Background: Psoriasis is one of the most frequent inflammatory skin conditions and could be treated via tele-dermatology, provided that the current lack of reliable tools for objective severity assessments is overcome. Psoriasis Area and Severity Index (PASI) has a prominent level of subjectivity and is rarely used in real practice, although it is the most widely accepted metric for measuring psoriasis severity currently. Objective: This study aimed to develop an image–artificial intelligence (AI)–based validated system for severity assessment with the explicit intention of facilitating long-term management of patients with psoriasis. Methods: A deep learning system was trained to estimate the PASI score by using 14,096 images from 2367 patients with psoriasis. We used 1962 patients from January 2015 to April 2021 to train the model and the other 405 patients from May 2021 to July 2021 to validate it. A multiview feature enhancement block was designed to combine vision features from different perspectives to better simulate the visual diagnostic method in clinical practice. A classification header along with a regression header was simultaneously applied to generate PASI scores, and an extra cross-teacher header after these 2 headers was designed to revise their output. The mean average error (MAE) was used as the metric to evaluate the accuracy of the predicted PASI score. By making the model minimize the MAE value, the model becomes closer to the target value. Then, the proposed model was compared with 43 experienced dermatologists. Finally, the proposed model was deployed into an app named SkinTeller on the WeChat platform. Results: The proposed image-AI–based PASI-estimating model outperformed the average performance of 43 experienced dermatologists with a 33.2% performance gain in the overall PASI score. The model achieved the smallest MAE of 2.05 at 3 input images by the ablation experiment. In other words, for the task of psoriasis severity assessment, the severity score predicted by our model was close to the PASI score diagnosed by experienced dermatologists. The SkinTeller app has been used 3369 times for PASI scoring in 1497 patients from 18 hospitals, and its excellent performance was confirmed by a feedback survey of 43 dermatologist users. Conclusions: An image-AI–based psoriasis severity assessment model has been proposed to automatically calculate PASI scores in an efficient, objective, and accurate manner. The SkinTeller app may be a promising alternative for dermatologists’ accurate assessment in the real world and chronic disease self-management in patients with psoriasis.

Background: Dexmedetomidine is recommended for sedation in patients on mechanical ventilation. Whether age or ICU types could alter mortality in invasive mechanically ventilated patients with sepsis receiving dexmedetomidine is unknown.Methods: We included patients with sepsis receiving invasive mechanical ventilation from the Medical Information Mart for Intensive Care IV database. The exposure was intravenous dexmedetomidine administration during ICU stay. The primary outcome was 28-day mortality. The secondary outcomes were the length of ICU stay and liberation from invasive mechanical ventilation. Propensity score matching (PSM) and Cox proportional hazards regression were used to adjust for confounders and investigate any association. Restricted cubic spline models were used to evaluate potential nonlinear associations.Results: The pre-matched and propensity score-matched cohorts included 5,871 and 2016 patients, respectively. In the PSM cohorts, dexmedetomidine exposure was related to lower 28-day mortality (186 [17.7%] vs. 319 [30.3%]; p < 0.001). Patients receiving dexmedetomidine, regardless of whether they were younger (≤65 years; hazard ratio [HR], 0.31; 95% confidence interval [CI], 0.23–0.42; p < 0.001) or elderly (>65 years; HR, 0.65; 95% CI, 0.52–0.83; p < 0.001), was associated with lower 28-day mortality (61 [10.3%] vs. 168 [28.2%] for younger; 125 [27.2%] vs. 152 [33.0%] for elderly). Patients receiving dexmedetomidine was also associated with lower 28-day mortality (53 [12.6%] vs. 113 [26.5%] for surgical intensive care unit [SICU]; 133 [21.0%] vs. 206 [32.9%] for non-SICU) regardless of whether the first admission to the SICU (HR, 0.36; 95% CI, 0.25–0.50; p < 0.001) or non-SICU (HR, 0.50; 95% CI, 0.40–0.62; p < 0.001). Moreover, both dose and duration of dexmedetomidine administration were related to lower 28-day mortality than no dexmedetomidine in younger patients (p < 0.001), but it not statistically significant in elderly patients.Conclusion: Dexmedetomidine was associated with lower 28-day mortality in critically ill patients with sepsis receiving invasive mechanical ventilation, regardless of whether patients were younger or elderly, the first admission to the SICU or non-SICU.

Data on the characteristics and treatment outcomes of super-responders and non-super-responders in psoriasis under adalimumab treatment are limited. A retrospective analysis from psoriatic patients treated with adalimumab was compared to characterize super-responders vs non-super-responders' groups, identify factors associated with super response, and assess treatment outcomes after switching. 15 out of 70 (21.4%) patients were categorized as super-responder. The proportion of patients achieving a PASI 100 response was significantly higher in super-responders than non-super-responders at weeks 12, 24, and 52. Female sex and Charlson Co-morbidity Index were significantly associated with super-responders. A high level of high-density lipoprotein was independently associated with PASI 90 response at weeks 24 and 52. Additionally, nearly 35%-43% of non-super-responders switching to interleukin-17A (IL-17A) inhibitors may achieve a PASI 100 response at week 12. In contrast, all super-responders switching to IL-17A inhibitors achieved a PASI 100 response at week 4. Super-responders treated with adalimumab have a higher rate of being female and fewer comorbidities. And super-responders have better PASI responses than non-super-responders, whether the patients were treated with adalimumab or switched to IL-17A inhibitors.