TY - JOUR
T1 - Improving Skin cancer Management with ARTificial Intelligence (SMARTI)
T2 - 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
AU - Felmingham, Claire
AU - MacNamara, Samantha
AU - Cranwell, William
AU - Williams, Narelle
AU - Wada, Miki
AU - Adler, Nikki R.
AU - Ge, Zongyuan
AU - Sharfe, Alastair
AU - Bowling, Adrian
AU - Haskett, Martin
AU - Wolfe, Rory
AU - Mar, Victoria
N1 - Funding Information:
Funding The research is funded by the Victorian Medical Research Acceleration Fund, Department of Health and Human Services, State Government of Victoria and MoleMap Ltd.
Funding Information:
Competing interests VM is supported by an NHMRC Early Career Fellowship. VM reports personal fees from Novartis, personal fees from Bristol-Myers-Squibb, personal fees from Merck, outside the submitted work. MH reports personal fees from MoleMap Ltd, during the conduct of the study; and is a shareholder in MoleMap Ltd. AB reports personal fees from MoleMap Ltd, during the conduct of the study; personal fees from Molemap Ltd, outside the submitted work; and is a shareholder in Molemap Ltd. AS reports personal fees from MoleMap Ltd, during the conduct of the study; personal fees from Molemap Ltd, outside the submitted work. ZG reports personal fees from MoleMap Ltd. NW and SM are former employees of the Cancer Collaborative Trials Group contracted to implement the SMARTI Study—Melanoma and Skin Cancer Trials (MASC Trials) Ltd. CF is supported by a Monash University Research Training Program Scholarship. RW, NA, WC and MW have nothing to disclose. The study is sponsored by Monash University and endorsed by MASC Trials Ltd.
Publisher Copyright:
© 2022 BMJ Publishing Group. All rights reserved.
PY - 2022/1/4
Y1 - 2022/1/4
N2 - 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.
AB - 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.
KW - adult dermatology
KW - dermatological tumours
KW - dermatology
UR - http://www.scopus.com/inward/record.url?scp=85122750261&partnerID=8YFLogxK
U2 - 10.1136/bmjopen-2021-050203
DO - 10.1136/bmjopen-2021-050203
M3 - Article
C2 - 34983756
AN - SCOPUS:85122750261
SN - 2044-6055
VL - 12
JO - BMJ Open
JF - BMJ Open
IS - 1
M1 - e050203
ER -