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

被引:18
作者
Knoedler, Leonard [1 ]
Baecher, Helena [1 ]
Kauke-Navarro, Martin [2 ]
Prantl, Lukas [1 ]
Machens, Hans-Gunther [3 ]
Scheuermann, Philipp [1 ]
Palm, Christoph [4 ]
Baumann, Raphael [4 ]
Kehrer, Andreas [1 ]
Panayi, Adriana C. [5 ]
Knoedler, Samuel [1 ,3 ,5 ]
机构
[1] Univ Hosp Regensburg, Dept Plast Hand & Reconstruct Surg, D-93053 Regensburg, Germany
[2] Yale Sch Med, Dept Surg, Div Plast Surg, New Haven, CT 06510 USA
[3] Tech Univ Munich, Dept Plast Surg & Hand Surg, Klinikum Rechts Isar, D-81675 Munich, Germany
[4] Ostbayr TH Regensburg, Regensburg Med Image Comp Lab, D-93053 Regensburg, Germany
[5] Harvard Med Sch, Brigham & Womens Hosp, Dept Surg, Div Plast Surg, Boston, MA 02115 USA
关键词
Bell's palsy; idiopathic facial paralysis; facial palsy; machine learning; grading systems; automated grading; artificial intelligence; NERVE; MANAGEMENT; PARALYSIS; REANIMATION;
D O I
10.3390/jcm11174998
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
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.
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页数:11
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