Detection of neurologic changes in critically ill infants using deep learning on video data: a retrospective single center cohort study.

Background: Infant alertness and neurologic changes can reflect life-threatening pathology but are assessed by physical exam, which can be intermittent and subjective. Reliable, continuous methods are needed. We hypothesized that our computer vision method to track movement, pose artificial intelligence (AI), could predict neurologic changes in the neonatal intensive care unit (NICU).

Pulsed directed energy deposition-arc technology for depositing stainless steel 309L: Microstructural, elements distribution, and mechanical characteristics.

In recent years, additive manufacturing has increased in prominence as a primary method of manufacturing around the globe. Modern metal additive manufacturing presents an innovative approach to manufacturing complex structures for the aerospace, energy, and construction sectors, due to technological advancements. In the present study, the austenitic stainless steel 309L (SS-309L) thick wall component was manufactured employing pulsed current gas tungsten arc welding (PC-GTAW) WAAM technology.

Machine Learning Quantification of Pulmonary Regurgitation Fraction from Echocardiography.

Assessment of pulmonary regurgitation (PR) guides treatment for patients with congenital heart disease. Quantitative assessment of PR fraction (PRF) by echocardiography is limited. Cardiac MRI (cMRI) is the reference-standard for PRF quantification.

Accurate prediction of neurologic changes in critically ill infants using pose AI.

Infant alertness and neurologic changes can reflect life-threatening pathology but are assessed by exam, which can be intermittent and subjective. Reliable, continuous methods are needed. We hypothesized that our computer vision method to track movement, pose AI, could predict neurologic changes in the neonatal intensive care unit (NICU).