Pedro Diniz, Bernd Grimm, Caroline Mouton, Christophe Ley, Thor Einar Andersen, Romain Seil
{"title":"人工智能框架在交叉检查公共数据库中男性职业足球前十字韧带撕裂报告中的高特异性。","authors":"Pedro Diniz, Bernd Grimm, Caroline Mouton, Christophe Ley, Thor Einar Andersen, Romain Seil","doi":"10.1002/ksa.12571","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>While public databases like Transfermarkt provide valuable data for assessing the impact of anterior cruciate ligament (ACL) injuries in professional footballers, they require robust verification methods due to accuracy concerns. We hypothesised that an artificial intelligence (AI)-powered framework could cross-check ACL tear-related information from large publicly available data sets with high specificity.</p><p><strong>Methods: </strong>The AI-powered framework uses Google Programmable Search Engine to search a curated, multilingual list of websites and OpenAI's GPT to translate search queries, appraise search results and analyse injury-related information in search result items (SRIs). Specificity was the chosen performance metric-the AI-powered framework's ability to accurately identify texts that do not mention an athlete suffering an ACL tear-with SRI as the evaluation unit. A database of ACL tears in male professional footballers from first- and second-tier leagues worldwide (1999-2024) was collected from Transfermarkt.com, and players were randomly selected for appraisal until enough SRIs were obtained to validate the framework's specificity. Player age at injury and time until return-to-play (RTP) were recorded and compared with Union of European Football Associations (UEFA) Elite Club Injury Study data.</p><p><strong>Results: </strong>Verification of 231 athletes yielded 1546 SRIs. Human analysis of the SRIs showed that 335 mentioned an ACL tear, corresponding to 83 athletes with ACL tears. Specificity and sensitivity of GPT in identifying mentions of ACL tears in a player were 99.3% and 88.4%, respectively. Mean age at rupture was 26.6 years (standard deviation: 4.6, 95% confidence interval [CI]: 25.6-27.6). Median RTP time was 225 days (interquartile range: 96, 95% CI: 209-251), which is comparable to reports using data from the UEFA Elite Club Injury Study.</p><p><strong>Conclusion: </strong>This study shows that an AI-powered framework can achieve high specificity in cross-checking ACL tear reports in male professional football from public databases, markedly reducing manual workload and enhancing the reliability of media-based sports medicine research.</p><p><strong>Level of evidence: </strong>Level III.</p>","PeriodicalId":17880,"journal":{"name":"Knee Surgery, Sports Traumatology, Arthroscopy","volume":" ","pages":""},"PeriodicalIF":3.3000,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"High specificity of an AI-powered framework in cross-checking male professional football anterior cruciate ligament tear reports in public databases.\",\"authors\":\"Pedro Diniz, Bernd Grimm, Caroline Mouton, Christophe Ley, Thor Einar Andersen, Romain Seil\",\"doi\":\"10.1002/ksa.12571\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>While public databases like Transfermarkt provide valuable data for assessing the impact of anterior cruciate ligament (ACL) injuries in professional footballers, they require robust verification methods due to accuracy concerns. We hypothesised that an artificial intelligence (AI)-powered framework could cross-check ACL tear-related information from large publicly available data sets with high specificity.</p><p><strong>Methods: </strong>The AI-powered framework uses Google Programmable Search Engine to search a curated, multilingual list of websites and OpenAI's GPT to translate search queries, appraise search results and analyse injury-related information in search result items (SRIs). Specificity was the chosen performance metric-the AI-powered framework's ability to accurately identify texts that do not mention an athlete suffering an ACL tear-with SRI as the evaluation unit. A database of ACL tears in male professional footballers from first- and second-tier leagues worldwide (1999-2024) was collected from Transfermarkt.com, and players were randomly selected for appraisal until enough SRIs were obtained to validate the framework's specificity. Player age at injury and time until return-to-play (RTP) were recorded and compared with Union of European Football Associations (UEFA) Elite Club Injury Study data.</p><p><strong>Results: </strong>Verification of 231 athletes yielded 1546 SRIs. Human analysis of the SRIs showed that 335 mentioned an ACL tear, corresponding to 83 athletes with ACL tears. Specificity and sensitivity of GPT in identifying mentions of ACL tears in a player were 99.3% and 88.4%, respectively. Mean age at rupture was 26.6 years (standard deviation: 4.6, 95% confidence interval [CI]: 25.6-27.6). Median RTP time was 225 days (interquartile range: 96, 95% CI: 209-251), which is comparable to reports using data from the UEFA Elite Club Injury Study.</p><p><strong>Conclusion: </strong>This study shows that an AI-powered framework can achieve high specificity in cross-checking ACL tear reports in male professional football from public databases, markedly reducing manual workload and enhancing the reliability of media-based sports medicine research.</p><p><strong>Level of evidence: </strong>Level III.</p>\",\"PeriodicalId\":17880,\"journal\":{\"name\":\"Knee Surgery, Sports Traumatology, Arthroscopy\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":3.3000,\"publicationDate\":\"2024-12-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Knee Surgery, Sports Traumatology, Arthroscopy\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1002/ksa.12571\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ORTHOPEDICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Knee Surgery, Sports Traumatology, Arthroscopy","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1002/ksa.12571","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ORTHOPEDICS","Score":null,"Total":0}
High specificity of an AI-powered framework in cross-checking male professional football anterior cruciate ligament tear reports in public databases.
