Development of Convolutional Neural Network-Based AI-Dermatoscope for Non-Invasive Skin Assessments

Early detection of skin conditions is crucial, and some skin conditions can become more difficult to treat if left untreated. The gold standard Dermatoscope is a non-invasive technique used for the examination and evaluation of skin lesions, which is equipped with a magnifying lens and a light source. However, precise inspection of existing dermatoscopes has become a limitation due to the unavailability of image-analyzing methods. Herein, this study reports the successful development of a Convolutional Neural Networks (CNN) based, Artificial intelligence (AI)-Dermatoscope integrating optics and a smart illumination system to enhance the accurate examination of acne conditions of the skin. The system was trained on a large dataset of acne to accurately identify and classify skin conditions. Finally, the system utilizes CNN knowledge to predict new images of skin and provide diagnostic information to doctors and other healthcare professionals. Thus, this system will improve the accuracy and speed of skin diagnosis, and consequently, improve the health-related quality of life of patients.