Diagnosing onychomycosis: A step forward?

Abstract

There are a number of available methods for diagnosing onychomycosis, but more emerge as technology advances. This review briefly discusses the common diagnostic methods, the use of artificial intelligence (AI) as a diagnostic tool in dermatology as a whole, and then examines research on the use of AI for diagnosing onychomycosis. The studies discussed implemented convolutional neural networks (CNNs) to examine datasets of images of entire nails or histological images and then used the information learned from those datasets to make a diagnostic decision of onychomycosis or not. Results: It was found that, on average, AI were able to diagnose onychomycosis from the images provided at an equivalent level as human dermatologists. However, there are a number of clear limitations for using AI in this manner. The AI models implemented relied solely on images and therefore were limited by image quality. As only images were examined, other clinical data were not taken into consideration, which could be important to the diagnostic outcome. Conclusion: In conclusion, although AI can be a very helpful tool in the diagnostic process by increasing efficiency and reducing costs, it still requires the precision and expertise of professional dermatologists to be used optimally.