The latest global big data evidence indicated the changes of skin and venereal disease burden was huge. HIV/AIDS disease burden was the heaviest diseases among all skin and venereal diseases, and its skin manifestation was serious. The evidence of skin manifestation was searched and classified by subjects such as clinical symptoms, diagnosis & treatment, nursing, etc. The results showed, that the skin manifestation of HIV/AIDS with high incidence was serious, atypical, difficult to cure which was easy to misdiagnose or miss diagnosis. After analyzing the global HIV/AIDS guidelines, we found that many high quality guidelines with widely-covered subjects were produced by developed countries, while quite a few low quality and ones with narrowly-covered subjects were produced by developing countries. Only one guideline was for treatment of HIV/AIDS skin lesion. Based on the current evidence, we call for that all healthcare professionals to increase their awareness, update knowledge, and joint in cooperative prevention and treatment of HIV/AIDS. We also call for that we should produce high quality primary evidence for clinical diagnosis and treatment of HIV/AIDS skin manifestation, and clinical practice guidelines based on good evidence. For the increasing heavy burden of skin and venereal diseases, we should adjust and expand research directions, enrich and improve new interdisciplinary knowledge. We also should constantly train professionals and spread out knowledge in public on prevention and treatment for skin manifestation, so as to transform the evidence in time, effectively protect medical staff and susceptible population, effectively prevent and treat this disease, and improve the satisfaction of our country, hospitals and patients.
The skin is the largest organ of the human body, and many visceral diseases will be directly reflected on the skin, so it is of great clinical significance to accurately segment the skin lesion images. To address the characteristics of complex color, blurred boundaries, and uneven scale information, a skin lesion image segmentation method based on dense atrous spatial pyramid pooling (DenseASPP) and attention mechanism is proposed. The method is based on the U-shaped network (U-Net). Firstly, a new encoder is redesigned to replace the ordinary convolutional stacking with a large number of residual connections, which can effectively retain key features even after expanding the network depth. Secondly, channel attention is fused with spatial attention, and residual connections are added so that the network can adaptively learn channel and spatial features of images. Finally, the DenseASPP module is introduced and redesigned to expand the perceptual field size and obtain multi-scale feature information. The algorithm proposed in this paper has obtained satisfactory results in the official public dataset of the International Skin Imaging Collaboration (ISIC 2016). The mean Intersection over Union (mIOU), sensitivity (SE), precision (PC), accuracy (ACC), and Dice coefficient (Dice) are 0.901 8, 0.945 9, 0.948 7, 0.968 1, 0.947 3, respectively. The experimental results demonstrate that the method in this paper can improve the segmentation effect of skin lesion images, and is expected to provide an auxiliary diagnosis for professional dermatologists.