ArticleJournal of personalized medicine2022
A Ready-to-Use Grading Tool for Facial Palsy Examiners-Automated Grading System in Facial Palsy Patients Made Easy.
Article in Journal of personalized medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
19 citing papers in PubMed.
- Resource utilization and outcomes of inpatient eyelid reconstruction in facial palsy patients: A 20-year HCUP-NIS study.JPRAS open · 2026Article
- Evolution of Facial Assessment in Aesthetic Medicine: 3D Imaging, Digital Analysis, and Patient-Reported Outcomes.Aesthetic plastic surgery · 2026Review
- Clinician-Led Development and Feasibility of a Neural Network for Assessing 3D Dental Cavity Preparations Assisted by Conversational AI.Dentistry journal · 2025Article
- Diagnosing facial synkinesis using artificial intelligence to advance facial palsy care.Scientific reports · 2025Article
- Quantitative Assessment of Facial Paralysis Using Dynamic 3D Photogrammetry and Deep Learning: A Hybrid Approach Integrating Expert Consensus.Sensors (Basel, Switzerland) · 2025Article
- Evaluation of nerve function after Bell's palsy based on different facial nerve assessment scales HBGS/SFGS/MPS: A comparative study.PloS one · 2025Article
- Turn Your Vision into Reality-AI-Powered Pre-operative Outcome Simulation in Rhinoplasty Surgery.Aesthetic plastic surgery · 2024Article
- Truth Lies in the Depths: Novel Insights into Facial Aesthetic Measurements from a U.S. Survey Panel.Aesthetic plastic surgery · 2024Article
- In-depth analysis of ChatGPT's performance based on specific signaling words and phrases in the question stem of 2377 USMLE step 1 style questions.Scientific reports · 2024Article
- Sailing the Seven Seas: A Multinational Comparison of ChatGPT's Performance on Medical Licensing Examinations.Annals of biomedical engineering · 2024Article
- [The assessment of facial nerve function].Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery · 2024Article
- Pure Wisdom or Potemkin Villages? A Comparison of ChatGPT 3.5 and ChatGPT 4 on USMLE Step 3 Style Questions: Quantitative Analysis.JMIR medical education · 2024Article
- The law code of ChatGPT and artificial intelligence-how to shield plastic surgeons and reconstructive surgeons against Justitia's sword.Frontiers in surgery · 2024Article
- Diagnosing lagophthalmos using artificial intelligence.Scientific reports · 2023Article
- Face the Future-Artificial Intelligence in Oral and Maxillofacial Surgery.Journal of clinical medicine · 2023Review
- Article
- ChatGPT's quiz skills in different otolaryngology subspecialties: an analysis of 2576 single-choice and multiple-choice board certification preparation questions.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2023Article
- Histomorphometry of the Sural Nerve for Use as a CFNG in Facial Reanimation Procedures.Journal of clinical medicine · 2023Article
- DeepSmile: Anomaly Detection Software for Facial Movement Assessment.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe 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.
methodsImage 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.
resultsThe 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.
conclusionWe 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.
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