Improving Skin cancer Management with ARTificial Intelligence (SMARTI): protocol for a preintervention/postintervention trial of an artificial intelligence system used as a diagnostic aid for skin cancer management in a specialist dermatology setting.

INTRODUCTION: 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. METHODS AND ANALYSIS: 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. ETHICS AND DISSEMINATION: 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. TRIAL REGISTRATION NUMBER: NCT04040114.

Real-world outcomes of melanoma surveillance using the MoleMap NZ telemedicine platform.

BACKGROUND: MoleMap NZ is a novel New Zealand-based store-and-forward telemedicine service to detect melanoma. It uses expert review of total body photography and close-up and dermoscopic images of skin lesions that are suspicious for malignancy. OBJECTIVE: The purpose of this study was to assess the effectiveness of MoleMap NZ as a melanoma early detection program. METHODS: We conducted a review of 2108 melanocytic lesions recommended for biopsy/excision by MoleMap NZ dermoscopists between January 2015 and December 2016. RESULTS: Pathologic diagnoses were available for 1571 lesions. Of these, 1303 (83%) lesions were benign and 260 (17%) lesions were diagnosed as melanoma, for a melanoma-specific benign:malignant ratio of 5.0:1. The number needed to biopsy to obtain 1 melanoma was 6. Among melanomas with available tumor thickness data (n = 137), 92% were <0.8 mm (range in situ to 3.1 mm), with in situ melanomas comprising 74%. LIMITATIONS: Only lesions recommended for excision were analyzed. Pathology results were available for 75% of these cases. Tumor thickness data were available for 53% of melanomas diagnosed. CONCLUSIONS: This real-world study of MoleMap NZ, a community-based teledermoscopy program, suggests that it has the potential to increase patients' access to specialist expertise via telemedicine. Additional studies are needed to more accurately define its efficacy.