Dataset Information

Prediagnosis recognition of acute ischemic stroke by artificial intelligence from facial images.

ABSTRACT: Stroke is a major threat to life and health in modern society, especially in the aging population. Stroke may cause sudden death or severe sequela-like hemiplegia. Although computed tomography (CT) and magnetic resonance imaging (MRI) are standard diagnosis methods, and artificial intelligence models have been built based on these images, shortage in medical resources and the time and cost of CT/MRI imaging hamper fast detection, thus increasing the severity of stroke. Here, we developed a convolutional neural network model by integrating four networks, Xception, ResNet50, VGG19, and EfficientNetb1, to recognize stroke based on 2D facial images with a cross-validation area under curve (AUC) of 0.91 within the training set of 185 acute ischemic stroke patients and 551 age- and sex-matched controls, and AUC of 0.82 in an independent data set regardless of age and sex. The model computed stroke probability was quantitatively associated with facial features, various clinical parameters of blood clotting indicators and leukocyte counts, and, more importantly, stroke incidence in the near future. Our real-time facial image artificial intelligence model can be used to rapidly screen and prediagnose stroke before CT scanning, thus meeting the urgent need in emergency clinics, potentially translatable to routine monitoring.

SUBMITTER: Wang Y

PROVIDER: S-EPMC11320352 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature

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Prediagnosis recognition of acute ischemic stroke by artificial intelligence from facial images.

Wang Yiyang Y Ye Yunyan Y Shi Shengyi S Mao Kehang K Zheng Haonan H Chen Xuguang X Yan Hanting H Lu Yiming Y Zhou Yong Y Ye Weimin W Ye Jing J Han Jing-Dong J JJ

Aging cell 20240606 8

Stroke is a major threat to life and health in modern society, especially in the aging population. Stroke may cause sudden death or severe sequela-like hemiplegia. Although computed tomography (CT) and magnetic resonance imaging (MRI) are standard diagnosis methods, and artificial intelligence models have been built based on these images, shortage in medical resources and the time and cost of CT/MRI imaging hamper fast detection, thus increasing the severity of stroke. Here, we developed a convo ...[more]

PMID: 38845183

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Project description:BackgroundCentral facial palsy (CFP), resulting from upper motor neuron lesions in the corticofacial pathway, is traditionally characterized by the sparing of the upper facial muscles. However, reports of upper facial weakness in CFP due to acute ischemic stroke have challenged this long-held assumption. This study aimed to determine the prevalence of upper facial weakness in CFP and identify its associated clinical factors.MethodsIn this cross-sectional study, we evaluated consecutive patients with acute ischemic stroke admitted to a university hospital in Thailand from January 2022 to June 2023. Full-face video recordings were analyzed using the Sunnybrook Facial Grading System. Upper facial weakness was defined as asymmetry in at least one upper facial expression. Multivariable logistic regression was performed to identify factors associated with upper facial weakness.ResultsOf 108 patients with acute ischemic stroke, 92 had CFP, and among these, 70 (76%) demonstrated upper facial weakness. Tight eye closure (force and wrinkle formation, both 42%) was the most sensitive indicator for detecting upper facial weakness. Greater stroke severity, as reflected by higher NIHSS scores (adjusted odds ratio [aOR], 1.42; 95% CI 1.07-1.88) and the presence of lower facial weakness (aOR, 6.56; 95% CI 1.85-23.29) were significantly associated with upper facial involvement. Although upper facial weakness was generally milder than lower facial weakness, its severity correlated with increasing lower facial asymmetry during movement.ConclusionsContrary to traditional teaching, upper facial weakness is common in CFP due to acute ischemic stroke. The severity of stroke and the presence of lower facial weakness are key predictors of upper facial involvement. These findings underscore the need for clinicians to reconsider the diagnostic paradigm, recognizing that upper facial weakness can occur in CFP. Enhanced awareness may improve diagnostic accuracy, inform treatment decisions, and ultimately lead to better patient outcomes.

Project description:ImportanceThe lack of standardized genetics training in pediatrics residencies, along with a shortage of medical geneticists, necessitates innovative educational approaches.ObjectiveTo compare pediatric resident recognition of Kabuki syndrome (KS) and Noonan syndrome (NS) after 1 of 4 educational interventions, including generative artificial intelligence (AI) methods.Design, setting, and participantsThis comparative effectiveness study used generative AI to create images of children with KS and NS. From October 1, 2022, to February 28, 2023, US pediatric residents were provided images through a web-based survey to assess whether these images helped them recognize genetic conditions.InterventionsParticipants categorized 20 images after exposure to 1 of 4 educational interventions (text-only descriptions, real images, and 2 types of images created by generative AI).Main outcomes and measuresAssociations between educational interventions with accuracy and self-reported confidence.ResultsOf 2515 contacted pediatric residents, 106 and 102 completed the KS and NS surveys, respectively. For KS, the sensitivity of text description was 48.5% (128 of 264), which was not significantly different from random guessing (odds ratio [OR], 0.94; 95% CI, 0.69-1.29; P = .71). Sensitivity was thus compared for real images vs random guessing (60.3% [188 of 312]; OR, 1.52; 95% CI, 1.15-2.00; P = .003) and 2 types of generative AI images vs random guessing (57.0% [212 of 372]; OR, 1.32; 95% CI, 1.04-1.69; P = .02 and 59.6% [193 of 324]; OR, 1.47; 95% CI, 1.12-1.94; P = .006) (denominators differ according to survey responses). The sensitivity of the NS text-only description was 65.3% (196 of 300). Compared with text-only, the sensitivity of the real images was 74.3% (205 of 276; OR, 1.53; 95% CI, 1.08-2.18; P = .02), and the sensitivity of the 2 types of images created by generative AI was 68.0% (204 of 300; OR, 1.13; 95% CI, 0.77-1.66; P = .54) and 71.0% (247 of 328; OR, 1.30; 95% CI, 0.92-1.83; P = .14). For specificity, no intervention was statistically different from text only. After the interventions, the number of participants who reported being unsure about important diagnostic facial features decreased from 56 (52.8%) to 5 (7.6%) for KS (P < .001) and 25 (24.5%) to 4 (4.7%) for NS (P < .001). There was a significant association between confidence level and sensitivity for real and generated images.Conclusions and relevanceIn this study, real and generated images helped participants recognize KS and NS; real images appeared most helpful. Generated images were noninferior to real images and could serve an adjunctive role, particularly for rare conditions.

Dataset Information

Prediagnosis recognition of acute ischemic stroke by artificial intelligence from facial images.

Publications

Prediagnosis recognition of acute ischemic stroke by artificial intelligence from facial images.

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