AIMC Topic: Humans

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The Role of Data Science in Closing the Implementation Gap.

Critical care clinics
Data science has the potential to greatly enhance efforts to translate evidence into practice in critical care. The intensive care unit is a data-rich environment enabling insight into both patient-level care patterns and clinician-level treatment pa...

The Emerging Role of Artificial Intelligence in Valvular Heart Disease.

Heart failure clinics
Valvular heart disease (VHD) is a morbid condition in which timely identification and evidence-based treatments can lead to improved outcomes. Artificial intelligence broadly refers to the ability for computers to perform tasks and problem solve like...

Clinical metrics and tools for provider assessment and tracking of trigonocephaly.

Journal of neurosurgery. Pediatrics
OBJECTIVE: Quantitative measurements of trigonocephaly can be used to characterize and track this phenotype, which is associated with metopic craniosynostosis. Traditionally, trigonocephaly metrics were extracted from CT scans; however, this method e...

Learning Curve of Robot-Assisted Lymph Node Dissection of the Left Recurrent Laryngeal Nerve: A Retrospective Study of 417 Patients.

Annals of surgical oncology
OBJECTIVE: Left recurrent laryngeal nerve (no.106recL) lymph node dissection is a challenging procedure, and robotic-assisted minimally invasive esophagectomy (RAMIE) may have some advantages. This study aimed to determine the learning curve of no.10...

Coronary X-ray angiography segmentation using Artificial Intelligence: a multicentric validation study of a deep learning model.

The international journal of cardiovascular imaging
INTRODUCTION: We previously developed an artificial intelligence (AI) model for automatic coronary angiography (CAG) segmentation, using deep learning. To validate this approach, the model was applied to a new dataset and results are reported.

A unique color-coded visualization system with multimodal information fusion and deep learning in a longitudinal study of Alzheimer's disease.

Artificial intelligence in medicine
PURPOSE: Automated diagnosis and prognosis of Alzheimer's Disease remain a challenging problem that machine learning (ML) techniques have attempted to resolve in the last decade. This study introduces a first-of-its-kind color-coded visualization mec...

Artificial intelligence-powered software detected more than half of the liver metastases overlooked by radiologists on contrast-enhanced CT.

European journal of radiology
PURPOSE: To evaluate the sensitivity of artificial intelligence (AI)-powered software in detecting liver metastases, especially those overlooked by radiologists.

Rethinking causality-driven robot tool segmentation with temporal constraints.

International journal of computer assisted radiology and surgery
PURPOSE: Vision-based robot tool segmentation plays a fundamental role in surgical robots perception and downstream tasks. CaRTS, based on a complementary causal model, has shown promising performance in unseen counterfactual surgical environments in...

Deep learning prediction of motor performance in stroke individuals using neuroimaging data.

Journal of biomedical informatics
The degree of motor impairment and profile of recovery after stroke are difficult to predict for each individual. Measures obtained from clinical assessments, as well as neurophysiological and neuroimaging techniques have been used as potential bioma...

Machine and deep learning-based clinical characteristics and laboratory markers for the prediction of sarcopenia.

Chinese medical journal
BACKGROUND: Sarcopenia is an age-related progressive skeletal muscle disorder involving the loss of muscle mass or strength and physiological function. Efficient and precise AI algorithms may play a significant role in the diagnosis of sarcopenia. In...