AI Medical Compendium Topic

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Cohort Studies

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Automated Deep Learning-Based Segmentation of Abdominal Adipose Tissue on Dixon MRI in Adolescents: A Prospective Population-Based Study.

AJR. American journal of roentgenology
The prevalence of childhood obesity has increased significantly worldwide, highlighting a need for accurate noninvasive quantification of body fat distribution in children. The purpose of this study was to develop and test an automated deep learnin...

Prediction of Retear After Arthroscopic Rotator Cuff Repair Based on Intraoperative Arthroscopic Images Using Deep Learning.

The American journal of sports medicine
BACKGROUND: It is challenging to predict retear after arthroscopic rotator cuff repair (ARCR). The usefulness of arthroscopic intraoperative images as predictors of the ARCR prognosis has not been analyzed.

Machine learning models of healthcare expenditures predicting mortality: A cohort study of spousal bereaved Danish individuals.

PloS one
BACKGROUND: The ability to accurately predict survival in older adults is crucial as it guides clinical decision making. The added value of using health care usage for predicting mortality remains unexplored. The aim of this study was to investigate ...

Prediction of gestational diabetes mellitus at the first trimester: machine-learning algorithms.

Archives of gynecology and obstetrics
PURPOSE: Short- and long-term complications of gestational diabetes mellitus (GDM) involving pregnancies and offspring warrant the development of an effective individualized risk prediction model to reduce and prevent GDM together with its associated...

Biochemical recurrence after chemohormonal therapy followed by robot-assisted radical prostatectomy in very-high-risk prostate cancer patients.

Journal of robotic surgery
Robot-assisted radical prostatectomy (RARP) has become one of the standard radical treatments for prostate cancer (PCa). A retrospective single-center cohort study was conducted on patients with PCa who underwent RARP at Gifu University Hospital betw...

A cost-effective model for training in Robot-Assisted Sacrocolpopexy.

International urogynecology journal
BACKGROUND: The number of robotically assisted sacrocolpopexy procedures are increasing; therefore, experienced clinicians are needed. Simulation-based cadaver models are challenging in aspects of cost and availability. Therefore, we need to look at ...

An Acoustical and Lexical Machine-Learning Pipeline to Identify .

Journal of palliative medicine
Developing scalable methods for conversation analytics is essential for health care communication science and quality improvement. To assess the feasibility of automating the identification of a conversational feature, which is associated with imp...

Modeling Epidemiology Data with Machine Learning Technique to Detect Risk Factors for Gastric Cancer.

Journal of gastrointestinal cancer
PURPOSE: Gastric cancer (GC) ranks as the 7th most common cancer worldwide and a leading cause of cancer mortality. In Iran, stomach malignancies are the most common fatal cancers with higher than world average incidence. In recent years, methods lik...

Machine learning to predict poor school performance in paediatric survivors of intensive care: a population-based cohort study.

Intensive care medicine
PURPOSE: Whilst survival in paediatric critical care has improved, clinicians lack tools capable of predicting long-term outcomes. We developed a machine learning model to predict poor school outcomes in children surviving intensive care unit (ICU).

Automatic retinoblastoma screening and surveillance using deep learning.

British journal of cancer
BACKGROUND: Retinoblastoma is the most common intraocular malignancy in childhood. With the advanced management strategy, the globe salvage and overall survival have significantly improved, which proposes subsequent challenges regarding long-term sur...