361 Automated assessment of facial nerve function using multimodal machine learning
Objectives/Goals: Current popular scoring systems for evaluating facial nerve function are subjective and imprecise. This study aims to quantify speech and facial motor changes in patients suffering from facial palsy after cerebellopontine angle (CPA) tumor resection to lay the foundation for a scor...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Cambridge University Press
2025-04-01
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| Series: | Journal of Clinical and Translational Science |
| Online Access: | https://www.cambridge.org/core/product/identifier/S2059866124009877/type/journal_article |
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| _version_ | 1850097825065467904 |
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| author | Oren Wei Diana Lopez Ioan Lina Kofi Boahene |
| author_facet | Oren Wei Diana Lopez Ioan Lina Kofi Boahene |
| author_sort | Oren Wei |
| collection | DOAJ |
| description | Objectives/Goals: Current popular scoring systems for evaluating facial nerve function are subjective and imprecise. This study aims to quantify speech and facial motor changes in patients suffering from facial palsy after cerebellopontine angle (CPA) tumor resection to lay the foundation for a scoring algorithm that is higher resolution and more objective. Methods/Study Population: We will obtain audio and video recordings from 20 adult patients prior to and after surgical resection of unilateral CPA tumors between October 2024 and February 2025. We will obtain preoperative recordings within two weeks prior to surgery and postoperative recordings following a preset schedule starting from the day after surgery up to one year. Audio recordings entail patient readings of standardized passages and phonations while video recordings entail patient performance of standardized facial expressions. We will analyze video data for key distance measurements, such as eye opening and wrinkle size, using DynaFace. We will process audio data using VoiceLab to extract metrics such as prominence and tonality. We will perform statistical tests such as t-tests and ANOVA to elucidate changes across time. Results/Anticipated Results: I expect to obtain 9 sets of audio and video recordings from each of the 20 participants. In terms of speech, I expect average speech duration to increase postoperatively. Similarly, I expect to find increases in time spent breathing, number of breaths taken, and mean breathing duration. In terms of facial movement, I expect nasolabial fold length to decrease postoperatively, as well as eye opening size and left-right symmetry at rest. For both audio and video, I expect these changes to revert towards their preoperative baseline as time passes. I also expect average House-Brackmann and Sunnybrook facial grading scores to increase postoperatively and then decrease with time, correlating strongly with the video and audio findings. I will use trajectory analysis and time point matching to handle any missing data. Discussion/Significance of Impact: This study will validate our analysis platform’s ability to automatically quantify measurable changes that occur to speech and facial movement which correlate strongly with existing scoring systems. Future work will synthesize these data streams to move towards establishing biomarkers for facial nerve function that aid clinical decision-making. |
| format | Article |
| id | doaj-art-bd3d7622ac2848f3bf4ea7777f5ed939 |
| institution | DOAJ |
| issn | 2059-8661 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Journal of Clinical and Translational Science |
| spelling | doaj-art-bd3d7622ac2848f3bf4ea7777f5ed9392025-08-20T02:40:52ZengCambridge University PressJournal of Clinical and Translational Science2059-86612025-04-01911111110.1017/cts.2024.987361 Automated assessment of facial nerve function using multimodal machine learningOren Wei0Diana Lopez1Ioan Lina2Kofi Boahene3Johns Hopkins University School of MedicineJohns Hopkins University School of Medicine, Departmentof Otolaryngology-Head & Neck SurgeryVanderbilt University Medical Center, Department of Otolaryngology-Head & Neck SurgeryJohns Hopkins University School of Medicine, Departmentof Otolaryngology-Head & Neck SurgeryObjectives/Goals: Current popular scoring systems for evaluating facial nerve function are subjective and imprecise. This study aims to quantify speech and facial motor changes in patients suffering from facial palsy after cerebellopontine angle (CPA) tumor resection to lay the foundation for a scoring algorithm that is higher resolution and more objective. Methods/Study Population: We will obtain audio and video recordings from 20 adult patients prior to and after surgical resection of unilateral CPA tumors between October 2024 and February 2025. We will obtain preoperative recordings within two weeks prior to surgery and postoperative recordings following a preset schedule starting from the day after surgery up to one year. Audio recordings entail patient readings of standardized passages and phonations while video recordings entail patient performance of standardized facial expressions. We will analyze video data for key distance measurements, such as eye opening and wrinkle size, using DynaFace. We will process audio data using VoiceLab to extract metrics such as prominence and tonality. We will perform statistical tests such as t-tests and ANOVA to elucidate changes across time. Results/Anticipated Results: I expect to obtain 9 sets of audio and video recordings from each of the 20 participants. In terms of speech, I expect average speech duration to increase postoperatively. Similarly, I expect to find increases in time spent breathing, number of breaths taken, and mean breathing duration. In terms of facial movement, I expect nasolabial fold length to decrease postoperatively, as well as eye opening size and left-right symmetry at rest. For both audio and video, I expect these changes to revert towards their preoperative baseline as time passes. I also expect average House-Brackmann and Sunnybrook facial grading scores to increase postoperatively and then decrease with time, correlating strongly with the video and audio findings. I will use trajectory analysis and time point matching to handle any missing data. Discussion/Significance of Impact: This study will validate our analysis platform’s ability to automatically quantify measurable changes that occur to speech and facial movement which correlate strongly with existing scoring systems. Future work will synthesize these data streams to move towards establishing biomarkers for facial nerve function that aid clinical decision-making.https://www.cambridge.org/core/product/identifier/S2059866124009877/type/journal_article |
| spellingShingle | Oren Wei Diana Lopez Ioan Lina Kofi Boahene 361 Automated assessment of facial nerve function using multimodal machine learning Journal of Clinical and Translational Science |
| title | 361 Automated assessment of facial nerve function using multimodal machine learning |
| title_full | 361 Automated assessment of facial nerve function using multimodal machine learning |
| title_fullStr | 361 Automated assessment of facial nerve function using multimodal machine learning |
| title_full_unstemmed | 361 Automated assessment of facial nerve function using multimodal machine learning |
| title_short | 361 Automated assessment of facial nerve function using multimodal machine learning |
| title_sort | 361 automated assessment of facial nerve function using multimodal machine learning |
| url | https://www.cambridge.org/core/product/identifier/S2059866124009877/type/journal_article |
| work_keys_str_mv | AT orenwei 361automatedassessmentoffacialnervefunctionusingmultimodalmachinelearning AT dianalopez 361automatedassessmentoffacialnervefunctionusingmultimodalmachinelearning AT ioanlina 361automatedassessmentoffacialnervefunctionusingmultimodalmachinelearning AT kofiboahene 361automatedassessmentoffacialnervefunctionusingmultimodalmachinelearning |