Factors affecting poor prognosis of COVID-19 in people living with human immunodeficiency virus: A systematic review and meta-analysis of co-infection.

Objectives: This study aims to assess the correlation between clinical features and mortality in human immunodeficiency virus (HIV)-infected individuals with COVID-19.

Methods: A systematic literature search was conducted for cohort, cross-sectional, and case series that reported co-infection with HIV and COVID-19 published from January to September 2020. Clinical features such as age, comorbidities, CD4T lymphocyte counts, HIV RNA levels, and antiretroviral regimens were evaluated using meta-analyses and systematic reviews.

3D DenseNet with temporal transition layer for heart rate estimation from real-life RGB videos.

Background: Deep learning has demonstrated superior performance over traditional methods for the estimation of heart rates in controlled contexts. However, in less controlled scenarios this performance seems to vary based on the training dataset and the architecture of the deep learning models.

Objectives: In this paper, we develop a deep learning-based model leveraging the power of 3D convolutional neural networks (3DCNN) to extract temporal and spatial features that lead to an accurate heart rates estimation from RGB no pre-defined region of interest (ROI) videos.

A Multimodal Transformer Model for Recognition of Images from Complex Laparoscopic Surgical Videos.

The determination of the potential role and advantages of artificial intelligence-based models in the field of surgery remains uncertain. This research marks an initial stride towards creating a multimodal model, inspired by the Video-Audio-Text Transformer, that aims to reduce negative occurrences and enhance patient safety. The model employs text and image embedding state-of-the-art models (ViT and BERT) to assess their efficacy in extracting the hidden and distinct features from the surgery video frames.

Conventional and deep learning methods in heart rate estimation from RGB face videos.

Contactless vital signs monitoring is a fast-advancing scientific field that aims to employ monitoring methods that do not necessitate the use of leads or physical attachments to the patient in order to overcome the shortcomings and limits of traditional monitoring systems. Several traditional methods have been applied to extract the heart rate (HR) signal from the face. Moreover, machine learning has recently contributed majorly to the development of such a field in which deep networks and other deep learning methods are employed to extract the HR signal from RGB face videos.

An update on the knee osteoarthritis severity grading using wide residual learning.

Background: Knee Osteoarthritis (KOA) is the most common type of Osteoarthritis (OA) and it is diagnosed by physicians using a standard 0 -4 Kellgren Lawrence (KL) grading system which sets the KOA on a spectrum of 5 grades; starting from normal (0) to Severe OA (4).

Objectives: In this paper, we propose a transfer learning approach of a very deep wide residual learning-based network (WRN-50-2) which is fine-tuned using X-ray plain radiographs from the Osteoarthritis Initiative (OAI) dataset to learn the KL severity grading of KOA.

Methods: We propose a data augmentation approach of OAI data to avoid data imbalance and reduce overfitting by applying it only to certain KL grades depending on their number of plain radiographs.

Radiologists versus Deep Convolutional Neural Networks: A Comparative Study for Diagnosing COVID-19.

The reverse transcriptase polymerase chain reaction (RT-PCR) is still the routinely used test for the diagnosis of SARS-CoV-2 (COVID-19). However, according to several reports, RT-PCR showed a low sensitivity and multiple tests may be required to rule out false negative results. Recently, chest computed tomography (CT) has been an efficient tool to diagnose COVID-19 as it is directly affecting the lungs.

An online survey and review about the awareness, coping style, and exercise behavior during the "COVID-19 pandemic situation" by implementing the cloud-based medical treatment technology system in China among the public.

To examine basic COVID-19 knowledge, coping style and exercise behavior among the public including government-provided medical cloud system treatment app based on the internet during the outbreak. Besides, to provide references for developing targeted strategies and measures on prevention and control of COVID-19. We conducted an online survey from 11th to 15th March 2020 via WeChat App using a designed questionnaire.