AIMC Topic: Disease Progression

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Histopathology-validated machine learning radiographic biomarker for noninvasive discrimination between true progression and pseudo-progression in glioblastoma.

Cancer Mar 4, 2020
BACKGROUND: Imaging of glioblastoma patients after maximal safe resection and chemoradiation commonly demonstrates new enhancements that raise concerns about tumor progression. However, in 30% to 50% of patients, these enhancements primarily represen...
Humans Machine Learning Glioblastoma Brain Neoplasms Male Female Middle Aged Aged Disease Progression Aged, 80 and over Magnetic Resonance Imaging Adult Biomarkers, Tumor
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Machine learning-based motor assessment of Parkinson's disease using postural sway, gait and lifestyle features on crowdsourced smartphone data.

Biomedical physics & engineering express Mar 4, 2020
OBJECTIVES: Remote assessment of gait in patients' homes has become a valuable tool for monitoring the progression of Parkinson's disease (PD). However, these measurements are often not as accurate or reliable as clinical evaluations because it is ch...
Wearable Electronic Devices Smartphone Posture Parkinson Disease Area Under Curve Accelerometry Humans Postural Balance Aged Life Style Machine Learning Remote Sensing Technology Motor Skills Male Female Walking Crowdsourcing Middle Aged Reproducibility of Results Gait Tremor Disease Progression Pattern Recognition, Automated Diagnosis, Computer-Assisted
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Machine Learning Framework to Identify Individuals at Risk of Rapid Progression of Coronary Atherosclerosis: From the PARADIGM Registry.

Journal of the American Heart Association Feb 22, 2020
Background Rapid coronary plaque progression (RPP) is associated with incident cardiovascular events. To date, no method exists for the identification of individuals at risk of RPP at a single point in time. This study integrated coronary computed to...
Coronary Artery Disease Multidetector Computed Tomography Humans Plaque, Atherosclerotic Machine Learning Time Factors Computed Tomography Angiography Aged Prospective Studies Predictive Value of Tests Coronary Angiography Male Female Radiographic Image Interpretation, Computer-Assisted Middle Aged Registries Disease Progression Diagnosis, Computer-Assisted
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A Machine-learning Approach to Forecast Aggravation Risk in Patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease with Clinical Indicators.

Scientific reports Feb 20, 2020
Patients with chronic obstructive pulmonary disease (COPD) repeat acute exacerbations (AE). Global Initiative for Chronic Obstructive Lung Disease (GOLD) is only available for patients in stable phase. Currently, there is a lack of assessment and pre...
Hospitalization Aged Aged, 80 and over Adult Pulmonary Disease, Chronic Obstructive Male Middle Aged Disease Progression Comorbidity ROC Curve Machine Learning Algorithms Prognosis Female Reproducibility of Results Humans Decision Support Systems, Clinical Risk Inflammation Models, Statistical Decision Trees False Positive Reactions
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Measuring the impact of age, gender and dementia on communication-robot interventions in residential care homes.

Geriatrics & gerontology international Feb 19, 2020
AIM: The primary aim of this study was to examine the impact of age, gender and the stage of dementia on the results of an assistive technology intervention that make use of communication robots (com-robots). The intervention was designed to improve ...
Sex Factors Nursing Homes Self-Help Devices Homes for the Aged Aged, 80 and over Humans Dementia Communication Delivery of Health Care Cohort Studies Female Male Age Factors Aged Quality of Life Robotics Disease Progression Japan
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Predicting individual clinical trajectories of depression with generative embedding.

NeuroImage. Clinical Feb 17, 2020
Patients with major depressive disorder (MDD) show heterogeneous treatment response and highly variable clinical trajectories: while some patients experience swift recovery, others show relapsing-remitting or chronic courses. Predicting individual cl...
Brain Mapping Middle Aged Humans Disease Progression Image Interpretation, Computer-Assisted Prognosis Depressive Disorder, Major Female Support Vector Machine Adult Magnetic Resonance Imaging Male
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A Deep Learning Model for Segmentation of Geographic Atrophy to Study Its Long-Term Natural History.

Ophthalmology Feb 15, 2020
PURPOSE: To develop and validate a deep learning model for the automatic segmentation of geographic atrophy (GA) using color fundus images (CFIs) and its application to study the growth rate of GA.
Geographic Atrophy Humans Deep Learning Follow-Up Studies Forecasting Fundus Oculi Prospective Studies Retina Female Fluorescein Angiography Aged Male Disease Progression Severity of Illness Index
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Model to Predict Progression of Liver Disease in Heavy Drinkers Is Useful Today and Supports the Future of Deep Learning.

Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association Feb 13, 2020
Humans Disease Progression Deep Learning Alcoholism Liver Diseases
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Prognostic value of anthropometric measures extracted from whole-body CT using deep learning in patients with non-small-cell lung cancer.

European radiology Feb 13, 2020
INTRODUCTION: The aim of the study was to extract anthropometric measures from CT by deep learning and to evaluate their prognostic value in patients with non-small-cell lung cancer (NSCLC).
Subcutaneous Fat Whole Body Imaging Body Composition Progression-Free Survival Proportional Hazards Models Body Surface Area Intra-Abdominal Fat Survival Rate Male Middle Aged Disease Progression Deep Learning Lung Neoplasms Carcinoma, Non-Small-Cell Lung Muscle, Skeletal Positron Emission Tomography Computed Tomography Neoplasm Staging Prognosis Female Reproducibility of Results Aged Adult Humans
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Predicting sporadic Alzheimer's disease progression via inherited Alzheimer's disease-informed machine-learning.

Alzheimer's & dementia : the journal of the Alzheimer's Association Feb 11, 2020
INTRODUCTION: Developing cross-validated multi-biomarker models for the prediction of the rate of cognitive decline in Alzheimer's disease (AD) is a critical yet unmet clinical challenge.
Humans Machine Learning Biomarkers Magnetic Resonance Imaging Alzheimer Disease Positron-Emission Tomography Female Adult Male Disease Progression Cognitive Dysfunction
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