School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
Victorian Melanoma Service, Alfred Health, Melbourne, Victoria, Australia.
BMJ Open. 2022 Jan 4;12(1):e050203. doi: 10.1136/bmjopen-2021-050203.
Convolutional neural networks (CNNs) can diagnose skin cancers with impressive accuracy in experimental settings, however, their performance in the real-world clinical setting, including comparison to teledermatology services, has not been validated in prospective clinical studies.
Participants will be recruited from dermatology clinics at the Alfred Hospital and Skin Health Institute, Melbourne. Skin lesions will be imaged using a proprietary dermoscopic camera. The artificial intelligence (AI) algorithm, a CNN developed by MoleMap Ltd and Monash eResearch, classifies lesions as benign, malignant or uncertain. This is a preintervention/postintervention study. In the preintervention period, treating doctors are blinded to AI lesion assessment. In the postintervention period, treating doctors review the AI lesion assessment in real time, and have the opportunity to then change their diagnosis and management. Any skin lesions of concern and at least two benign lesions will be selected for imaging. Each participant's lesions will be examined by a registrar, the treating consultant dermatologist and later by a teledermatologist. At the conclusion of the preintervention period, the safety of the AI algorithm will be evaluated in a primary analysis by measuring its sensitivity, specificity and agreement with histopathology where available, or the treating consultant dermatologists' classification. At trial completion, AI classifications will be compared with those of the teledermatologist, registrar, treating dermatologist and histopathology. The impact of the AI algorithm on diagnostic and management decisions will be evaluated by: (1) comparing the initial management decision of the registrar with their AI-assisted decision and (2) comparing the benign to malignant ratio (for lesions biopsied) between the preintervention and postintervention periods.
Human Research Ethics Committee (HREC) approval received from the Alfred Hospital Ethics Committee on 14 February 2019 (HREC/48865/Alfred-2018). Findings from this study will be disseminated through peer-reviewed publications, non-peer reviewed media and conferences.
NCT04040114.
卷积神经网络 (CNN) 在实验环境中可以准确诊断皮肤癌，然而，它们在真实临床环境中的性能，包括与远程皮肤病学服务的比较，尚未在前瞻性临床研究中得到验证。
参与者将从墨尔本阿尔弗雷德医院和皮肤健康研究所的皮肤科诊所招募。皮肤病变将使用专有的皮肤镜相机进行成像。人工智能 (AI) 算法，一种由 MoleMap Ltd 和莫纳什大学电子研究中心开发的卷积神经网络，将病变分类为良性、恶性或不确定。这是一项干预前/干预后研究。在干预前阶段，治疗医生对 AI 病变评估是盲目的。在干预后阶段，治疗医生实时查看 AI 病变评估，并有机会更改他们的诊断和管理。任何有问题的皮肤病变和至少两个良性病变将被选择进行成像。每个参与者的病变将由一名住院医师、治疗顾问皮肤科医生和后来的远程皮肤科医生进行检查。在干预前阶段结束时，将通过测量其在有组织病理学的情况下的敏感性、特异性和一致性，或者通过治疗顾问皮肤科医生的分类，对 AI 算法的安全性进行主要分析评估。在试验完成时，将比较 AI 分类与远程皮肤科医生、住院医师、治疗皮肤科医生和组织病理学的分类。通过以下方法评估 AI 算法对诊断和管理决策的影响：(1) 比较住院医师的初始管理决策与其 AI 辅助决策，(2) 比较干预前和干预后期间活检病变的良性到恶性比例。
2019 年 2 月 14 日获得阿尔弗雷德医院伦理委员会的人类研究伦理委员会 (HREC) 批准（HREC/48865/Alfred-2018）。该研究的结果将通过同行评议的出版物、非同行评议的媒体和会议进行传播。
NCT04040114。
