Voluntary and Spontaneous Smile Quantification in Facial Palsy Patients: Validation of a Novel Mobile Application

Fuzi, Jordan; Meller, Catherine; Ch’ng, Sydney; Hadlock, Tessa; Dusseldorp, Joseph R.

doi:10.1089/fpsam.2022.0104

Cited by 6 publications

(3 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The current literature using AI describes various attempts to integrate various functions into preoperative surgical planning, patient education, and assessment of outcomes. 9–16 No study to our knowledge has assessed the feasibility of a patient interactive AI program for answering questions, troubleshooting complications, and providing clinical recommendations during the perioperative period.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence as a Triage Tool during the Perioperative Period: Pilot Study of Accuracy and Accessibility for Clinical Application

Boyd,

Hemal,

Sorenson

et al. 2024

Plastic and Reconstructive Surgery - Global Open

View full text Add to dashboard Cite

Background: Given the dialogistic properties of ChatGPT, we hypothesized that this artificial intelligence (AI) function can be used as a self-service tool where clinical questions can be directly answered by AI. Our objective was to assess the content, accuracy, and accessibility of AI-generated content regarding common perioperative questions for reduction mammaplasty. Methods: ChatGPT (OpenAI, February Version, San Francisco, Calif.) was used to query 20 common patient concerns that arise in the perioperative period of a reduction mammaplasty. Searches were performed in duplicate for both a general term and a specific clinical question. Query outputs were analyzed both objectively and subjectively. Descriptive statistics, t tests, and chi-square tests were performed where appropriate with a predetermined level of significance of P less than 0.05. Results: From a total of 40 AI-generated outputs, mean word length was 191.8 words. Readability was at the thirteenth grade level. Regarding content, of all query outputs, 97.5% were on the appropriate topic. Medical advice was deemed to be reasonable in 100% of cases. General queries more frequently reported overarching background information, whereas specific queries more frequently reported prescriptive information (P < 0.0001). AI outputs specifically recommended following surgeon provided postoperative instructions in 82.5% of instances. Conclusions: Currently available AI tools, in their nascent form, can provide recommendations for common perioperative questions and concerns for reduction mammaplasty. With further calibration, AI interfaces may serve as a tool for fielding patient queries in the future; however, patients must always retain the ability to bypass technology and be able to contact their surgeon.

show abstract

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence as a Triage Tool during the Perioperative Period: Pilot Study of Accuracy and Accessibility for Clinical Application

Boyd,

Hemal,

Sorenson

et al. 2024

Plastic and Reconstructive Surgery - Global Open

View full text Add to dashboard Cite

show abstract

“…8 Patient-based surveys such as the FACE Instrument pose a similar limitation, although they also uniquely capture the individual's experience. 7 While artificial intelligence (AI) and machine learning (ML) hold promise for improved methods of automated quantification of facial function, most currently available ML algorithms are either limited to static image analysis (such as Emotrics or the auto-eFACE 9,10 ), require proprietary marketing software to estimate facial emotional expressions [11][12][13][14][15] or were limited to a single surgical case report 16 following surgical interventions. An objective, open-source, rigorous quantification of dynamic facial function in videos has remained elusive.…”

Section: Introductionmentioning

confidence: 99%

Learning from Machine Learning: advancing from static to dynamic facial function quantification

Kalavacherla,

Mills,

Greene

2024

Preprint

View full text Add to dashboard Cite

ObjectivesWe assess an open-source Python machine learning algorithm’s efficacy in image and video analysis of facial palsy (FP) patients.MethodsImages and videos of 60 patients with varying FP severities performing standard movements were obtained from MEEI Facial Palsy database. Landmarks generated on images by the open-source algorithm (adapted from OpenCV and Dlib libraries) and Emotrics (standard for two-dimensional FP analysis) were compared. Considering the human eye as the standard for accuracy, three raters marked perceived errors in each algorithm’s tracking of five facial features. Cumulative error distributions between both algorithms were compared via normalized root mean square error. FP severity and facial feature-specific error rates were compared using ANOVA tests. Raters also analyzed open-source algorithm-generated video landmarks; similar statistical comparisons between open-source algorithms’ image and video-based analyses were performed.ResultsCumulative error distribution between both algorithms’ image analyses was most robust for normal function; significant discrepancies were observed in mild/moderate flaccidity and nearly-normal/complete synkinesis. Both algorithms had similar error rates across all facial features (p=0.76) and FP severities (p=0.37). In the open-source algorithm’s video analysis, mild synkinesis (24.7%) and severe flaccidity (19.7%) had the highest error rates. Comparing image and video analyses generated by the open-source algorithm, video analyses had lower error rates across all FP severities (p<0.001).ConclusionsWe report for the first time the feasibility and relative accuracy of a Python open-source algorithm for dynamic facial landmark tracking in FP videos. The demonstrated superiority of landmark tracking with videos over images can improve objective FP quantification.

show abstract

“…The most advanced method of automated facial analysis in facial paralysis patients, Emotrics, is limited to static photos only and requires significant manual landmark adjustments [23]. Other attempts to quantify facial function have incorporated proprietary marketing software to estimate facial emotional expressions [24] [25] [26] [27] or were limited to a single surgical case report [28] following surgical interventions. An objective, open-source, rigorous quantification of dynamic facial function in videos has remained elusive [28] [29] [30], as there are multiple challenges to accumulating a sufficiently large training dataset to improve landmark accuracy in the facial paralysis population.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Methods to Track Dynamic Facial Function in Facial Palsy

Rao,

Greene,

Coleman

2024

Preprint

View full text Add to dashboard Cite

For patients with facial paralysis, the wait for return of facial function and the resulting vision risk from poor eye closure, difficulty speaking and eating from flaccid oral sphincter muscles, as well as the psychological morbidity from the inability to smile or express emotions through facial movement can be devastating. There are limited methods to assess ongoing facial nerve regeneration: clinicians rely on subjective descriptions, imprecise scales, and static photographs to evaluate facial functional recovery and thus facial nerve regeneration remains poorly understood. We propose a more precise evaluation of dynamic facial function through video-based machine learning analysis which would facilitate a better understanding of the sometimes subtle onset of facial nerve recovery and improve guidance for facial reanimation surgery. Specifically, we here present machine learning methods employing likelihood ratio tests, optimal transport theory, and Mahalanobis distances to: 1) assess the use of defined facial landmarks for binary classification of different types of facial palsy; 2) identify regions of asymmetry and potential paralysis during specific facial cues; and 3) determining severity of abnormal facial function when compared to a reference class of normal facial function. Our work presents promising results of utilizing videos, rather than static photographs, to provide robust quantitative analyses of dynamic properties for various facial movements without requiring manual assessment. The long-term potential of this project is to enable clinicians to have more accurate and timely information to make decisions for facial reanimation surgery which will have drastic consequences on quality of life for affected patients.

show abstract

Voluntary and Spontaneous Smile Quantification in Facial Palsy Patients: Validation of a Novel Mobile Application

Cited by 6 publications

References 36 publications

Artificial Intelligence as a Triage Tool during the Perioperative Period: Pilot Study of Accuracy and Accessibility for Clinical Application

Artificial Intelligence as a Triage Tool during the Perioperative Period: Pilot Study of Accuracy and Accessibility for Clinical Application

Learning from Machine Learning: advancing from static to dynamic facial function quantification

Machine Learning Methods to Track Dynamic Facial Function in Facial Palsy

Contact Info

Product

Resources

About