The proposed deep learning–based approach for body composition analysis demonstrated comparable performance to the manual process, presenting a more cost-effective alternative to conventional methods.

Columbia Engineering researchers reveal how robots use self-observation to learn movement and adapt, enhancing autonomy and resilience in real-world tasks.

Utility of Normalized Body Composition Areas, Derived From Outpatient Abdominal CT Using a Fully Automated Deep Learning Method, for Predicting Subsequent Cardiovascular Events.

Children who move while learning sounds of letters significantly improve their ability to recognize individual letter sounds.