Skin diseases in the Da Qing Diabetes Study: a cross-sectional study / 中华医学杂志(英文版)

BACKGROUND@#The prevalence of skin diseases and diabetes mellitus (DM) are prominent around the world. The current scope of knowledge regarding the prevalence of skin diseases and comorbidities with type 2 DM (T2DM) is limited, leading to limited recognition of the correlations between skin diseases and T2DM.@*METHODS@#We collected 383 subjects from the Da Qing Diabetes Study during the period from July 9th to September 1st, 2016. The subjects were categorized into three groups: Normal glucose tolerance (NGT), impaired glucose tolerance (IGT), and T2DM. The prevalence and clinical characteristics of skin diseases were recorded and investigated.@*RESULTS@#In this cross-sectional study, 383 individuals with ages ranging from 53 to 89-year-old were recruited. The overall prevalence of skin diseases was 93.5%, and 75.7% of individuals had two or more kinds of skin diseases. Additionally, there were 47 kinds of comorbid skin diseases in patients with T2DM, of which eight kinds of skin diseases had a prevalence >10%. The prevalence of skin diseases in NGT, IGT, and T2DM groups were 93.3%, 91.5%, and 96.6%, respectively; stratified analysis by categories showed a statistically significant difference in "disturbances of pigmentation" and "neurological and psychogenic dermatoses". The duration of T2DM also significantly associated with the prevalence of "disturbances of pigmentation" and "neurological and psychogenic dermatoses". Subsequently, the prevalence of "disturbances of pigmentation" was higher in males than females in NGT (P < 0.01) and T2DM (P < 0.01) groups. In addition, the difference in the prevalence of "disturbances of pigmentation" was also significant in NGT and T2DM groups (P < 0.01).@*CONCLUSIONS@#There was a high prevalence of skin diseases in the Da Qing Diabetes Study. To address the skin diseases in the Da Qing Diabetes Study, increased awareness and intervention measures should be implemented.

Diagnostic capacity of skin tumor artificial intelligence-assisted decision-making software in real-world clinical settings / 中华医学杂志(英文版)

BACKGROUND@#Youzhi artificial intelligence (AI) software is the AI-assisted decision-making system for diagnosing skin tumors. The high diagnostic accuracy of Youzhi AI software was previously validated in specific datasets. The objective of this study was to compare the performance of diagnostic capacity between Youzhi AI software and dermatologists in real-world clinical settings.@*METHODS@#A total of 106 patients who underwent skin tumor resection in the Dermatology Department of China-Japan Friendship Hospital from July 2017 to June 2019 and were confirmed as skin tumors by pathological biopsy were selected. Dermoscopy and clinical images of 106 patients were diagnosed by Youzhi AI software and dermatologists at different dermoscopy diagnostic levels. The primary outcome was to compare the diagnostic accuracy of the Youzhi AI software with that of dermatologists and that measured in the laboratory using specific data sets. The secondary results included the sensitivity, specificity, positive predictive value, negative predictive value, F-measure, and Matthews correlation coefficient of Youzhi AI software in the real-world.@*RESULTS@#The diagnostic accuracy of Youzhi AI software in real-world clinical settings was lower than that of the laboratory data (P < 0.001). The output result of Youzhi AI software has good stability after several tests. Youzhi AI software diagnosed benign and malignant diseases by recognizing dermoscopic images and diagnosed disease types with higher diagnostic accuracy than by recognizing clinical images (P = 0.008, P = 0.016, respectively). Compared with dermatologists, Youzhi AI software was more accurate in the diagnosis of skin tumor types through the recognition of dermoscopic images (P = 0.01). By evaluating the diagnostic performance of dermatologists under different modes, the diagnostic accuracy of dermatologists in diagnosing disease types by matching dermoscopic and clinical images was significantly higher than that by identifying dermoscopic and clinical images in random sequence (P = 0.022). The diagnostic accuracy of dermatologists in the diagnosis of benign and malignant diseases by recognizing dermoscopic images was significantly higher than that by recognizing clinical images (P = 0.010).@*CONCLUSION@#The diagnostic accuracy of Youzhi AI software for skin tumors in real-world clinical settings was not as high as that of using special data sets in the laboratory. However, there was no significant difference between the diagnostic capacity of Youzhi AI software and the average diagnostic capacity of dermatologists. It can provide assistant diagnostic decisions for dermatologists in the current state.