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Applying Machine Learning to Kinematic and Eye Movement Features of a Movement Imitation Task to Predict Autism Diagnosis.

Scientific reports
Autism is a developmental condition currently identified by experts using observation, interview, and questionnaire techniques and primarily assessing social and communication deficits. Motor function and movement imitation are also altered in autism...

Artificial intelligence may offer insight into factors determining individual TSH level.

PloS one
The factors that determine Serum Thyrotropin (TSH) levels have been examined through different methods, using different covariates. However, the use of machine learning methods has so far not been studied in population databases like NHANES (National...

Stability Assessment of Intracranial Aneurysms Using Machine Learning Based on Clinical and Morphological Features.

Translational stroke research
Machine learning (ML) as a novel approach could help clinicians address the challenge of accurate stability assessment of unruptured intracranial aneurysms (IAs). We developed multiple ML models for IA stability assessment and compare their performan...

Early Prediction of Single-Cell Derived Sphere Formation Rate Using Convolutional Neural Network Image Analysis.

Analytical chemistry
Functional identification of cancer stem-like cells (CSCs) is an established method to identify and study this cancer subpopulation critical for cancer progression and metastasis. The method is based on the unique capability of single CSCs to survive...

Predictors of remission from body dysmorphic disorder after internet-delivered cognitive behavior therapy: a machine learning approach.

BMC psychiatry
BACKGROUND: Previous attempts to identify predictors of treatment outcomes in body dysmorphic disorder (BDD) have yielded inconsistent findings. One way to increase precision and clinical utility could be to use machine learning methods, which can in...

CytoCensus, mapping cell identity and division in tissues and organs using machine learning.

eLife
A major challenge in cell and developmental biology is the automated identification and quantitation of cells in complex multilayered tissues. We developed CytoCensus: an easily deployed implementation of supervised machine learning that extends conv...

Automated fibroglandular tissue segmentation in breast MRI using generative adversarial networks.

Physics in medicine and biology
Fibroglandular tissue (FGT) segmentation is a crucial step for quantitative analysis of background parenchymal enhancement (BPE) in magnetic resonance imaging (MRI), which is useful for breast cancer risk assessment. In this study, we develop an auto...

Osteoporotic hip fracture prediction from risk factors available in administrative claims data - A machine learning approach.

PloS one
OBJECTIVE: Hip fractures are among the most frequently occurring fragility fractures in older adults, associated with a loss of quality of life, high mortality, and high use of healthcare resources. The aim was to apply the superlearner method to pre...