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Dear Editor, While studies have established risk factors for clinical deterioration in coronavirus disease 2019 (COVID-19) patients [1, 2], or attempted to identify phenotypes based on experts opinion [3], identifying sub-phenotypes based on more easily obtained data could help identify patients at highest risk of clinical deterioration and refine inclusion of more homogeneous subpopulations in clinical trials. We here applied an unsupervised, multivariate clustering algorithm using easy-to-obtain clinical variables to identify COVID-19 sub-phenotypes and examined the association with clinical deterioration. This retrospective cohort study was performed among adult COVID-19-positive patients (using real-time reverse transcriptase-polymerase chain reaction assay) with a hospital visit between February 28 and March 26, 2020, at eight teaching hospitals of the Assistance Publique-Hôpitaux de Paris. The Institutional Review Board (IRB) of Ile-de-France VII approved the study and waived the need for informed consent from individual patients (DC 2009/CO-15-000). We selected 22 candidate variables for the clustering analysis including demographic information among 608 patients with available candidate variables, disease history, major clinical symptoms, and medications on the day of positive diagnostic, which represents the final cohort (Supplementary file). We

LAGB seems to be a good option to treat obese adolescents, as it is a minimally invasive procedure that does not radically change the patient's anatomy and is associated with minimal morbidity. It leads to a sustained improvement/resolution of OAC, and although weight loss is not continuous, it is maintained over time.

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Hepatocellular carcinoma ranks fifth amongst the most common malignancies and is the third most common cause of cancer-related death globally. Artificial Intelligence is a rapidly growing field of interest. Following the PRISMA reporting guidelines, we conducted a systematic review to retrieve articles reporting the application of AI in HCC detection and characterization. A total of 27 articles were included and analyzed with our composite score for the evaluation of the quality of the publications. The contingency table reported a statistically significant constant improvement over the years of the total quality score (p = 0.004). Different AI methods have been adopted in the included articles correlated with 19 articles studying CT (41.30%), 20 studying US (43.47%), and 7 studying MRI (15.21%). No article has discussed the use of artificial intelligence in PET and X-ray technology. Our systematic approach has shown that previous works in HCC detection and characterization have assessed the comparability of conventional interpretation with machine learning using US, CT, and MRI. The distribution of the imaging techniques in our analysis reflects the usefulness and evolution of medical imaging for the diagnosis of HCC. Moreover, our results highlight an imminent need for data sharing in collaborative data repositories to minimize unnecessary repetition and wastage of resources.

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