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      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

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          Abstract

          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.

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          Most cited references31

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          The Measurement of Observer Agreement for Categorical Data

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            Dermatologist-level classification of skin cancer with deep neural networks

            Skin cancer, the most common human malignancy, is primarily diagnosed visually, beginning with an initial clinical screening and followed potentially by dermoscopic analysis, a biopsy and histopathological examination. Automated classification of skin lesions using images is a challenging task owing to the fine-grained variability in the appearance of skin lesions. Deep convolutional neural networks (CNNs) show potential for general and highly variable tasks across many fine-grained object categories. Here we demonstrate classification of skin lesions using a single CNN, trained end-to-end from images directly, using only pixels and disease labels as inputs. We train a CNN using a dataset of 129,450 clinical images—two orders of magnitude larger than previous datasets—consisting of 2,032 different diseases. We test its performance against 21 board-certified dermatologists on biopsy-proven clinical images with two critical binary classification use cases: keratinocyte carcinomas versus benign seborrheic keratoses; and malignant melanomas versus benign nevi. The first case represents the identification of the most common cancers, the second represents the identification of the deadliest skin cancer. The CNN achieves performance on par with all tested experts across both tasks, demonstrating an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists. Outfitted with deep neural networks, mobile devices can potentially extend the reach of dermatologists outside of the clinic. It is projected that 6.3 billion smartphone subscriptions will exist by the year 2021 (ref. 13) and can therefore potentially provide low-cost universal access to vital diagnostic care.
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              High-performance medicine: the convergence of human and artificial intelligence

              Eric Topol (2019)
              The use of artificial intelligence, and the deep-learning subtype in particular, has been enabled by the use of labeled big data, along with markedly enhanced computing power and cloud storage, across all sectors. In medicine, this is beginning to have an impact at three levels: for clinicians, predominantly via rapid, accurate image interpretation; for health systems, by improving workflow and the potential for reducing medical errors; and for patients, by enabling them to process their own data to promote health. The current limitations, including bias, privacy and security, and lack of transparency, along with the future directions of these applications will be discussed in this article. Over time, marked improvements in accuracy, productivity, and workflow will likely be actualized, but whether that will be used to improve the patient-doctor relationship or facilitate its erosion remains to be seen.
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                Author and article information

                Journal
                BMJ Open
                BMJ Open
                bmjopen
                bmjopen
                BMJ Open
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                2044-6055
                2022
                4 January 2022
                : 12
                : 1
                : e050203
                Affiliations
                [1 ]departmentSchool of Public Health and Preventive Medicine , Monash University , Melbourne, Victoria, Australia
                [2 ]departmentVictorian Melanoma Service , Alfred Health , Melbourne, Victoria, Australia
                [3 ]Melanoma and Skin Cancer Trials Ltd , Melbourne, Victoria, Australia
                [4 ]departmentMonash eResearch Centre , Monash University , Clayton, Victoria, Australia
                [5 ]departmentDepartment of Electrical and Computer Systems Engineering , Monash University Faculty of Engineering , Clayton, Victoria, Australia
                [6 ]MoleMap Ltd , Auckland, New Zealand
                [7 ]MoleMap Ltd , Melbourne, Victoria, Australia
                Author notes
                [Correspondence to ] Dr Claire Felmingham; clairefelmingham@ 123456gmail.com
                Author information
                http://orcid.org/0000-0002-3443-8065
                http://orcid.org/0000-0001-6368-5738
                http://orcid.org/0000-0002-6337-3619
                http://orcid.org/0000-0002-7972-9050
                http://orcid.org/0000-0002-5880-8673
                http://orcid.org/0000-0002-3357-5826
                http://orcid.org/0000-0002-2126-1045
                http://orcid.org/0000-0001-9423-3435
                Article
                bmjopen-2021-050203
                10.1136/bmjopen-2021-050203
                8728443
                34983756
                aec314f4-cc44-4afe-adb0-ecd99ee21b50
                © 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.

                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/.

                History
                : 28 February 2021
                : 13 December 2021
                Funding
                Funded by: MoleMap Ltd;
                Award ID: N/A
                Funded by: Victorian Medical Research Acceleration Fund, Department of Health and Human Services, State Government of Victoria;
                Award ID: N/A
                Categories
                Dermatology
                1506
                1687
                Protocol
                Custom metadata
                unlocked

                Medicine
                dermatology,dermatological tumours,adult dermatology
                Medicine
                dermatology, dermatological tumours, adult dermatology

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