Comparing a deep learning model's diagnostic performance to that of radiologists to detect Covid -19 features on chest radiographs.

Background: Whether the sensitivity of Deep Learning (DL) models to screen chest radiographs (CXR) for CoVID-19 can approximate that of radiologists, so that they can be adopted and used if real-time review of CXRs by radiologists is not possible, has not been explored before.

Objective: To evaluate the diagnostic performance of a doctor-trained DL model (Svita_DL8) to screen for COVID-19 on CXR, and to compare the performance of the DL model with that of expert radiologists.

Materials And Methods: We used a pre-trained convolutional neural network to develop a publicly available online DL model to evaluate CXR examinations saved in .

Clinical profiles and risk factors for early and medium-term mortality following heart transplantation in a pediatric population: A single-center experience.

Aims And Objectives: There is a paucity of data regarding the outcomes of Heart transplantation in children from the Indian subcontinent. The data of patients under the age of 18 undergoing an isolated heart transplantation was analyzed for patient clinical profiles and risk factors for early and medium-term mortality. Hospital mortality was defined as death within 90 days of transplantation and medium-term survival as follow up of up to 6 years.