Dual center validation of deep learning for automated multi-label segmentation of thoracic anatomy in bedside chest radiographs.

Background And Objectives: Bedside chest radiographs (CXRs) are challenging to interpret but important for monitoring cardiothoracic disease and invasive therapy devices in critical care and emergency medicine. Taking surrounding anatomy into account is likely to improve the diagnostic accuracy of artificial intelligence and bring its performance closer to that of a radiologist. Therefore, we aimed to develop a deep convolutional neural network for efficient automatic anatomy segmentation of bedside CXRs.

Height Estimation of Children under Five Years using Depth Images.

Malnutrition is a global health crisis and is a leading cause of death among children under 5 years. Detecting malnutrition requires anthropometric measurements of weight, height, and middle-upper arm circumference. However, measuring them accurately is a challenge, especially in the global south, due to limited resources.