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Background: Due to the lower reliability of laboratory tests, skin diseases are more suitable for diagnosis with AI models. There are limited AI dermatology diagnostic models combining images and text; few of these are for Asian populations, and few cover the most common types of diseases. Methods: Leveraging a dataset sourced from Asia comprising over 200,000 images and 220,000 medical records, we explored a deep learning-based system for Dual-channel images and extracted text for the diagnosis of skin diseases model DIET-AI to diagnose 31 skin diseases, which covers the majority of common skin diseases. From 1 September to 1 December 2021, we prospectively collected images from 6,043 cases and medical records from 15 hospitals in seven provinces in China. Then the performance of DIET-AI was compared with that of six doctors of different seniorities in the clinical dataset. Results: The average performance of DIET-AI in 31 diseases was not less than that of all the doctors of different seniorities. By comparing the area under the curve, sensitivity, and specificity, we demonstrate that the DIET-AI model is effective in clinical scenarios. In addition, medical records affect the performance of DIET-AI and physicians to varying degrees. Conclusion: This is the largest dermatological dataset for the Chinese demographic. For the first time, we built a Dual-channel image classification model on a non-cancer dermatitis dataset with both images and medical records and achieved comparable diagnostic performance to senior doctors about common skin diseases. It provides references for exploring the feasibility and performance evaluation of DIET-AI in clinical use afterward.
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Introduction: The efficacy of abrocitinib for atopic dermatitis remains controversial. Aim: We conducted a systematic review and meta-analysis to explore the influence of abrocitinib versus placebo on the treatment of atopic dermatitis. Material and methods: We searched PubMed, Embase, Web of Science, EBSCO, and Cochrane Library databases up to June 2021 for randomized controlled trials (RCTs) assessing the effect of abrocitinib versus placebo for patients with atopic dermatitis. This meta-analysis was performed using a random-effect model. Results: Four RCTs involving 932 patients were included in the meta-analysis. Overall, compared with the control group for atopic dermatitis, abrocitinib has a remarkably positive impact on IGA response (OR = 6.60; 95% CI: 4.41-9.87; p < 0.00001), EASI-75 (OR = 9.19; 95% CI: 6.20-13.61; p < 0.00001), EASI-90 (OR = 10.50; 95% CI: 5.54-19.93; p < 0.0001), NRS response (OR = 6.99; 95% CI: 4.43-11.01; p < 0.00001) and adverse events (OR = 1.76; 95% CI: 1.23-2.52; p = 0.002), but showed no obvious influence on serious adverse events (OR = 0.53; 95% CI: 0.20-1.44; p = 0.22). Conclusions: Abrocitinib exerts a favorable effect on the treatment of atopic dermatitis.
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OBJECTIVES: This meta-analysis aims to summarize and estimate the relationship between rheumatoid arthritis (RA) susceptibility and two polymorphisms of interleukin-17F (IL-17F) 7488A/G and 7383A/G. MATERIALS AND METHODS: PubMed, Embase and Web of Science were searched up to 01 July 2017. Case-control studies with genotype frequencies data for 7488A/G and 7383A/G were included. The pooled effects were calculated by fixed-effect model or random effects model. RESULTS: A total of seven publications with 1,409 RA patients and 1,303 controls were included in the present meta-analysis. The results indicated that 7488A/G was significantly associated with increased susceptibility to RA (GA vs. AA: odds ratio [OR]=1.43, 95% confidence interval [CI]: 1.07-1.90, p=0.02; GG vs. AA: OR=3.22, 95% CI: 1.54-6.74, p=0.002; GA+GG vs. AA: OR=1.57, 95% CI: 1.02-2.42, p=0.04; GG vs. GA+AA: OR=3.05, 95% CI: 1.46-6.39, p=0.003). In subgroup analysis, 7488A/G was a strong risk factor in Europeans but not in Americans or Africans. No significant association was found between 7383A/G and RA in overall population or ethnic subgroups by all genetic model comparisons. CONCLUSION: This meta-analysis provided evidence that IL-17F 7488A/G polymorphism is associated with increased RA susceptibility, while no clear correlation was found between 7383A/G and RA.
