Department of Dermatology, Venereology and Leprosy, Shri B M Patil Medical College, Hospital and Research Centre, BLDE (Deemed to be) University, Vijayapura, Karnataka, India.
JMIR Dermatol. 2024 Jul 2;7:e48811. doi: 10.2196/48811.
BACKGROUND: Dermatology is an ideal specialty for artificial intelligence (AI)-driven image recognition to improve diagnostic accuracy and patient care. Lack of dermatologists in many parts of the world and the high frequency of cutaneous disorders and malignancies highlight the increasing need for AI-aided diagnosis. Although AI-based applications for the identification of dermatological conditions are widely available, research assessing their reliability and accuracy is lacking. OBJECTIVE: The aim of this study was to analyze the efficacy of the Aysa AI app as a preliminary diagnostic tool for various dermatological conditions in a semiurban town in India. METHODS: This observational cross-sectional study included patients over the age of 2 years who visited the dermatology clinic. Images of lesions from individuals with various skin disorders were uploaded to the app after obtaining informed consent. The app was used to make a patient profile, identify lesion morphology, plot the location on a human model, and answer questions regarding duration and symptoms. The app presented eight differential diagnoses, which were compared with the clinical diagnosis. The model's performance was evaluated using sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and F-score. Comparison of categorical variables was performed with the χ test and statistical significance was considered at P<.05. RESULTS: A total of 700 patients were part of the study. A wide variety of skin conditions were grouped into 12 categories. The AI model had a mean top-1 sensitivity of 71% (95% CI 61.5%-74.3%), top-3 sensitivity of 86.1% (95% CI 83.4%-88.6%), and all-8 sensitivity of 95.1% (95% CI 93.3%-96.6%). The top-1 sensitivities for diagnosis of skin infestations, disorders of keratinization, other inflammatory conditions, and bacterial infections were 85.7%, 85.7%, 82.7%, and 81.8%, respectively. In the case of photodermatoses and malignant tumors, the top-1 sensitivities were 33.3% and 10%, respectively. Each category had a strong correlation between the clinical diagnosis and the probable diagnoses (P<.001). CONCLUSIONS: The Aysa app showed promising results in identifying most dermatoses.
背景：皮肤病学是人工智能（AI）驱动的图像识别改善诊断准确性和患者护理的理想专业。世界上许多地区缺乏皮肤科医生，以及皮肤疾病和恶性肿瘤的高频率，突出了对 AI 辅助诊断的日益增长的需求。尽管用于识别皮肤病的基于 AI 的应用程序已经广泛可用，但缺乏对其可靠性和准确性的研究评估。 目的：本研究旨在分析 Aysa AI 应用程序作为印度一个半城市小镇各种皮肤病初步诊断工具的功效。 方法：这项观察性横断面研究纳入了年龄在 2 岁以上的皮肤科诊所就诊患者。在获得知情同意后，将患有各种皮肤疾病的个体的病变图像上传到应用程序。该应用程序用于制作患者档案，识别病变形态，在人体模型上绘制位置，并回答有关持续时间和症状的问题。该应用程序提供了 8 种鉴别诊断，将其与临床诊断进行比较。使用敏感性、特异性、准确性、阳性预测值、阴性预测值和 F 分数评估模型性能。使用 χ 检验比较分类变量，P<.05 时认为具有统计学意义。 结果：共有 700 名患者参与了这项研究。广泛的皮肤疾病分为 12 类。AI 模型的平均 top-1 敏感性为 71%（95%CI 61.5%-74.3%），top-3 敏感性为 86.1%（95%CI 83.4%-88.6%），所有 8 种敏感性为 95.1%（95%CI 93.3%-96.6%）。皮肤寄生虫病、角化障碍、其他炎症性疾病和细菌感染的 top-1 敏感性分别为 85.7%、85.7%、82.7%和 81.8%。光皮病和恶性肿瘤的 top-1 敏感性分别为 33.3%和 10%。每个类别中，临床诊断与可能的诊断之间都具有很强的相关性（P<.001）。 结论：Aysa 应用程序在识别大多数皮肤病方面显示出有希望的结果。
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