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Forensic age estimation for pelvic X-ray images using deep learning.

European radiology
PURPOSE: To develop a deep learning bone age assessment model based on pelvic radiographs for forensic age estimation and compare its performance to that of the existing cubic regression model.

Predominant polarity classification and associated clinical variables in bipolar disorder: A machine learning approach.

Journal of affective disorders
BACKGROUND: Bipolar disorder (BD) is a severe psychiatric disorder characterized by periodic episodes of manic and depressive symptomatology. Predominant polarity (PP) appears to be an important specifier of BD. The present study employed machine lea...

Chest Radiographs in Congestive Heart Failure: Visualizing Neural Network Learning.

Radiology
Purpose To examine Generative Visual Rationales (GVRs) as a tool for visualizing neural network learning of chest radiograph features in congestive heart failure (CHF). Materials and Methods A total of 103 489 frontal chest radiographs in 46 712 pati...

A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using F-FDG PET of the Brain.

Radiology
Purpose To develop and validate a deep learning algorithm that predicts the final diagnosis of Alzheimer disease (AD), mild cognitive impairment, or neither at fluorine 18 (F) fluorodeoxyglucose (FDG) PET of the brain and compare its performance to t...

Keratoconus severity identification using unsupervised machine learning.

PloS one
We developed an unsupervised machine learning algorithm and applied it to big corneal parameters to identify and monitor keratoconus stages. A big dataset of corneal swept source optical coherence tomography (OCT) images of 12,242 eyes acquired from ...

A machine learning ensemble to predict treatment outcomes following an Internet intervention for depression.

Psychological medicine
BACKGROUND: Some Internet interventions are regarded as effective treatments for adult depression, but less is known about who responds to this form of treatment.

Multi-Source Ensemble Learning for the Remote Prediction of Parkinson's Disease in the Presence of Source-Wise Missing Data.

IEEE transactions on bio-medical engineering
As the collection of mobile health data becomes pervasive, missing data can make large portions of datasets inaccessible for analysis. Missing data has shown particularly problematic for remotely diagnosing and monitoring Parkinson's disease (PD) usi...

Predicting conversion from clinically isolated syndrome to multiple sclerosis-An imaging-based machine learning approach.

NeuroImage. Clinical
Magnetic resonance imaging (MRI) scans play a pivotal role in the evaluation of patients presenting with a clinically isolated syndrome (CIS), as these may depict brain lesions suggestive of an inflammatory cause. We hypothesized that it is possible ...