Integrating generative AI with ABCDE rule analysis for enhanced skin cancer diagnosis, dermatologist training and patient education

Significance: The early detection and accurate monitoring of suspicious skin lesions are critical for effective dermatological diagnosis and treatment, particularly for reliable identification of the progression of nevi to melanoma. The traditional diagnostic framework, the ABCDE rule, provides a foundation for evaluating lesion characteristics by visual examination using dermoscopes. Simulations of skin lesion progression could improve the understanding of melanoma growth patterns.

Aim: This study aims to enhance lesion analysis and understanding of lesion progression by providing a simulated potential progression of nevi into melanomas.

Approach: The study generates a dataset of simulated lesion progressions, from nevi to simulated melanoma, based on a Cycle-Consistent Adversarial Network (Cycle-GAN) and frame interpolation. We apply an optical flow analysis to the generated dermoscopic image sequences, enabling the quantification of lesion transformation. In parallel, we evaluate changes in ABCDE rule metrics as example to assess the simulated evolution.

Results: We present the first simulation of nevi progressing into simulated melanoma counterparts, consisting of 152 detailed steps. The ABCDE rule metrics correlate with the simulation in a natural manner. For the seven samples studied, the asymmetry metric increased by an average of 19%, the border gradient metric increased by an average of 63%, the convexity metric decreased by an average of 3%, the diameter increased by an average of 2%, and the color dispersion metric increased by an average of 45%. The diagnostic value of the ABCDE rule is enhanced through the addition of insights based on optical flow. The outward expansion of lesions, as captured by optical flow vectors, correlates strongly with the expected increase in diameter, confirming the simulation's fidelity to known lesion growth patterns. The heatmap visualizations further illustrate the degree of change within lesions, offering an intuitive visual proxy for lesion evolution.

Conclusion: The achieved simulations of potential lesion progressions could facilitate improved early detection and understanding of how lesions evolve. By combining the optical flow analysis with the established criteria of the ABCDE rule, this study presents a significant advancement in dermatoscopic diagnostics and patient education. Future research will focus on applying this integrated approach to real patient data, with the aim of enhancing the understanding of lesion progression and the personalization of dermatological care.