The integration of digital technologies, e.g., smartphone apps, represents an impactful advancement in training for melanoma diagnosis.
The increasing significance of digital technologies in dermatology is highlighted by developments in contactless dermoscopy and computerized analysis of pigmented skin lesions. Improvements in image quality enhance the visibility of dermoscopic patterns, providing more detailed information that can be instrumental in understanding lesion growth patterns and aiding in more accurate diagnoses. Other novel diagnostic modalities for melanoma are optical coherence tomography, Raman spectroscopy, combined ultrasound and photoacoustic imaging, and molecular diagnostics.
We propose the application of Cycle-Consistent Adversarial Networks (Cycle-GANs) in the transformation of dermoscopic images of nevi into simulated melanoma counterparts. This technology allows to visually demonstrate the subtle differences between nevi and melanoma using actual dermoscopic images from the patient’s body. During skin screening procedures, dermatologists could present both the original nevus image and the AI-generated melanoma version to the patient. This side-by-side comparison might aid dermatologists in explaining why certain lesions do not require excision and what changes to look for in follow-up examination. Conversely, for lesions appearing to be melanoma, the AI can generate a nevus counterpart to also help educate patients on recognizing the differences between nevi and melanomas and the importance of monitoring for changes. Additionally, the utilization of Cycle-GANs to simulate the evolution of skin lesions presents an innovative opportunity to test potential novel diagnostic criteria based on image processing against AI-generated simulations. By comparing the characteristics of nevi and simulated melanoma counterparts, our approach could accelerate the validation process of image processing techniques and might enhance the general understanding of lesion growth patterns and dynamics.
Further reading: https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2024.1445318/full