{"title":"人工智能在基于手部和前臂掌侧撕裂伤临床图像预测损伤结构中的作用。","authors":"Arman Vahabi, Ali Engin Daştan, Hüseyin Günay","doi":"10.1016/j.jham.2025.100255","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>Recently introduced image processing capabilities of AI models, which are accessible to a broad audience, may contribute to progress in medical research. Inspection and physical examination are important components of hand injury assessment, but they have inherent limitations in accuracy. The purpose of this study was to compare the structures identified as damaged during physical examination with those predicted by an AI model, utilizing its image processing capability. We hypothesized that the AI tool would demonstrate a level of accuracy comparable to that of physical examination in predicting injured structures.</p><p><strong>Methods: </strong>We retrospectively reviewed the files of patients with hand and forearm injuries related to the volar aspect from January 2024 to July 2024. After exclusions, a total of 30 patients were included in the final analyses. Structures suspected to be damaged based on the initial evaluation and those identified as injured during surgery were documented through chart review. For the same patients, the AI tool (ChatGPT-4.0) was utilized to predict injured structures from clinical photos obtained during the initial examination. We examined the correlation and overlap between the structures identified as injured during the initial clinical examination and those predicted by the AI tool, as well as the correlation and overlap between the structures predicted by the AI tool and those confirmed as injured during surgical procedures.</p><p><strong>Results: </strong>The sensitivity of the physical examination was found to be 66.0 % (95 % CI: 57.5 %-73.7 %), while the specificity was 98,7 % (95 % CI: 97,6 % to 99,4 %). The sensitivity of the AI tool was found to be 61.7 % (95 % CI: 53.1 %-69.8 %), while the specificity was 82.4 % (95 % CI: 79.4 %-85.2 %).</p><p><strong>Conclusion: </strong>In its current form, AI demonstrates limited yet promising potential as an adjunctive tool in the clinical evaluation of flexor-side injuries of the hand and forearm.</p><p><strong>Level of evidence: </strong>III, Diagnostic study.</p>","PeriodicalId":45368,"journal":{"name":"Journal of Hand and Microsurgery","volume":"17 4","pages":"100255"},"PeriodicalIF":0.3000,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12018172/pdf/","citationCount":"0","resultStr":"{\"title\":\"The role of artificial intelligence in predicting injured structures based on clinical images of lacerations in the volar aspect of the hand and forearm.\",\"authors\":\"Arman Vahabi, Ali Engin Daştan, Hüseyin Günay\",\"doi\":\"10.1016/j.jham.2025.100255\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>Recently introduced image processing capabilities of AI models, which are accessible to a broad audience, may contribute to progress in medical research. Inspection and physical examination are important components of hand injury assessment, but they have inherent limitations in accuracy. The purpose of this study was to compare the structures identified as damaged during physical examination with those predicted by an AI model, utilizing its image processing capability. We hypothesized that the AI tool would demonstrate a level of accuracy comparable to that of physical examination in predicting injured structures.</p><p><strong>Methods: </strong>We retrospectively reviewed the files of patients with hand and forearm injuries related to the volar aspect from January 2024 to July 2024. After exclusions, a total of 30 patients were included in the final analyses. Structures suspected to be damaged based on the initial evaluation and those identified as injured during surgery were documented through chart review. For the same patients, the AI tool (ChatGPT-4.0) was utilized to predict injured structures from clinical photos obtained during the initial examination. We examined the correlation and overlap between the structures identified as injured during the initial clinical examination and those predicted by the AI tool, as well as the correlation and overlap between the structures predicted by the AI tool and those confirmed as injured during surgical procedures.</p><p><strong>Results: </strong>The sensitivity of the physical examination was found to be 66.0 % (95 % CI: 57.5 %-73.7 %), while the specificity was 98,7 % (95 % CI: 97,6 % to 99,4 %). The sensitivity of the AI tool was found to be 61.7 % (95 % CI: 53.1 %-69.8 %), while the specificity was 82.4 % (95 % CI: 79.4 %-85.2 %).</p><p><strong>Conclusion: </strong>In its current form, AI demonstrates limited yet promising potential as an adjunctive tool in the clinical evaluation of flexor-side injuries of the hand and forearm.</p><p><strong>Level of evidence: </strong>III, Diagnostic study.</p>\",\"PeriodicalId\":45368,\"journal\":{\"name\":\"Journal of Hand and Microsurgery\",\"volume\":\"17 4\",\"pages\":\"100255\"},\"PeriodicalIF\":0.3000,\"publicationDate\":\"2025-04-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12018172/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Hand and Microsurgery\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1016/j.jham.2025.100255\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/7/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q4\",\"JCRName\":\"SURGERY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Hand and Microsurgery","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.jham.2025.100255","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/7/1 0:00:00","PubModel":"eCollection","JCR":"Q4","JCRName":"SURGERY","Score":null,"Total":0}
The role of artificial intelligence in predicting injured structures based on clinical images of lacerations in the volar aspect of the hand and forearm.
Introduction: Recently introduced image processing capabilities of AI models, which are accessible to a broad audience, may contribute to progress in medical research. Inspection and physical examination are important components of hand injury assessment, but they have inherent limitations in accuracy. The purpose of this study was to compare the structures identified as damaged during physical examination with those predicted by an AI model, utilizing its image processing capability. We hypothesized that the AI tool would demonstrate a level of accuracy comparable to that of physical examination in predicting injured structures.
Methods: We retrospectively reviewed the files of patients with hand and forearm injuries related to the volar aspect from January 2024 to July 2024. After exclusions, a total of 30 patients were included in the final analyses. Structures suspected to be damaged based on the initial evaluation and those identified as injured during surgery were documented through chart review. For the same patients, the AI tool (ChatGPT-4.0) was utilized to predict injured structures from clinical photos obtained during the initial examination. We examined the correlation and overlap between the structures identified as injured during the initial clinical examination and those predicted by the AI tool, as well as the correlation and overlap between the structures predicted by the AI tool and those confirmed as injured during surgical procedures.
Results: The sensitivity of the physical examination was found to be 66.0 % (95 % CI: 57.5 %-73.7 %), while the specificity was 98,7 % (95 % CI: 97,6 % to 99,4 %). The sensitivity of the AI tool was found to be 61.7 % (95 % CI: 53.1 %-69.8 %), while the specificity was 82.4 % (95 % CI: 79.4 %-85.2 %).
Conclusion: In its current form, AI demonstrates limited yet promising potential as an adjunctive tool in the clinical evaluation of flexor-side injuries of the hand and forearm.
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