Deep Learning-Based Automatic Assessment of Lung Impairment in COVID-19 Pneumonia: Predicting Markers of Hypoxia With Computer Vision.

Background: Hypoxia is a potentially life-threatening condition that can be seen in pneumonia patients. Objective: We aimed to develop and test an automatic assessment of lung impairment in COVID-19 associated pneumonia with machine learning regression models that predict markers of respiratory and cardiovascular functioning from radiograms and lung CT. Materials and Methods: We enrolled a total of 605 COVID-19 cases admitted to Al Ain Hospital from 24 February to 1 July 2020 into the study. The inclusion criteria were as follows: age ≥ 18 years; inpatient admission; PCR positive for SARS-CoV-2; lung CT available at PACS. We designed a CNN-based regression model to predict systemic oxygenation markers from lung CT and 2D diagnostic images of the chest. The 2D images generated by averaging CT scans were analogous to the frontal and lateral view radiograms. The functional (heart and breath rate, blood pressure) and biochemical findings (SpO2, H C O 3 - , K +, Na +, anion gap, C-reactive protein) served as ground truth. Results: Radiologic findings in the lungs of COVID-19 patients provide reliable assessments of functional status with clinical utility. If fed to ML models, the sagittal view radiograms reflect dyspnea more accurately than the coronal view radiograms due to the smaller size and the lower model complexity. Mean absolute error of the models trained on single-projection radiograms was approximately 11÷12% and it dropped by 0.5÷1% if both projections were used (11.97 ± 9.23 vs. 11.43 ± 7.51%; p = 0.70). Thus, the ML regression models based on 2D images acquired in multiple planes had slightly better performance. The data blending approach was as efficient as the voting regression technique: 10.90 ± 6.72 vs. 11.96 ± 8.30%, p = 0.94. The models trained on 3D images were more accurate than those on 2D: 8.27 ± 4.13 and 11.75 ± 8.26%, p = 0.14 before lung extraction; 10.66 ± 5.83 and 7.94 ± 4.13%, p = 0.18 after the extraction. The lung extraction boosts 3D model performance unsubstantially (from 8.27 ± 4.13 to 7.94 ± 4.13%; p = 0.82). However, none of the differences between 3D and 2D were statistically significant. Conclusion: The constructed ML algorithms can serve as models of structure-function association and pathophysiologic changes in COVID-19. The algorithms can improve risk evaluation and disease management especially after oxygen therapy that changes functional findings. Thus, the structural assessment of acute lung injury speaks of disease severity.

Metabolomic Profiling of Lipids and Fatty Acids: 3 Years Postoperative Laparoscopic Sleeve Gastrectomy.

Visceral obesity is common in the United Arab Emirates and worldwide. Although laparoscopic sleeve gastrectomy (LSG) leads to effective and sustainable weight loss, its long-term beneficial impact on other risk factors, including blood lipid and fatty acid (FA) profiles, remains unknown. These two profiles were assessed in patients 3 years after undergoing LSG and in LSG candidates (controls). Lipid profiles were measured using the Cobas e411 modular analyzer, and 35 FAs were identified. The age and body mass index were 36.55 ± 8.65 years and 31.49 ± 6.43 kg/m2 in the LSG group and 35.44 ± 9.51 years and 32.29 ± 5.38 kg/m2 in the control group, respectively. The overall lipid profile was more favorable in the LSG group than in the control group. Total saturated, monounsaturated, and polyunsaturated FAs were similar between the groups, but total medium-chain FAs were more abundant in the LSG group. In endogenous FA synthesis, the estimated activity of C16Δ9 desaturase and Δ5 desaturase decreased, whereas that of elongase increased in the LSG group compared with that in the control group. The benefits of LSG on blood lipid and FA profiles in patients with 3-year LSG may be limited. Hence, lifestyle interventions combined with a long-term and strict regular follow-up regime may be warranted for patients undergoing LSG.

Laparoscopic Sleeve Gastrectomy versus Gastric Bypass in Late Adolescents: What Is the Optimal Surgical Strategy for Morbid Obesity?

Introduction Interest in bariatric surgery in adolescents is increasing, because adolescent obesity equals increased health risks in adult life. To define the preferred procedure in adolescents, this study compares outcomes of LSG and LRYGB in late adolescents in our center. Materials and methods Data on baseline characteristics, operative details, and follow-up were collected retrospectively in all patients (age 18-20 years) who underwent LSG or LRYGB in our clinic. Outcomes were analyzed using nonparametric tests. Results Total 65 adolescents (54 females; median age 19 years) were included, 45 patients underwent LSG, and 20 underwent LRYGB.Significant differences in %EWL were observed at 2-year follow-up; 81.0% in LRYGB (n = 11) versus 96.8% in LSG (n = 23), p = 0.007. No differences were observed in postoperative mortality, complication rate, and resolution of comorbidities between both procedures. Discussion LSG showed significant better results than LRYGB in terms of %EWL after 2 years in this selected group of late adolescents. Considering these results in LSG patients and the theoretical advantages of LSG (normal diet options, preservation of an intact GI-tract, less vitamin disturbances, and better quality of life), LSG may be an appropriate bariatric technique to perform in morbidly obese adolescents.