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Pyramid feature adaptation for semi-supervised cardiac bi-ventricle segmentation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Cardiac bi-ventricle segmentation (BVS) is an essential task for assessing cardiac indices, such as the ejection fraction and volume of the left ventricle (LV) and right ventricle (RV). However, BVS is extremely challenging due to the high variabilit...

Deep learning risk assessment models for predicting progression of radiographic medial joint space loss over a 48-MONTH follow-up period.

Osteoarthritis and cartilage
OBJECTIVE: To develop and evaluate deep learning (DL) risk assessment models for predicting the progression of radiographic medial joint space loss using baseline knee X-rays.

Predicting 10-Year Risk of End-Organ Complications of Type 2 Diabetes With and Without Metabolic Surgery: A Machine Learning Approach.

Diabetes care
OBJECTIVE: To construct and internally validate prediction models to estimate the risk of long-term end-organ complications and mortality in patients with type 2 diabetes and obesity that can be used to inform treatment decisions for patients and pra...

Imaging-Based Algorithm for the Local Grading of Glioma.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Gliomas are highly heterogeneous tumors, and optimal treatment depends on identifying and locating the highest grade disease present. Imaging techniques for doing so are generally not validated against the histopathologic crit...

The Design of CNN Architectures for Optimal Six Basic Emotion Classification Using Multiple Physiological Signals.

Sensors (Basel, Switzerland)
This study aimed to design an optimal emotion recognition method using multiple physiological signal parameters acquired by bio-signal sensors for improving the accuracy of classifying individual emotional responses. Multiple physiological signals su...

Machine learning algorithm to predict mortality in patients undergoing continuous renal replacement therapy.

Critical care (London, England)
BACKGROUND: Previous scoring models such as the Acute Physiologic Assessment and Chronic Health Evaluation II (APACHE II) and the Sequential Organ Failure Assessment (SOFA) scoring systems do not adequately predict mortality of patients undergoing co...

Predicting patient outcomes in psychiatric hospitals with routine data: a machine learning approach.

BMC medical informatics and decision making
BACKGROUND: A common problem in machine learning applications is availability of data at the point of decision making. The aim of the present study was to use routine data readily available at admission to predict aspects relevant to the organization...

A quantitative model based on clinically relevant MRI features differentiates lower grade gliomas and glioblastoma.

European radiology
OBJECTIVES: To establish a quantitative MR model that uses clinically relevant features of tumor location and tumor volume to differentiate lower grade glioma (LRGG, grades II and III) and glioblastoma (GBM, grade IV).

Diagnosing schizophrenia with network analysis and a machine learning method.

International journal of methods in psychiatric research
OBJECTIVE: Schizophrenia is a chronic and debilitating neuropsychiatric disorder. It has been suggested that impaired brain connectivity underlies the pathophysiology of schizophrenia. Network analysis has thus recently emerged in the field of schizo...