke-Navarro, Lukas PrantlORCiD, Hans-Günther Machens, Philipp Scheuermann, Christoph PalmORCiDGND, Raphael Baumann, Andreas Kehrer, Adriana C. Panayi, Samuel Knoedler
Parent Title (English):Journal of Clinical Medicine
Place of publication:Basel
Document Type:Article
Year of first Publication:2022
Release Date:2022/12/19
Article Number:4998
Institutes:Fakultät Informatik und Mathematik
Fakultät Informatik und Mathematik / Regensburg Medical Image Computing (ReMIC)
OpenAccess Publikationsweg:Gold Open Access- Erstveröffentlichung in einem/als Open-Access-Medium
research focus:Lebenswissenschaften und Ethik
Licence (German):Creative Commons - CC BY - Namensnennung 4.0 International