Artificial Intelligence Applied to Non-Invasive Imaging Modalities in Identification of Nonmelanoma Skin Cancer: A Systematic Review

• Published in: Cancers Vol. 16; no. 3; p. 629
• Main Authors: Foltz, Emilie A., Witkowski, Alexander, Becker, Alyssa L., Latour, Emile, Lim, Jeong Youn, Hamilton, Andrew, Ludzik, Joanna
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.02.2024
• Subjects: Accuracy; Algorithms; Artificial intelligence; Biopsy; Cancer; Comparative analysis; Computational linguistics; Computers; Confocal microscopy; Datasets; Deep learning; Dermatology; Design; Diagnosis; Image processing; Invasiveness; Language processing; Literature reviews; Machine learning; Melanoma; Microscopy; Natural language interfaces; Neural networks; Oncology, Experimental; Reflectance; Reviews; Risk assessment; Sensitivity analysis; Skin cancer; Skin tests; Squamous cell carcinoma; Tomography; reflectance confocal microscopy; nonmelanoma skin cancer; squamous-cell carcinoma; early detection; machine learning; non-invasive imaging; artificial intelligence; basal-cell carcinoma; dermoscopy
• ISSN: 2072-6694; 2072-6694
• DOI: 10.3390/cancers16030629

Background: The objective of this study is to systematically analyze the current state of the literature regarding novel artificial intelligence (AI) machine learning models utilized in non-invasive imaging for the early detection of nonmelanoma skin cancers. Furthermore, we aimed to assess their potential clinical relevance by evaluating the accuracy, sensitivity, and specificity of each algorithm and assessing for the risk of bias. Methods: Two reviewers screened the MEDLINE, Cochrane, PubMed, and Embase databases for peer-reviewed studies that focused on AI-based skin cancer classification involving nonmelanoma skin cancers and were published between 2018 and 2023. The search terms included skin neoplasms, nonmelanoma, basal-cell carcinoma, squamous-cell carcinoma, diagnostic techniques and procedures, artificial intelligence, algorithms, computer systems, dermoscopy, reflectance confocal microscopy, and optical coherence tomography. Based on the search results, only studies that directly answered the review objectives were included and the efficacy measures for each were recorded. A QUADAS-2 risk assessment for bias in included studies was then conducted. Results: A total of 44 studies were included in our review; 40 utilizing dermoscopy, 3 using reflectance confocal microscopy (RCM), and 1 for hyperspectral epidermal imaging (HEI). The average accuracy of AI algorithms applied to all imaging modalities combined was 86.80%, with the same average for dermoscopy. Only one of the three studies applying AI to RCM measured accuracy, with a result of 87%. Accuracy was not measured in regard to AI based HEI interpretation. Conclusion: AI algorithms exhibited an overall favorable performance in the diagnosis of nonmelanoma skin cancer via noninvasive imaging techniques. Ultimately, further research is needed to isolate pooled diagnostic accuracy for nonmelanoma skin cancers as many testing datasets also include melanoma and other pigmented lesions.