Knoedler, Leonard and Baecher, Helena and Kauke-Navarro, Martin and Prantl, Lukas and Machens, Hans-Gunther and Scheuermann, Philipp and Palm, Christoph and Baumann, Raphael and Kehrer, Andreas and Panayi, Adriana C. and Knoedler, Samuel (2022) Towards a Reliable and Rapid Automated Grading System in Facial Palsy Patients: Facial Palsy Surgery Meets Computer Science. JOURNAL OF CLINICAL MEDICINE, 11 (17): 4998. ISSN , 2077-0383
Full text not available from this repository. (Request a copy)Abstract
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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | NERVE; MANAGEMENT; PARALYSIS; REANIMATION; Bell's palsy; idiopathic facial paralysis; facial palsy; machine learning; grading systems; automated grading; artificial intelligence |
| Subjects: | 600 Technology > 610 Medical sciences Medicine |
| Divisions: | Medicine > Zentren des Universitätsklinikums Regensburg > Zentrum für Plastische-, Hand- und Wiederherstellungschirurgie |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 14 Feb 2024 08:03 |
| Last Modified: | 14 Feb 2024 08:03 |
| URI: | https://pred.uni-regensburg.de/id/eprint/57294 |
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