Cargando…
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. MET...
Autores principales: | , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8728443/ https://www.ncbi.nlm.nih.gov/pubmed/34983756 http://dx.doi.org/10.1136/bmjopen-2021-050203 |
_version_ | 1784626735409528832 |
---|---|
author | Felmingham, Claire MacNamara, Samantha Cranwell, William Williams, Narelle Wada, Miki Adler, Nikki R Ge, Zongyuan Sharfe, Alastair Bowling, Adrian Haskett, Martin Wolfe, Rory Mar, Victoria |
author_facet | Felmingham, Claire MacNamara, Samantha Cranwell, William Williams, Narelle Wada, Miki Adler, Nikki R Ge, Zongyuan Sharfe, Alastair Bowling, Adrian Haskett, Martin Wolfe, Rory Mar, Victoria |
author_sort | Felmingham, Claire |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8728443 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-87284432022-01-18 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 Felmingham, Claire MacNamara, Samantha Cranwell, William Williams, Narelle Wada, Miki Adler, Nikki R Ge, Zongyuan Sharfe, Alastair Bowling, Adrian Haskett, Martin Wolfe, Rory Mar, Victoria BMJ Open Dermatology 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. BMJ Publishing Group 2022-01-04 /pmc/articles/PMC8728443/ /pubmed/34983756 http://dx.doi.org/10.1136/bmjopen-2021-050203 Text en © Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) . |
spellingShingle | Dermatology Felmingham, Claire MacNamara, Samantha Cranwell, William Williams, Narelle Wada, Miki Adler, Nikki R Ge, Zongyuan Sharfe, Alastair Bowling, Adrian Haskett, Martin Wolfe, Rory Mar, Victoria 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 |
title | 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 |
title_full | 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 |
title_fullStr | 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 |
title_full_unstemmed | 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 |
title_short | 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 |
title_sort | 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 |
topic | Dermatology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8728443/ https://www.ncbi.nlm.nih.gov/pubmed/34983756 http://dx.doi.org/10.1136/bmjopen-2021-050203 |
work_keys_str_mv | AT felminghamclaire improvingskincancermanagementwithartificialintelligencesmartiprotocolforapreinterventionpostinterventiontrialofanartificialintelligencesystemusedasadiagnosticaidforskincancermanagementinaspecialistdermatologysetting AT macnamarasamantha improvingskincancermanagementwithartificialintelligencesmartiprotocolforapreinterventionpostinterventiontrialofanartificialintelligencesystemusedasadiagnosticaidforskincancermanagementinaspecialistdermatologysetting AT cranwellwilliam improvingskincancermanagementwithartificialintelligencesmartiprotocolforapreinterventionpostinterventiontrialofanartificialintelligencesystemusedasadiagnosticaidforskincancermanagementinaspecialistdermatologysetting AT williamsnarelle improvingskincancermanagementwithartificialintelligencesmartiprotocolforapreinterventionpostinterventiontrialofanartificialintelligencesystemusedasadiagnosticaidforskincancermanagementinaspecialistdermatologysetting AT wadamiki improvingskincancermanagementwithartificialintelligencesmartiprotocolforapreinterventionpostinterventiontrialofanartificialintelligencesystemusedasadiagnosticaidforskincancermanagementinaspecialistdermatologysetting AT adlernikkir improvingskincancermanagementwithartificialintelligencesmartiprotocolforapreinterventionpostinterventiontrialofanartificialintelligencesystemusedasadiagnosticaidforskincancermanagementinaspecialistdermatologysetting AT gezongyuan improvingskincancermanagementwithartificialintelligencesmartiprotocolforapreinterventionpostinterventiontrialofanartificialintelligencesystemusedasadiagnosticaidforskincancermanagementinaspecialistdermatologysetting AT sharfealastair improvingskincancermanagementwithartificialintelligencesmartiprotocolforapreinterventionpostinterventiontrialofanartificialintelligencesystemusedasadiagnosticaidforskincancermanagementinaspecialistdermatologysetting AT bowlingadrian improvingskincancermanagementwithartificialintelligencesmartiprotocolforapreinterventionpostinterventiontrialofanartificialintelligencesystemusedasadiagnosticaidforskincancermanagementinaspecialistdermatologysetting AT haskettmartin improvingskincancermanagementwithartificialintelligencesmartiprotocolforapreinterventionpostinterventiontrialofanartificialintelligencesystemusedasadiagnosticaidforskincancermanagementinaspecialistdermatologysetting AT wolferory improvingskincancermanagementwithartificialintelligencesmartiprotocolforapreinterventionpostinterventiontrialofanartificialintelligencesystemusedasadiagnosticaidforskincancermanagementinaspecialistdermatologysetting AT marvictoria improvingskincancermanagementwithartificialintelligencesmartiprotocolforapreinterventionpostinterventiontrialofanartificialintelligencesystemusedasadiagnosticaidforskincancermanagementinaspecialistdermatologysetting |