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Within node connectivity changes, not simply edge changes, influence graph theory measures in functional connectivity studies of the brain.

NeuroImage
Interest in understanding the organization of the brain has led to the application of graph theory methods across a wide array of functional connectivity studies. The fundamental basis of a graph is the node. Recent work has shown that functional nod...

Esophagus Segmentation in CT Images via Spatial Attention Network and STAPLE Algorithm.

Sensors (Basel, Switzerland)
One essential step in radiotherapy treatment planning is the organ at risk of segmentation in Computed Tomography (CT). Many recent studies have focused on several organs such as the lung, heart, esophagus, trachea, liver, aorta, kidney, and prostate...

A machine learning based exploration of COVID-19 mortality risk.

PloS one
Early prediction of patient mortality risks during a pandemic can decrease mortality by assuring efficient resource allocation and treatment planning. This study aimed to develop and compare prognosis prediction machine learning models based on invas...

Deep learning segmentation of transverse musculoskeletal ultrasound images for neuromuscular disease assessment.

Computers in biology and medicine
Ultrasound imaging is a patient-friendly and robust technique for studying physiological and pathological muscles. An automatic deep learning (DL) system for the analysis of ultrasound images could be useful to support an expert operator, allowing th...

Robotic salvage pelvic lymph node dissection for locoregional recurrence after radical prostatectomy: a single institution experience.

Scandinavian journal of urology
OBJECTIVES: To assess treatment response (PSA < 0.2 ng/ml), need for additional therapy and complication rate after robot assisted salvage pelvic lymph node dissection (sPLND).

Development and Validation of an Artificial Intelligence System to Optimize Clinician Review of Patient Records.

JAMA network open
IMPORTANCE: Physicians are required to work with rapidly growing amounts of medical data. Approximately 62% of time per patient is devoted to reviewing electronic health records (EHRs), with clinical data review being the most time-consuming portion.

Performance of a Machine Learning Algorithm Using Electronic Health Record Data to Identify and Estimate Survival in a Longitudinal Cohort of Patients With Lung Cancer.

JAMA network open
IMPORTANCE: Electronic health records (EHRs) provide a low-cost means of accessing detailed longitudinal clinical data for large populations. A lung cancer cohort assembled from EHR data would be a powerful platform for clinical outcome studies.