Abstract：
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 F1-score. Comparison of categorical variables was performed with the χ2 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.
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 F1-score. Comparison of categorical variables was performed with the χ2 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.
摘要：
背景：皮肤病学是人工智能（AI）驱动的图像识别的理想专业，可提高诊断准确性和患者护理。世界上许多地方缺乏皮肤科医生，皮肤疾病和恶性肿瘤的发生率很高，这凸显了对AI辅助诊断的需求日益增加。尽管基于AI的皮肤病识别应用广泛可用，缺乏评估其可靠性和准确性的研究。
目的：本研究的目的是分析AysaAI应用程序作为印度半城市城镇各种皮肤病的初步诊断工具的功效。
方法：这项观察性横断面研究包括2岁以上到皮肤科就诊的患者。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。这款应用是用来做病人档案的，确定病变形态，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。该应用程序提供了八种鉴别诊断，将其与临床诊断进行比较。使用灵敏度评估模型的性能，特异性，准确度,正预测值，负预测值，和F1得分。分类变量的比较采用χ2检验，P<0.05时具有统计学意义。
结果：总共700名患者是研究的一部分。各种各样的皮肤状况被分为12类。AI模型的平均top-1敏感度为71%(95%CI61.5%-74.3%)，前3名敏感性为86.1%（95%CI83.4%-88.6%），和所有-8灵敏度为95.1%（95%CI93.3%-96.6%）。诊断皮肤感染的前1名敏感性，角质化疾病，其他炎症，细菌感染占85.7%,85.7%,82.7%,和81.8%，分别。在光皮肤病和恶性肿瘤的情况下，前1名的敏感度分别为33.3%和10%，分别。每个类别在临床诊断和可能诊断之间都有很强的相关性（P<.001）。
结论：Aysa应用程序在识别大多数皮肤病方面显示出可喜的结果。
目的：本研究的目的是分析AysaAI应用程序作为印度半城市城镇各种皮肤病的初步诊断工具的功效。
方法：这项观察性横断面研究包括2岁以上到皮肤科就诊的患者。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。这款应用是用来做病人档案的，确定病变形态，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。该应用程序提供了八种鉴别诊断，将其与临床诊断进行比较。使用灵敏度评估模型的性能，特异性，准确度,正预测值，负预测值，和F1得分。分类变量的比较采用χ2检验，P<0.05时具有统计学意义。
结果：总共700名患者是研究的一部分。各种各样的皮肤状况被分为12类。AI模型的平均top-1敏感度为71%(95%CI61.5%-74.3%)，前3名敏感性为86.1%（95%CI83.4%-88.6%），和所有-8灵敏度为95.1%（95%CI93.3%-96.6%）。诊断皮肤感染的前1名敏感性，角质化疾病，其他炎症，细菌感染占85.7%,85.7%,82.7%,和81.8%，分别。在光皮肤病和恶性肿瘤的情况下，前1名的敏感度分别为33.3%和10%，分别。每个类别在临床诊断和可能诊断之间都有很强的相关性（P<.001）。
结论：Aysa应用程序在识别大多数皮肤病方面显示出可喜的结果。