Purpose: While public databases like Transfermarkt provide valuable data for assessing the impact of anterior cruciate ligament (ACL) injuries in professional footballers, they require robust verification methods due to accuracy concerns. We hypothesised that an artificial intelligence (AI)-powered framework could cross-check ACL tear-related information from large publicly available data sets with high specificity.
Methods: The AI-powered framework uses Google Programmable Search Engine to search a curated, multilingual list of websites and OpenAI's GPT to translate search queries, appraise search results and analyse injury-related information in search result items (SRIs). Specificity was the chosen performance metric-the AI-powered framework's ability to accurately identify texts that do not mention an athlete suffering an ACL tear-with SRI as the evaluation unit. A database of ACL tears in male professional footballers from first- and second-tier leagues worldwide (1999-2024) was collected from Transfermarkt.com, and players were randomly selected for appraisal until enough SRIs were obtained to validate the framework's specificity. Player age at injury and time until return-to-play (RTP) were recorded and compared with Union of European Football Associations (UEFA) Elite Club Injury Study data.
Results: Verification of 231 athletes yielded 1546 SRIs. Human analysis of the SRIs showed that 335 mentioned an ACL tear, corresponding to 83 athletes with ACL tears. Specificity and sensitivity of GPT in identifying mentions of ACL tears in a player were 99.3% and 88.4%, respectively. Mean age at rupture was 26.6 years (standard deviation: 4.6, 95% confidence interval [CI]: 25.6-27.6). Median RTP time was 225 days (interquartile range: 96, 95% CI: 209-251), which is comparable to reports using data from the UEFA Elite Club Injury Study.
Conclusion: This study shows that an AI-powered framework can achieve high specificity in cross-checking ACL tear reports in male professional football from public databases, markedly reducing manual workload and enhancing the reliability of media-based sports medicine research.
期刊介绍：
Few other areas of orthopedic surgery and traumatology have undergone such a dramatic evolution in the last 10 years as knee surgery, arthroscopy and sports traumatology. Ranked among the top 33% of journals in both Orthopedics and Sports Sciences, the goal of this European journal is to publish papers about innovative knee surgery, sports trauma surgery and arthroscopy. Each issue features a series of peer-reviewed articles that deal with diagnosis and management and with basic research. Each issue also contains at least one review article about an important clinical problem. Case presentations or short notes about technical innovations are also accepted for publication.
The articles cover all aspects of knee surgery and all types of sports trauma; in addition, epidemiology, diagnosis, treatment and prevention, and all types of arthroscopy (not only the knee but also the shoulder, elbow, wrist, hip, ankle, etc.) are addressed. Articles on new diagnostic techniques such as MRI and ultrasound and high-quality articles about the biomechanics of joints, muscles and tendons are included. Although this is largely a clinical journal, it is also open to basic research with clinical relevance.
Because the journal is supported by a distinguished European Editorial Board, assisted by an international Advisory Board, you can be assured that the journal maintains the highest standards.
Official Clinical Journal of the European Society of Sports Traumatology, Knee Surgery and Arthroscopy (ESSKA).
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