AIMC Topic: Disease Progression

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Realistic simulation of virtual multi-scale, multi-modal patient trajectories using Bayesian networks and sparse auto-encoders.

Scientific reports Jul 3, 2020
Translational research of many disease areas requires a longitudinal understanding of disease development and progression across all biologically relevant scales. Several corresponding studies are now available. However, to compile a comprehensive pi...
Humans Databases, Factual Brain Computer Simulation Disease Progression Parkinson Disease Models, Statistical Bayes Theorem Translational Research, Biomedical Alzheimer Disease Deep Learning User-Computer Interface Cohort Studies Polymorphism, Single Nucleotide Longitudinal Studies
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Age-related Macular Degeneration: Nutrition, Genes and Deep Learning-The LXXVI Edward Jackson Memorial Lecture.

American journal of ophthalmology Jun 20, 2020
PURPOSE: To evaluate the importance of nutritional supplements, dietary pattern, and genetic associations in age-related macular degeneration (AMD); and to discuss the technique of artificial intelligence/deep learning to potentially enhance research...
Macular Degeneration Deep Learning Dietary Supplements Visual Acuity Genetic Testing Humans Disease Progression
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Serum adipokines/related inflammatory factors and ratios as predictors of infrapatellar fat pad volume in osteoarthritis: Applying comprehensive machine learning approaches.

Scientific reports Jun 19, 2020
OBJECTIVE: The infrapatellar fat pad (IPFP) has been associated with knee osteoarthritis onset and progression. This study uses machine learning (ML) approaches to predict serum levels of some adipokines/related inflammatory factors and their ratios ...
Aged Male Middle Aged Disease Progression Magnetic Resonance Imaging Adipose Tissue Knee Joint Adipokines Osteoarthritis, Knee Humans Machine Learning Female Reproducibility of Results
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Machine Learning of 12-Lead QRS Waveforms to Identify Cardiac Resynchronization Therapy Patients With Differential Outcomes.

Circulation. Arrhythmia and electrophysiology Jun 14, 2020
BACKGROUND: Cardiac resynchronization therapy (CRT) improves heart failure outcomes but has significant nonresponse rates, highlighting limitations in ECG selection criteria: QRS duration (QRSd) ≥150 ms and subjective labeling of left bundle branch b...
Male Ventricular Function, Left Stroke Volume Middle Aged Risk Assessment Retrospective Studies Female Risk Factors Aged Unsupervised Machine Learning Recovery of Function Predictive Value of Tests Signal Processing, Computer-Assisted Heart Failure Disease Progression Diagnosis, Computer-Assisted Time Factors Electrocardiography Treatment Outcome Humans Cardiac Resynchronization Therapy Heart-Assist Devices Heart Transplantation Bundle-Branch Block
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Artificial intelligence for interpretation of segments of whole body MRI in CNO: pilot study comparing radiologists versus machine learning algorithm.

Pediatric rheumatology online journal Jun 9, 2020
BACKGROUND: To initiate the development of a machine learning algorithm capable of comparing segments of pre and post pamidronate whole body MRI scans to assess treatment response and to compare the results of this algorithm with the analysis of a pa...
Femur Whole Body Imaging Osteitis Pamidronate Pilot Projects Humans Adolescent Chronic Disease Artificial Intelligence Treatment Outcome Radiologists Machine Learning Support Vector Machine Image Interpretation, Computer-Assisted Sensitivity and Specificity Magnetic Resonance Imaging Tibia Disease Progression Bone Density Conservation Agents
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Using machine learning models to predict the initiation of renal replacement therapy among chronic kidney disease patients.

PloS one Jun 5, 2020
Starting renal replacement therapy (RRT) for patients with chronic kidney disease (CKD) at an optimal time, either with hemodialysis or kidney transplantation, is crucial for patient's well-being and for successful management of the condition. In thi...
Taiwan Models, Biological Datasets as Topic Time-to-Treatment Humans Severity of Illness Index Retrospective Studies Machine Learning Time Factors Renal Replacement Therapy Clinical Decision-Making ROC Curve Disease Progression Renal Insufficiency, Chronic Comorbidity
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AD-NET: Age-adjust neural network for improved MCI to AD conversion prediction.

NeuroImage. Clinical Jun 1, 2020
The prediction of Mild Cognitive Impairment (MCI) patients who are at higher risk converting to Alzheimer's Disease (AD) is critical for effective intervention and patient selection in clinical trials. Different biomarkers including neuroimaging have...
Pattern Recognition, Automated Neuroimaging Cognitive Dysfunction Age Factors Predictive Value of Tests Humans Alzheimer Disease Neural Networks, Computer Image Interpretation, Computer-Assisted Brain Disease Progression
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Multi-classifier prediction of knee osteoarthritis progression from incomplete imbalanced longitudinal data.

Scientific reports May 21, 2020
Conventional inclusion criteria used in osteoarthritis clinical trials are not very effective in selecting patients who would benefit from a therapy being tested. Typically majority of selected patients show no or limited disease progression during a...
Humans Osteoarthritis, Knee Machine Learning Bone and Bones Disease Progression Models, Theoretical Patient Selection Cartilage
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Pulmonary Embolism in Acute Asthma Exacerbation: Clinical Characteristics, Prediction Model and Hospital Outcomes.

Lung May 18, 2020
PURPOSE: Little is known about the characteristics and impact of acute pulmonary embolism (PE) during episodes of asthma exacerbation. We aimed to characterize patients diagnosed with acute PE in the setting of asthma exacerbation, develop a predicti...
Natriuretic Peptide, Brain Fibrin Fibrinogen Degradation Products Computed Tomography Angiography Clinical Decision Rules Humans International Normalized Ratio Case-Control Studies Asthma Pulmonary Embolism Male Length of Stay Machine Learning Creatinine Middle Aged Body Mass Index Intensive Care Units Hospitals, University Disease Progression Oxygen Female Heart Rate Comorbidity Aged Adult
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Predicting conversion to wet age-related macular degeneration using deep learning.

Nature medicine May 18, 2020
Progression to exudative 'wet' age-related macular degeneration (exAMD) is a major cause of visual deterioration. In patients diagnosed with exAMD in one eye, we introduce an artificial intelligence (AI) system to predict progression to exAMD in the ...
Deep Learning Imaging, Three-Dimensional Humans Female Wet Macular Degeneration Aged Tomography, Optical Coherence Prognosis Aged, 80 and over Macular Degeneration Early Medical Intervention Male Early Diagnosis Geographic Atrophy Disease Progression
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