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Detecting Large Vessel Occlusion at Multiphase CT Angiography by Using a Deep Convolutional Neural Network.

Radiology
Background Large vessel occlusion (LVO) stroke is one of the most time-sensitive diagnoses in medicine and requires emergent endovascular therapy to reduce morbidity and mortality. Leveraging recent advances in deep learning may facilitate rapid dete...

Machine learning prediction of the adverse outcome for nontraumatic subarachnoid hemorrhage patients.

Annals of clinical and translational neurology
OBJECTIVE: Subarachnoid hemorrhage (SAH) is often devastating with increased early mortality, particularly in those with presumed delayed cerebral ischemia (DCI). The ability to accurately predict survival for SAH patients during the hospital course ...

AI in the treatment of fertility: key considerations.

Journal of assisted reproduction and genetics
Artificial intelligence (AI) has been proposed as a potential tool to help address many of the existing problems related with empirical or subjective assessments of clinical and embryological decision points during the treatment of infertility. AI te...

Claims-Based Algorithms for Identifying Patients With Pulmonary Hypertension: A Comparison of Decision Rules and Machine-Learning Approaches.

Journal of the American Heart Association
Background Real-world healthcare data are an important resource for epidemiologic research. However, accurate identification of patient cohorts-a crucial first step underpinning the validity of research results-remains a challenge. We developed and e...

Public Perceptions of Artificial Intelligence and Robotics in Medicine.

Journal of endourology
To understand better the public perception and comprehension of medical technology such as artificial intelligence (AI) and robotic surgery. In addition to this, to identify sensitivity to their use to ensure acceptability and quality of counseling....

Kinematic parameters obtained with the ArmeoSpring for upper-limb assessment after stroke: a reliability and learning effect study for guiding parameter use.

Journal of neuroengineering and rehabilitation
BACKGROUND: After stroke, kinematic measures obtained with non-robotic and robotic devices are highly recommended to precisely quantify the sensorimotor impairments of the upper-extremity and select the most relevant therapeutic strategies. Although ...

Predicting clinically significant motor function improvement after contemporary task-oriented interventions using machine learning approaches.

Journal of neuroengineering and rehabilitation
BACKGROUND: Accurate prediction of motor recovery after stroke is critical for treatment decisions and planning. Machine learning has been proposed to be a promising technique for outcome prediction because of its high accuracy and ability to process...

Artificial intelligence accuracy in detecting pathological breath sounds in children using digital stethoscopes.

Respiratory research
BACKGROUND: Manual auscultation to detect abnormal breath sounds has poor inter-observer reliability. Digital stethoscopes with artificial intelligence (AI) could improve reliable detection of these sounds. We aimed to independently test the abilitie...

Machine Learning-Based Differentiation of Nontuberculous Mycobacteria Lung Disease and Pulmonary Tuberculosis Using CT Images.

BioMed research international
An increasing number of patients infected with nontuberculous mycobacteria (NTM) are observed worldwide. However, it is challenging to identify NTM lung diseases from pulmonary tuberculosis (PTB) due to considerable overlap in classic manifestations ...