Model Dermatol – 皮肤病，皮肤癌

IDerma

关于此应用

人工智能会扫描提供的照片，立即帮助识别您的皮肤问题。AI 还提供有关皮肤病（如皮疹、疣、荨麻疹）和皮肤癌（如黑色素瘤）的相关医学信息。

cf) 如果您无法从中国大陆访问，请通过 https://modelderm.com 使用网页版。

◉ 拍摄皮肤照片并提交。裁剪后的图像会被传输，但我们不会存储您的数据。
◉ 人工智能提供描述皮肤病和皮肤癌（如黑色素瘤）相关体征和症状的网站链接。
◉ 该算法可以对186种皮肤病的图像进行分类，包括常见的皮肤病类型（如特应性皮炎、荨麻疹、湿疹、牛皮癣、痤疮、酒渣鼻、甲癣、黑色素瘤、痣）。
◉ 算法使用免费，支持共104种语言。

🞹 出版物
我们使用“Model Dermatology”算法。分类器的性能已发表在几家著名的医学期刊上。
- Assessment of Deep Neural Networks for the Diagnosis of Benign and Malignant Skin Neoplasms in Comparison with Dermatologists: A Retrospective Validation Study. PLOS Medicine, 2020
- Performance of a deep neural network in teledermatology: a single center prospective diagnostic study. J Eur Acad Dermatol Venereol. 2020
- Keratinocytic Skin Cancer Detection on the Face using Region-based Convolutional Neural Network. JAMA Dermatol. 2019
- Seems to be low, but is it really poor? : Need for Cohort and Comparative studies to Clarify Performance of Deep Neural Networks. J Invest Dermatol. 2020
- Multiclass Artificial Intelligence in Dermatology: Progress but Still Room for Improvement. J Invest Dermatol. 2020
- Augment Intelligence Dermatology : Deep Neural Networks Empower Medical Professionals in Diagnosing Skin Cancer and Predicting Treatment Options for 134 Skin Disorders. J Invest Dermatol. 2020
- Interpretation of the Outputs of Deep Learning Model trained with Skin Cancer Dataset. J Invest Dermatol. 2018
- Automated Dermatological Diagnosis: Hype or Reality? J Invest Dermatol. 2018
- Classification of the Clinical Images for Benign and Malignant Cutaneous Tumors Using a Deep Learning Algorithm. J Invest Dermatol. 2018
- Augmenting the Accuracy of Trainee Doctors in Diagnosing Skin Lesions Suspected of Skin Neoplasms in a Real-World Setting: A Prospective Controlled Before and After Study. PLOS One, 2022
- Evaluation of Artificial Intelligence-assisted Diagnosis of Skin Neoplasms – a single-center, paralleled, unmasked, randomized controlled trial. J Invest Dermatol. 2022

🞹 免责声明
- 除了使用此应用程序之外，在做出任何医疗决定之前，请寻求医生的建议。
- 仅根据临床图像诊断皮肤癌或皮肤病可能会漏掉多达 10% 的病例。因此，此应用程序不能替代标准护理（亲自检查）。
- 该算法的预测不是皮肤癌或皮肤病的最终诊断。仅供参考，提供个性化医疗信息

评分和评价

好好用

好