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Opportunities and Challenges in Artificial Intelligence Skin Image Analyses Using Total Body Photography

In the last decade the application of artificial intelligence (AI) algorithms in dermatology to classify skin lesions, particularly melanoma, has advanced rapidly.

Large international computer skin image analysis challenges have successfully drawn attention to the potential for AI to aid the detection of skin cancers. A landmark study reported by Esteva et al1 in 2017, compared the accuracy of their algorithm against 16 expert Dermatologists. The algorithm outperformed the average dermatologist score for detecting both melanomas and keratinocyte carcinomas. Since then, there have been several other reports of algorithms that have outperformed dermatologists in reader studies.

While reported algorithm accuracy and performance are promising, they do not represent a real-world clinical setting, where examination and diagnosis involve context beyond the pixels of a single lesion image. Furthermore, the image datasets generally used for machine learning (ML) can lack generalisability to the day-to-day skin lesions examined by dermatologists, as image archives often capture skin lesions that were deemed ‘interesting enough’ to warrant a picture. In a clinical setting, a dermatologist would consider several factors relating to the patient’s clinical and family history, photodamage of the skin, naevi characteristics of the whole patient, age, and potentially genetic information when available.

The growing use of Total Body Photography (TBP) systems represents both challenges and opportunities to standardise both the image acquisition and labelling of clinical metadata for ML. The opportunity to train algorithms to not just consider the image pixels of a single lesion, but also phenotype information of the whole patient, along with medical records and genetic information will produce the next generation of AI algorithms for dermatology.

With funding from the European Union 2020 Horizons programme, the iToBoS consortium are developing an Intelligent Total Body Scanner with integrated Computer Aided Diagnostic (CAD) tools. As part of this project investigators have explored the expected challenges and opportunities of applying AI algorithms for Total Body Photography, and we have recently published our findings in a narrative review (available online here).2 

 

Reference:

  1. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017 Feb 2;542(7639):115-118. doi: 10.1038/nature21056. Epub 2017 Jan 25. Erratum in: Nature. 2017 Jun 28;546(7660):686. PMID: 28117445; PMCID: PMC8382232.

  2. Primiero CA, Rezze GG, Caffery LJ, et al. A Narrative Review: Opportunities and Challenges in Artificial Intelligence Skin Image Analyses Using Total Body Photography. J Invest Dermatol. 2024 Jan 16:S0022-202X(23)03123-8. doi: 10.1016/j.jid.2023.11.007. Epub ahead of print. PMID: 38231164.