Artificial intelligence for melanoma diagnosis: how can we deliver on the promise?

摘要

Currently, diagnostic accuracy for melanoma is dependent on the experience and training of the treating doctor. In this issue of the Annals of Oncology, Haenssle et al. [1.Haenssle H.A. Fink C. Schneiderbauer R. et al.Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists.Ann Oncol. 2018; 29: 1836-1842Abstract Full Text Full Text PDF PubMed Scopus (663) Google Scholar] have shown that a computer algorithm using convolutional neural networks outperformed the majority of 58 dermatologists tested in accurately diagnosing melanoma, with a median area under the receiver operating characteristic curve of 0.86 compared with 0.79, P < 0.01. This, together with the recent landmark work by Esteva et al. [2.Esteva A. Kuprel B. Novoa R.A. et al.Dermatologist-level classification of skin cancer with deep neural networks.Nature. 2017; 542: 115-118Crossref PubMed Scopus (54) Google Scholar] shows that artificial intelligence (AI) promises a more standardised level of diagnostic accuracy, such that all people, regardless of where they live or which doctor they see, will be able to access reliable diagnostic assessment. The downstream effects of improved diagnostic accuracy are tangible: earlier diagnosis, more appropriate referrals to specialists, fewer unnecessary procedures and therefore less morbidity and better outcomes for patients at lower cost to the healthcare system. So can we deliver on this promise in the real-world setting and what are the potential barriers? It is important to consider how this technology might be used and in what setting. First, consider the function of AI as a screening or triage tool to ensure timely access for people requiring more urgent attention. There is a lack of evidence that population-based screening programs for melanoma are effective, and they may be harmful due to high false positive rates and overdiagnosis [3.Bibbins-Domingo K. Grossman D.C. Curry S.J. Force USPST et al.Screening for skin cancer: US Preventive Services Task Force Recommendation Statement.JAMA. 2016; 316: 429-435Crossref PubMed Scopus (175) Google Scholar]. Targeted surveillance of patients at high risk, however, has been shown to be effective at detecting melanomas early with lower associated cost [4.Haenssle H.A. Vente C. Bertsch H.P. et al.Results of a surveillance programme for patients at high risk of malignant melanoma using digital and conventional dermoscopy.Eur J Cancer Prev. 2004; 13: 133-138Crossref PubMed Scopus (51) Google Scholar, 5.Salerni G. Carrera C. Lovatto L. et al.Benefits of total body photography and digital dermatoscopy (“two-step method of digital follow-up”) in the early diagnosis of melanoma in patients at high risk for melanoma.J Am Acad Dermatol. 2012; 67: e17-e27Abstract Full Text Full Text PDF PubMed Scopus (140) Google Scholar, 6.Watts C.G. Cust A.E. Menzies S.W. et al.Cost-effectiveness of skin surveillance through a specialized clinic for patients at high risk of melanoma.J Clin Oncol. 2017; 35: 63-71Crossref PubMed Scopus (52) Google Scholar]. For high-risk patients requiring total body photography and sequential dermoscopic imaging, changing lesions could be flagged automatically by AI systems integrated into cutting edge two-dimensional (2D) or 3D imaging technology [7.Rayner J.E. Laino A.M. Nufer K.L. et al.Clinical perspective of 3D total body photography for early detection and screening.Front Med. 2018; Crossref Scopus (37) Google Scholar] (Figure 1). Automated filtering of benign lesions would allow more efficient management of suspicious lesions and improve access to specialists. Unfortunately, people in rural areas have limited access to diagnostic services which can adversely affect disease outcomes [9.Cramb S.M. Moraga P. Mengersen K.L. Baade P.D. Spatial variation in cancer incidence and survival over time across Queensland, Australia.Spat Spatiotemporal Epidemiol. 2017; 23: 59-