Diniz Pedro, Grimm Bernd, Mouton Caroline, Ley Christophe, Andersen Thor Einar, Seil Romain
Department of Orthopaedic Surgery, Centre Hospitalier de Luxembourg - Clinique d'Eich, Luxembourg, Luxembourg.
Luxembourg Institute of Research in Orthopaedics, Sports Medicine and Science (LIROMS), Luxembourg, Luxembourg.
Knee Surg Sports Traumatol Arthrosc. 2024 Dec 26. doi: 10.1002/ksa.12571.
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.
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.
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.
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.
Level III.
虽然像转会市场这样的公共数据库为评估前交叉韧带（ACL）损伤对职业足球运动员的影响提供了有价值的数据，但由于准确性问题，它们需要强大的验证方法。我们假设一个由人工智能（AI）驱动的框架可以高度特异性地交叉核对来自大型公开数据集的与ACL撕裂相关的信息。
由人工智能驱动的框架使用谷歌可编程搜索引擎搜索精心策划的多语言网站列表，并使用OpenAI的GPT来翻译搜索查询、评估搜索结果并分析搜索结果项（SRI）中与损伤相关的信息。特异性是所选的性能指标——以SRI为评估单位，该由人工智能驱动的框架准确识别未提及运动员ACL撕裂的文本的能力。从Transfermarkt.com收集了全球一线和二线联赛男性职业足球运动员ACL撕裂的数据库（1999 - 2024年），并随机选择球员进行评估，直到获得足够的SRI来验证框架的特异性。记录受伤时的球员年龄和恢复比赛（RTP）所需时间，并与欧洲足球协会联盟（UEFA）精英俱乐部损伤研究数据进行比较。
对231名运动员的验证产生了1546个SRI。对SRI的人工分析表明，其中335个提到了ACL撕裂，对应83名ACL撕裂的运动员。GPT识别球员中ACL撕裂提及的特异性和敏感性分别为99.3%和88.4%。破裂时的平均年龄为26.6岁（标准差：4.6，95%置信区间[CI]：25.6 - 27.6）。RTP时间中位数为225天（四分位间距：96，95%CI：209 - 251），这与使用UEFA精英俱乐部损伤研究数据的报告结果相当。
本研究表明，一个由人工智能驱动的框架在交叉核对公共数据库中男性职业足球中ACL撕裂报告时可以实现高度特异性，显著减少人工工作量并提高基于媒体的运动医学研究的可靠性。
三级。
