Knoedler, Leonard and Miragall, Maximilian and Kauke-Navarro, Martin and Obed, Doha and Bauer, Maximilian and Tissler, Patrick and Prantl, Lukas and Machens, Hans-Guenther and Broer, Peter Niclas and Baecher, Helena and Panayi, Adriana C. and Knoedler, Samuel and Kehrer, Andreas (2022) A Ready-to-Use Grading Tool for Facial Palsy Examiners-Automated Grading System in Facial Palsy Patients Made Easy. JOURNAL OF PERSONALIZED MEDICINE, 12 (10): 1739. ISSN , 2075-4426
Full text not available from this repository. (Request a copy)Abstract
Background: The grading process in facial palsy (FP) patients is crucial for time- and cost-effective therapy decision-making. The House-Brackmann scale (HBS) represents the most commonly used classification system in FP diagnostics. This study investigated the benefits of linking machine learning (ML) techniques with the HBS. Methods: Image datasets of 51 patients seen at the Department of Plastic, Hand, and Reconstructive Surgery at the University Hospital Regensburg, Germany, between June 2020 and May 2021, were used to build the neural network. A total of nine facial poses per patient were used to automatically determine the HBS. Results: The algorithm had an accuracy of 98%. The algorithm processed the real patient image series (i.e., nine images per patient) in 112 ms. For optimized accuracy, we found 30 training runs to be the most effective training length. Conclusion: We have developed an easy-to-use, time- and cost-efficient algorithm that provides highly accurate automated grading of FP patient images. In combination with our application, the algorithm may facilitate the FP surgeon's clinical workflow.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | BELLS-PALSY; DIAGNOSIS; MANAGEMENT; PARALYSIS; ETIOLOGY; MUSCLES; MEMBERS; facial palsy; facial paralysis; House-Brackmann scale; artificial intelligence; deep learning; bell's palsy; smile restoration; facial reanimation; application |
| Subjects: | 000 Computer science, information & general works > 004 Computer science 600 Technology > 610 Medical sciences Medicine |
| Divisions: | Medicine > Lehrstuhl für Mund-, Kiefer- und Gesichtschirurgie Medicine > Zentren des Universitätsklinikums Regensburg > Zentrum für Plastische-, Hand- und Wiederherstellungschirurgie Informatics and Data Science |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 14 Feb 2024 14:35 |
| Last Modified: | 14 Feb 2024 14:35 |
| URI: | https://pred.uni-regensburg.de/id/eprint/57474 |
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