AIMC Topic: Aged, 80 and over

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Automated estimation of echocardiogram image quality in hospitalized patients.

The international journal of cardiovascular imaging Nov 19, 2020
We developed a machine learning model for efficient analysis of echocardiographic image quality in hospitalized patients. This study applied a machine learning model for automated transthoracic echo (TTE) image quality scoring in three inpatient grou...
Automation Male Humans Reproducibility of Results Inpatients Echocardiography Machine Learning Hospitalization Predictive Value of Tests Middle Aged Female Aged Respiration, Artificial Feasibility Studies Aged, 80 and over Case-Control Studies Video Recording Adult Image Interpretation, Computer-Assisted
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Deep learning to predict elevated pulmonary artery pressure in patients with suspected pulmonary hypertension using standard chest X ray.

Scientific reports Nov 17, 2020
Accurate diagnosis of pulmonary hypertension (PH) is crucial to ensure that patients receive timely treatment. We hypothesized that application of artificial intelligence (AI) to the chest X-ray (CXR) could identify elevated pulmonary artery pressure...
Pulmonary Artery Deep Learning Area Under Curve Heart Failure Hypertension, Pulmonary Radiography, Thoracic Aged Humans Aged, 80 and over Neural Networks, Computer Male Algorithms Middle Aged Retrospective Studies Diagnosis, Computer-Assisted Female
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Type IV Collagen 7S Is the Most Accurate Test For Identifying Advanced Fibrosis in NAFLD With Type 2 Diabetes.

Hepatology communications Nov 16, 2020
This study aimed to examine whether the diagnostic accuracy of four noninvasive tests (NITs) for detecting advanced fibrosis in nonalcoholic fatty liver disease (NAFLD) is maintained or is inferior to with or without the presence of type 2 diabetes. ...
ROC Curve Diabetes Mellitus, Type 2 Adult Liver Cirrhosis Cross-Sectional Studies Collagen Type IV Age Factors Male Dyslipidemias Humans Non-alcoholic Fatty Liver Disease Middle Aged Platelet Count Female Adolescent Aged Aged, 80 and over Young Adult
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Development of a machine learning algorithm to predict intubation among hospitalized patients with COVID-19.

Journal of critical care Nov 16, 2020
PURPOSE: The purpose of this study is to develop a machine learning algorithm to predict future intubation among patients diagnosed or suspected with COVID-19.
Prognosis Retrospective Studies Female SARS-CoV-2 Hospitalization Aged Humans COVID-19 Algorithms Cohort Studies Predictive Value of Tests Area Under Curve Supervised Machine Learning New York City Intubation, Intratracheal Aged, 80 and over Adult Male Middle Aged Respiration, Artificial
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Visualizing "featureless" regions on mammograms classified as invasive ductal carcinomas by a deep learning algorithm: the promise of AI support in radiology.

Japanese journal of radiology Nov 16, 2020
PURPOSE: To demonstrate how artificial intelligence (AI) can expand radiologists' capacity, we visualized the features of invasive ductal carcinomas (IDCs) that our algorithm, developed and validated for basic pathological classification on mammogram...
Humans Algorithms Female Aged Aged, 80 and over Adult Carcinoma, Ductal, Breast Breast Neoplasms Middle Aged Deep Learning Mammography
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Using Auto-Segmentation to Reduce Contouring and Dose Inconsistency in Clinical Trials: The Simulated Impact on RTOG 0617.

International journal of radiation oncology, biology, physics Nov 13, 2020
PURPOSE: Contouring inconsistencies are known but understudied in clinical radiation therapy trials. We applied auto-contouring to the Radiation Therapy Oncology Group (RTOG) 0617 dose escalation trial data. We hypothesized that the trial heart doses...
Deep Learning Lung Neoplasms Carcinoma, Non-Small-Cell Lung Heart Organs at Risk Aged Confidence Intervals Aged, 80 and over Linear Models Adult Proportional Hazards Models Male Radiotherapy Dosage Humans Middle Aged Clinical Trials, Phase III as Topic Algorithms Statistics, Nonparametric Tomography, X-Ray Computed Female
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Artificial Intelligence-Electrocardiography to Predict Incident Atrial Fibrillation: A Population-Based Study.

Circulation. Arrhythmia and electrophysiology Nov 13, 2020
BACKGROUND: An artificial intelligence (AI) algorithm applied to electrocardiography during sinus rhythm has recently been shown to detect concurrent episodic atrial fibrillation (AF). We sought to characterize the value of AI-enabled electrocardiogr...
Action Potentials Predictive Value of Tests Signal Processing, Computer-Assisted Heart Conduction System Atrial Fibrillation Heart Rate Aged, 80 and over Male Risk Assessment Incidence Electrocardiography Humans Machine Learning Female Risk Factors Aged
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Early and accurate detection and diagnosis of heart disease using intelligent computational model.

Scientific reports Nov 12, 2020
Heart disease is a fatal human disease, rapidly increases globally in both developed and undeveloped countries and consequently, causes death. Normally, in this disease, the heart fails to supply a sufficient amount of blood to other parts of the bod...
ROC Curve Computational Biology Follow-Up Studies Models, Statistical Heart Diseases Aged Aged, 80 and over Adult Male Middle Aged Humans Artificial Intelligence Machine Learning Prognosis Female
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Machine learning approaches for sex estimation using cranial measurements.

International journal of legal medicine Nov 11, 2020
The aim of the present study is to apply support vector machines (SVM) and artificial neural network (ANN) as sex classifiers and to generate useful classification models for sex estimation based on cranial measurements. Besides, the performance of t...
Aged, 80 and over Adult Male Middle Aged Adolescent Logistic Models Humans Neural Networks, Computer Female Reproducibility of Results Aged Skull Support Vector Machine Bulgaria Anatomic Landmarks Cephalometry Datasets as Topic Sex Determination by Skeleton
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Differentiation of low and high grade renal cell carcinoma on routine MRI with an externally validated automatic machine learning algorithm.

Scientific reports Nov 11, 2020
Pre-treatment determination of renal cell carcinoma aggressiveness may help guide clinical decision-making. We aimed to differentiate low-grade (Fuhrman I-II) from high-grade (Fuhrman III-IV) renal cell carcinoma using radiomics features extracted fr...
Kidney Neoplasms Carcinoma, Renal Cell Female Aged Humans Middle Aged Aged, 80 and over Machine Learning ROC Curve Adult Retrospective Studies Magnetic Resonance Imaging Male Diagnosis, Differential Bayes Theorem Neoplasm Grading
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