AIMC Topic: Aged, 80 and over

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Pain Assessment Tool With Electrodermal Activity for Postoperative Patients: Method Validation Study.

JMIR mHealth and uHealth May 5, 2021
BACKGROUND: Accurate, objective pain assessment is required in the health care domain and clinical settings for appropriate pain management. Automated, objective pain detection from physiological data in patients provides valuable information to hosp...
Humans Machine Learning Algorithms Aged Aged, 80 and over Galvanic Skin Response Young Adult Adult Middle Aged Pain Measurement Pain
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A machine learning approach to screen for preclinical Alzheimer's disease.

Neurobiology of aging May 4, 2021
Combining multimodal biomarkers could help in the early diagnosis of Alzheimer's disease (AD). We included 304 cognitively normal individuals from the INSIGHT-preAD cohort. Amyloid and neurodegeneration were assessed on F-florbetapir and F-fluorodeox...
Neuropsychological Tests Apolipoprotein E4 Nerve Degeneration Magnetic Resonance Imaging Electroencephalography Alzheimer Disease Positron-Emission Tomography Aged, 80 and over Hippocampus Male Cohort Studies Genotype Mass Screening Follow-Up Studies Humans Machine Learning Biomarkers Female Aged
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Assessment of an AI Aid in Detection of Adult Appendicular Skeletal Fractures by Emergency Physicians and Radiologists: A Multicenter Cross-sectional Diagnostic Study.

Radiology May 4, 2021
Background The interpretation of radiographs suffers from an ever-increasing workload in emergency and radiology departments, while missed fractures represent up to 80% of diagnostic errors in the emergency department. Purpose To assess the performan...
Emergency Service, Hospital Radiologists Radiographic Image Interpretation, Computer-Assisted Reproducibility of Results Humans Aged Artificial Intelligence Sensitivity and Specificity Aged, 80 and over Physicians Cross-Sectional Studies Young Adult Retrospective Studies Male Adult Female Middle Aged Adolescent Fractures, Bone
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Projecting COVID-19 disease severity in cancer patients using purposefully-designed machine learning.

BMC infectious diseases May 4, 2021
BACKGROUND: Accurately predicting outcomes for cancer patients with COVID-19 has been clinically challenging. Numerous clinical variables have been retrospectively associated with disease severity, but the predictive value of these variables, and how...
COVID-19 Algorithms Prognosis Retrospective Studies Female Risk Factors Humans Machine Learning Comorbidity Severity of Illness Index ROC Curve Decision Support Systems, Clinical Area Under Curve New York City Aged Aged, 80 and over Male Middle Aged Neoplasms Respiration, Artificial
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White matter hyperintensities segmentation using the ensemble U-Net with multi-scale highlighting foregrounds.

NeuroImage May 3, 2021
White matter hyperintensities (WMHs) are abnormal signals within the white matter region on the human brain MRI and have been associated with aging processes, cognitive decline, and dementia. In the current study, we proposed a U-Net with multi-scale...
Female Aged Aged, 80 and over Male Aging Deep Learning Humans Leukoaraiosis Cognitive Dysfunction Magnetic Resonance Imaging Diagnosis, Differential Alzheimer Disease Neuroimaging
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Development and Validation of a Machine Learning Model Using Administrative Health Data to Predict Onset of Type 2 Diabetes.

JAMA network open May 3, 2021
IMPORTANCE: Systems-level barriers to diabetes care could be improved with population health planning tools that accurately discriminate between high- and low-risk groups to guide investments and targeted interventions.
Forecasting Retrospective Studies Electronic Health Records Female Aged Aged, 80 and over Young Adult Adult Male Middle Aged Risk Assessment Humans Machine Learning Algorithms Diabetes Mellitus, Type 2 Age of Onset Decision Making, Computer-Assisted Adolescent Incidence Cohort Studies Child Ontario
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Comparative Effectiveness of Machine Learning Approaches for Predicting Gastrointestinal Bleeds in Patients Receiving Antithrombotic Treatment.

JAMA network open May 3, 2021
IMPORTANCE: Anticipating the risk of gastrointestinal bleeding (GIB) when initiating antithrombotic treatment (oral antiplatelets or anticoagulants) is limited by existing risk prediction models. Machine learning algorithms may result in superior pre...
Venous Thromboembolism Antifibrinolytic Agents Gastrointestinal Hemorrhage Myocardial Ischemia Thienopyridines United States Predictive Value of Tests Fibrinolytic Agents Anticoagulants Atrial Fibrillation Clinical Decision-Making Female Aged Aged, 80 and over Young Adult Adult Cross-Sectional Studies Male Middle Aged Humans Risk Assessment Machine Learning Adolescent Retrospective Studies Cohort Studies
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Development of a Method for Clinical Evaluation of Artificial Intelligence-Based Digital Wound Assessment Tools.

JAMA network open May 3, 2021
IMPORTANCE: Accurate assessment of wound area and percentage of granulation tissue (PGT) are important for optimizing wound care and healing outcomes. Artificial intelligence (AI)-based wound assessment tools have the potential to improve the accurac...
Observer Variation Photography Software Design Granulation Tissue Artificial Intelligence Female Aged Aged, 80 and over Young Adult Adult Male Middle Aged Clinical Competence Adolescent Wound Healing Humans
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Prediction of the treatment outcome using machine learning with FDG-PET image-based multiparametric approach in patients with oral cavity squamous cell carcinoma.

Clinical radiology Apr 30, 2021
AIM: To investigate the value of machine learning-based multiparametric analysis using 2-[F]-fluoro-2-deoxy-d-glucose positron-emission tomography (FDG-PET) images to predict treatment outcome in patients with oral cavity squamous cell carcinoma (OCS...
Mouth Neoplasms Fluorodeoxyglucose F18 Aged, 80 and over Radiopharmaceuticals Adult Humans Male Mouth Machine Learning Middle Aged Positron-Emission Tomography Female Treatment Outcome Image Interpretation, Computer-Assisted Reproducibility of Results Squamous Cell Carcinoma of Head and Neck Aged
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Distinguishing retinal angiomatous proliferation from polypoidal choroidal vasculopathy with a deep neural network based on optical coherence tomography.

Scientific reports Apr 29, 2021
This cross-sectional study aimed to build a deep learning model for detecting neovascular age-related macular degeneration (AMD) and to distinguish retinal angiomatous proliferation (RAP) from polypoidal choroidal vasculopathy (PCV) using a convoluti...
Diagnosis, Differential Tomography, Optical Coherence Macular Degeneration Retinal Neovascularization Choroidal Neovascularization Polyploidy Cross-Sectional Studies Male Middle Aged Humans Case-Control Studies Neural Networks, Computer Cell Proliferation Female Aged Aged, 80 and over
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