AIMC Topic: Prospective Studies
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Diagnostic Test Accuracy of Deep Learning Detection of COVID-19: A Systematic Review and Meta-Analysis.
Academic radiology
Sep 17, 2021
RATIONALE AND OBJECTIVE: To perform a meta-analysis to compare the diagnostic test accuracy (DTA) of deep learning (DL) in detecting coronavirus disease 2019 (COVID-19), and to investigate how network architecture and type of datasets affect DL perfo...
Multi-step validation of a deep learning-based system for the quantification of bowel preparation: a prospective, observational study.
The Lancet. Digital health
Sep 16, 2021
BACKGROUND: Inadequate bowel preparation is associated with a decrease in adenoma detection rate (ADR). A deep learning-based bowel preparation assessment system based on the Boston bowel preparation scale (BBPS) has been previously established to ca...
Deep Learning Computer-aided Polyp Detection Reduces Adenoma Miss Rate: A United States Multi-center Randomized Tandem Colonoscopy Study (CADeT-CS Trial).
Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association
Sep 14, 2021
BACKGROUND & AIMS: Artificial intelligence-based computer-aided polyp detection (CADe) systems are intended to address the issue of missed polyps during colonoscopy. The effect of CADe during screening and surveillance colonoscopy has not previously ...
Deep Learning Predicts Interval and Screening-detected Cancer from Screening Mammograms: A Case-Case-Control Study in 6369 Women.
Radiology
Sep 7, 2021
Background The ability of deep learning (DL) models to classify women as at risk for either screening mammography-detected or interval cancer (not detected at mammography) has not yet been explored in the literature. Purpose To examine the ability of...
Machine learning approaches to predict gestational age in normal and complicated pregnancies via urinary metabolomics analysis.
Scientific reports
Sep 7, 2021
The elucidation of dynamic metabolomic changes during gestation is particularly important for the development of methods to evaluate pregnancy status or achieve earlier detection of pregnancy-related complications. Some studies have constructed model...
Machine learning guided postnatal gestational age assessment using new-born screening metabolomic data in South Asia and sub-Saharan Africa.
BMC pregnancy and childbirth
Sep 7, 2021
BACKGROUND: Babies born early and/or small for gestational age in Low and Middle-income countries (LMICs) contribute substantially to global neonatal and infant mortality. Tracking this metric is critical at a population level for informed policy, ad...
Applying interpretable deep learning models to identify chronic cough patients using EHR data.
Computer methods and programs in biomedicine
Sep 4, 2021
BACKGROUND AND OBJECTIVE: Chronic cough (CC) affects approximately 10% of adults. Many disease states are associated with chronic cough, such as asthma, upper airway cough syndrome, bronchitis, and gastroesophageal reflux disease. The lack of an ICD ...
A machine learning-based biological aging prediction and its associations with healthy lifestyles: the Dongfeng-Tongji cohort.
Annals of the New York Academy of Sciences
Sep 3, 2021
This study aims to establish a biological age (BA) predictor and to investigate the roles of lifestyles on biological aging. The 14,848 participants with the available information of multisystem measurements from the Dongfeng-Tongji cohort were used ...
Identifying the predictive effectiveness of a genetic risk score for incident hypertension using machine learning methods among populations in rural China.
Hypertension research : official journal of the Japanese Society of Hypertension
Sep 3, 2021
Current studies have shown the controversial effect of genetic risk scores (GRSs) in hypertension prediction. Machine learning methods are used extensively in the medical field but rarely in the mining of genetic information. This study aims to deter...
Artificial intelligence-assisted colonoscopy: A prospective, multicenter, randomized controlled trial of polyp detection.
Cancer medicine
Sep 3, 2021
BACKGROUND: Artificial intelligence (AI) assistance has been considered as a promising way to improve colonoscopic polyp detection, but there are limited prospective studies on real-time use of AI systems.
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