AIMC Topic: Feasibility Studies

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Large Vessel Occlusion Prediction in the Emergency Department with National Institutes of Health Stroke Scale Components: A Machine Learning Approach.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
OBJECTIVE: To determine the feasibility of using a machine learning algorithm to screen for large vessel occlusions (LVO) in the Emergency Department (ED).

Shape-Sensing Robotic-Assisted Bronchoscopy in the Diagnosis of Pulmonary Parenchymal Lesions.

Chest
BACKGROUND: The landscape of guided bronchoscopy for the sampling of pulmonary parenchymal lesions is evolving rapidly. Shape-sensing robotic-assisted bronchoscopy (ssRAB) recently was introduced as means to allow successful sampling of traditionally...

Patterns of Metastatic Disease in Patients with Cancer Derived from Natural Language Processing of Structured CT Radiology Reports over a 10-year Period.

Radiology
Background Patterns of metastasis in cancer are increasingly relevant to prognostication and treatment planning but have historically been documented by means of autopsy series. Purpose To show the feasibility of using natural language processing (NL...

Assessing the Adequacy of Hemodialysis Patients via the Graph-Based Takagi-Sugeno-Kang Fuzzy System.

Computational and mathematical methods in medicine
Maintenance hemodialysis is the main method for the treatment of end-stage renal disease in China. The / value is the gold standard of hemodialysis adequacy. However, / requires repeated blood drawing and evaluation; it is hard to monitor dialysis ad...

Machine learning analysis of multispectral imaging and clinical risk factors to predict amputation wound healing.

Journal of vascular surgery
OBJECTIVE: Prediction of amputation wound healing is challenging due to the multifactorial nature of critical limb ischemia and lack of objective assessment tools. Up to one-third of amputations require revision to a more proximal level within 1 year...

A feasibility study on deep learning-based individualized 3D dose distribution prediction.

Medical physics
PURPOSE: Radiation therapy treatment planning is a trial-and-error, often time-consuming process. An approximately optimal dose distribution corresponding to a specific patient's anatomy can be predicted by using pre-trained deep learning (DL) models...

Exploring Feasibility of Multivariate Deep Learning Models in Predicting COVID-19 Epidemic.

Frontiers in public health
Mathematical models are powerful tools to study COVID-19. However, one fundamental challenge in current modeling approaches is the lack of accurate and comprehensive data. Complex epidemiological systems such as COVID-19 are especially challenging t...

A fusion decision system to identify and grade malnutrition in cancer patients: Machine learning reveals feasible workflow from representative real-world data.

Clinical nutrition (Edinburgh, Scotland)
BACKGROUND AND AIMS: Most nutritional assessment tools are based on pre-defined questionnaires or consensus guidelines. However, it has been postulated that population data can be used directly to develop a solution for assessing malnutrition. This s...

Using NLP in openEHR archetypes retrieval to promote interoperability: a feasibility study in China.

BMC medical informatics and decision making
BACKGROUND: With the development and application of medical information system, semantic interoperability is essential for accurate and advanced health-related computing and electronic health record (EHR) information sharing. The openEHR approach can...

Artificial intelligence for quality assurance in radiotherapy.

Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique
In radiotherapy, patient-specific quality assurance is very time-consuming and causes machine downtime. It consists of testing (using measurement with a phantom and detector) if a modulated plan is correctly delivered by a treatment unit. Artificial ...