Comprehensive analysis of clinical images contributions for melanoma classification using convolutional neural networks.

BACKGROUND: Timely diagnosis plays a critical role in determining melanoma prognosis, prompting the development of deep learning models to aid clinicians. Questions persist regarding the efficacy of clinical images alone or in conjunction with dermoscopy images for model training. This study aims to compare the classification performance for melanoma of three types of CNN models: those trained on clinical images, dermoscopy images, and a combination of paired clinical and dermoscopy images from the same lesion. MATERIALS AND METHODS: We divided 914 image pairs into training, validation, and test sets. Models were built using pre-trained Inception-ResNetV2 convolutional layers for feature extraction, followed by binary classification. Training comprised 20 models per CNN type using sets of random hyperparameters. Best models were chosen based on validation AUC-ROC. RESULTS: Significant AUC-ROC differences were found between clinical versus dermoscopy models (0.661 vs. 0.869, p < 0.001) and clinical versus clinical + dermoscopy models (0.661 vs. 0.822, p = 0.001). Significant sensitivity differences were found between clinical and dermoscopy models (0.513 vs. 0.799, p = 0.01), dermoscopy versus clinical + dermoscopy models (0.799 vs. 1.000, p = 0.02), and clinical versus clinical + dermoscopy models (0.513 vs. 1.000, p < 0.001). Significant specificity differences were found between dermoscopy versus clinical + dermoscopy models (0.800 vs. 0.288, p < 0.001) and clinical versus clinical + dermoscopy models (0.650 vs. 0.288, p < 0.001). CONCLUSION: CNN models trained on dermoscopy images outperformed those relying solely on clinical images under our study conditions. The potential advantages of incorporating paired clinical and dermoscopy images for CNN-based melanoma classification appear less clear based on our findings.

Association of vitiligo with multiple cutaneous and extra-cutaneous autoimmune diseases: a nationwide cross-sectional study.

Previous studies found conflicting results about associations of vitiligo with different autoimmune diseases. To evaluate associations of vitiligo with multiple autoimmune diseases. A cross-sectional study representative of 612,084,148 US patients from the Nationwide Emergency Department Sample (NEDS) 2015-2019 was performed. Vitiligo and autoimmune diseases were identified using International Classification of Diseases-10 codes. The most frequent autoimmune disorders in patients with vitiligo were type 1 diabetes, rheumatoid arthritis, systemic lupus erythematosus (SLE), autoimmune thyroiditis, Addison's disease, and systemic sclerosis (SSc). Vitiligo was associated with any autoimmune disorder (adjusted odds ratio [95% confidence interval] 1.45 [1.32-1.58]). Cutaneous disorders with largest effect-sizes were alopecia areata (186.22 [115.31-300.72]) and SSc (32.13 [25.28-40.82]). Non-cutaneous comorbidities with largest effect-sizes were primary sclerosing cholangitis (43.12 [18.98-97.99]), pernicious anemia (41.26 [31.66-53.78]), Addison's disease (33.85 [26.68-42.9]), and autoimmune thyroiditis (31.65 [26.34-38.02]). Vitiligo is associated with multiple cutaneous and non-cutaneous autoimmune diseases, especially in females and older age.