皮肤真菌感染是最常见的皮肤病之一,因此,用高碘酸-希夫(PAS)和戈森亚甲基胺银(GMS)染色进行显微镜检查时确定真菌元素的负担,非常耗时。尽管一些形态变异性对训练基于人工智能(AI)的解决方案提出了挑战,这些结构是有利的潜在目标,能够招募有前途的基于人工智能的技术。在这里,我们提出了一种用于识别皮肤真菌感染的新型AI解决方案,可能为病理学家提供决策支持系统。在舍巴医疗中心诊断为皮肤真菌感染的患者的皮肤活检,在2014年至2023年之间使用了以色列。样品用PAS和GMS染色,并用PhilipsIntelliSite扫描仪数字化。DeePathology®STUDIO真菌元素进行了注释,并在两名专业病理学家进行了全面修订后将其视为地面实况数据。随后,他们被用来创建一个基于人工智能的解决方案,这在其他利益领域得到了进一步验证。研究参与者被分为两组。在第一个队列中,该算法的总体灵敏度为0.8,特异性为0.97,F1评分为0.78;其次,该算法的总体敏感性为0.93,特异性为0.99,F1评分为0.95.作为基于AI的真菌检测算法的概念证明,获得的结果令人鼓舞。DeePathology®STUDIO可用作病理学家使用PAS和GMS染色诊断皮肤真菌感染时的决策支持系统,因此,节省时间和金钱。
Cutaneous fungal infections are one of the most common skin conditions, hence, the burden of determining fungal elements upon microscopic examination with periodic acid-Schiff (PAS) and Gomori methenamine silver (GMS) stains, is very time consuming. Despite some morphological variability posing challenges to training artificial intelligence (AI)-based solutions, these structures are favored potential targets, enabling the recruitment of promising AI-based technologies. Herein, we present a novel AI solution for identifying skin fungal infections, potentially providing a decision support system for pathologists. Skin biopsies of patients diagnosed with a cutaneous fungal infection at the Sheba Medical Center, Israel between 2014 and 2023, were used. Samples were stained with PAS and GMS and digitized by the Philips IntelliSite scanner. DeePathology® STUDIO fungal elements were annotated and deemed as ground truth data after an overall revision by two specialist pathologists. Subsequently, they were used to create an AI-based solution, which has been further validated in other regions of interests. The study participants were divided into two cohorts. In the first cohort, the overall sensitivity of the algorithm was 0.8, specificity 0.97, F1 score 0.78; in the second, the overall sensitivity of the algorithm was 0.93, specificity 0.99, F1 score 0.95. The results obtained are encouraging as proof of concept for an AI-based fungi detection algorithm. DeePathology® STUDIO can be employed as a decision support system for pathologists when diagnosing a cutaneous fungal infection using PAS and GMS stains, thereby, saving time and money.