AI Medical Compendium Topic:
Disease Progression

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Detection of Longitudinal Visual Field Progression in Glaucoma Using Machine Learning.

American journal of ophthalmology
PURPOSE: Global indices of standard automated perimerty are insensitive to localized losses, while point-wise indices are sensitive but highly variable. Region-wise indices sit in between. This study introduces a machine learning-based index for glau...

Distinguishing True Progression From Radionecrosis After Stereotactic Radiation Therapy for Brain Metastases With Machine Learning and Radiomics.

International journal of radiation oncology, biology, physics
PURPOSE: Treatment effect or radiation necrosis after stereotactic radiosurgery (SRS) for brain metastases is a common phenomenon often indistinguishable from true progression. Radiomics is an emerging field that promises to improve on conventional i...

A Novel Method to Predict Knee Osteoarthritis Progression on MRI Using Machine Learning Methods.

IEEE transactions on nanobioscience
This paper explored the hidden biomedical information from knee magnetic resonance (MR) images for osteoarthritis (OA) prediction. We have computed the cartilage damage index (CDI) information from 36 informative locations on tibiofemoral cartilage c...

A Multi-Task Framework for Monitoring Health Conditions via Attention-based Recurrent Neural Networks.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Monitoring the future health status of patients from the historical Electronic Health Record (EHR) is a core research topic in predictive healthcare. The most important challenges are to model the temporality of sequential EHR data and to interpret t...

Treatment-related features improve machine learning prediction of prognosis in soft tissue sarcoma patients.

Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]
BACKGROUND AND PURPOSE: Current prognostic models for soft tissue sarcoma (STS) patients are solely based on staging information. Treatment-related data have not been included to date. Including such information, however, could help to improve these ...

Knee menisci segmentation using convolutional neural networks: data from the Osteoarthritis Initiative.

Osteoarthritis and cartilage
OBJECTIVE: To present a novel method for automated segmentation of knee menisci from MRIs. To evaluate quantitative meniscal biomarkers for osteoarthritis (OA) estimated thereof.

Predication of different stages of Alzheimer's disease using neighborhood component analysis and ensemble decision tree.

Journal of neuroscience methods
BACKGROUND: There is a spectrum of the progression from healthy control (HC) to mild cognitive impairment (MCI) without conversion to Alzheimer's disease (AD), to MCI with conversion to AD (cMCI), and to AD. This study aims to predict the different d...

A Systematic Machine Learning Based Approach for the Diagnosis of Non-Alcoholic Fatty Liver Disease Risk and Progression.

Scientific reports
Prevention and diagnosis of NAFLD is an ongoing area of interest in the healthcare community. Screening is complicated by the fact that the accuracy of noninvasive testing lacks specificity and sensitivity to make and stage the diagnosis. Currently n...