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INTRODUCTION: The diagnosis of melasma is often based on the naked-eye judgment of physicians. However, this is a challenge for inexperienced physicians and non-professionals, and incorrect treatment might have serious consequences. Therefore, it is important to develop an accurate method for melasma diagnosis. The objective of this study is to develop and validate an intelligent diagnostic system based on deep learning for melasma images. METHODS: A total of 8010 images in the VISIA system, comprising 4005 images of patients with melasma and 4005 images of patients without melasma, were collected for training and testing. Inspired by four high-performance structures (i.e., DenseNet, ResNet, Swin Transformer, and MobileNet), the performances of deep learning models in melasma and non-melasma binary classifiers were evaluated. Furthermore, considering that there were five modes of images for each shot in VISIA, we fused these modes via multichannel image input in different combinations to explore whether multimode images could improve network performance. RESULTS: The proposed network based on DenseNet121 achieved the best performance with an accuracy of 93.68% and an area under the curve (AUC) of 97.86% on the test set for the melasma classifier. The results of the Gradient-weighted Class Activation Mapping showed that it was interpretable. In further experiments, for the five modes of the VISIA system, we found the best performing mode to be "BROWN SPOTS." Additionally, the combination of "NORMAL," "BROWN SPOTS," and "UV SPOTS" modes significantly improved the network performance, achieving the highest accuracy of 97.4% and AUC of 99.28%. CONCLUSIONS: In summary, deep learning is feasible for diagnosing melasma. The proposed network not only has excellent performance with clinical images of melasma, but can also acquire high accuracy by using multiple modes of images in VISIA.
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The aim of this study is to examine the association of the combined indices of respiratory sinus arrhythmia at rest (basal RSA) and in response to a mental arithmetic task (RSA reactivity) to internet addiction. Participants included 99 young adults (61 men and 38 women) who reported on their levels of internet addiction. The results indicated that RSA reactivity moderated the association between basal RSA and self-reported internet addiction. This showcased that basal RSA had a negative association with internet addiction for individuals with higher RSA reactivity but had no significant association with internet addiction for those with lower RSA reactivity. These findings help to extend our understanding of the link between the parasympathetic nervous systems activity and internet addiction. Additionally, it underscores the need for the simultaneous consideration of basal RSA and RSA reactivity in future studies.