AIMC Topic: Prospective Studies
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Adoption of New Technologies: Artificial Intelligence.
Gastrointestinal endoscopy clinics of North America
Jul 20, 2021
Over the past decade, artificial intelligence (AI) has been broadly applied to many aspects of human life, with recent groundbreaking successes in facial recognition, natural language processing, autonomous driving, and medical imaging. Gastroenterol...
Deep-learning based super-resolution for 3D isotropic coronary MR angiography in less than a minute.
Magnetic resonance in medicine
Jul 9, 2021
PURPOSE: To develop and evaluate a novel and generalizable super-resolution (SR) deep-learning framework for motion-compensated isotropic 3D coronary MR angiography (CMRA), which allows free-breathing acquisitions in less than a minute.
Sex Differences in the Association Between Inflammation and Event-Free Survival in Patients With Heart Failure.
The Journal of cardiovascular nursing
Jul 8, 2021
BACKGROUND: Heart failure (HF) is associated with chronic inflammation, which is adversely associated with survival. Although sex-related differences in inflammation have been described in patients with HF, whether sex-related differences in inflamma...
Deep learning system compared with expert endoscopists in predicting early gastric cancer and its invasion depth and differentiation status (with videos).
Gastrointestinal endoscopy
Jul 7, 2021
BACKGROUND AND AIMS: We aimed to develop and validate a deep learning-based system that covers various aspects of early gastric cancer (EGC) diagnosis, including detecting gastric neoplasm, identifying EGC, and predicting EGC invasion depth and diffe...
Automated Measurements of Body Composition in Abdominal CT Scans Using Artificial Intelligence Can Predict Mortality in Patients With Cirrhosis.
Hepatology communications
Jul 7, 2021
Body composition measures derived from already available electronic medical records (computed tomography [CT] scans) can have significant value, but automation of measurements is needed for clinical implementation. We sought to use artificial intelli...
3D Deep Learning Model for the Pretreatment Evaluation of Treatment Response in Esophageal Carcinoma: A Prospective Study (ChiCTR2000039279).
International journal of radiation oncology, biology, physics
Jul 3, 2021
PURPOSE: To develop and validate a pretreatment computed tomography (CT)-based deep-learning (DL) model for predicting the treatment response to concurrent chemoradiation therapy (CCRT) among patients with locally advanced thoracic esophageal squamou...
Self-Supervised Feature Learning and Phenotyping for Assessing Age-Related Macular Degeneration Using Retinal Fundus Images.
Ophthalmology. Retina
Jul 2, 2021
OBJECTIVE: Diseases such as age-related macular degeneration (AMD) are classified based on human rubrics that are prone to bias. Supervised neural networks trained using human-generated labels require labor-intensive annotations and are restricted to...
Artificial Intelligence for Interstitial Lung Disease Analysis on Chest Computed Tomography: A Systematic Review.
Academic radiology
Jul 1, 2021
RATIONALE AND OBJECTIVES: High-resolution computed tomography (HRCT) is paramount in the assessment of interstitial lung disease (ILD). Yet, HRCT interpretation of ILDs may be hampered by inter- and intra-observer variability. Recently, artificial in...
Evaluation of deep learning-based artificial intelligence techniques for breast cancer detection on mammograms: Results from a retrospective study using a BreastScreen Victoria dataset.
Journal of medical imaging and radiation oncology
Jul 1, 2021
INTRODUCTION: This study aims to evaluate deep learning (DL)-based artificial intelligence (AI) techniques for detecting the presence of breast cancer on a digital mammogram image.
Development and Validation of an Artificial Intelligence System to Optimize Clinician Review of Patient Records.
JAMA network open
Jul 1, 2021
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.
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