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A multimodal multitask deep learning model for predicting stroke lesion and functional outcomes using 4D CTP imaging and clinical metadata.

Scientific reports
Acute ischemic stroke is a major global health challenge, leading to long-term disability or death without timely intervention. Among neuroimaging modalities, spatio-temporal (4D) computed tomography perfusion (CTP) is widely used to assess cerebral ...

An interpretable machine learning model predicts the interactive and cumulative risks of different environmental chemical exposures on depression.

Translational psychiatry
Humans are exposed to a multitude of environmental chemical mixtures (ECMs) in daily life that may influence depression risk. While prior studies have shown individual ECM exposures to depression, the cumulative and interactive effects of multiple co...

Optic disc morphometrics as a potential ocular biomarker for depression: evidence from two cross-sectional cohort studies.

Translational psychiatry
Depression, which is increasingly prevalent among older adults, has traditionally been diagnosed through symptom-based questionnaires. However, emerging evidence suggests that retinal changes could serve as objective biomarkers for depression. In thi...

The application of amplitude of low-frequency fluctuations metrics in the diagnosis and prediction of treatment response as well as their associated genes and biological processes in patients with bipolar disorder.

Translational psychiatry
While previous studies have reported functional abnormalities in the prefrontal-limbic-subcortical circuit, the treatment effects on this activity remain unclear. This longitudinal study aimed to investigate spontaneous brain activity in bipolar diso...

Comparative study of coronary artery disease prediction: conventional QRISK3 versus enhanced machine learning models combined with particle swarm optimisation algorithm.

Open heart
BACKGROUND: Coronary artery disease (CAD) is one of the biggest causes of mortality worldwide. Risk stratification for early detection is essential for the primary prevention of CAD. QRISK3 is known to overestimate future CAD risk in some populations...

Artificial intelligence-powered spatial analysis of tumor microenvironment in patients with non-small cell lung cancer with acquired resistance to EGFR tyrosine kinase inhibitor.

Journal for immunotherapy of cancer
PURPOSE: This study evaluated the dynamic changes in the tumor microenvironment (TME) in patients with non-small cell lung cancer (NSCLC) and acquired resistance to epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitors (TKIs) using an ar...

Detecting Perceived Unfair Treatment Among US College Students Using Mobile Sensing: Pilot Machine Learning Study.

JMIR formative research
BACKGROUND: Experiences of unfair treatment on college campuses are linked to adverse mental and physical health outcomes, highlighting the need for interventions. However, detecting such experiences relies mainly on self-reports. No prior research h...

Reliable biomarkers for diabetic nephropathy using machine learning-assisted contrast-enhanced ultrasonography and clinical characteristics.

Clinical and experimental medicine
OBJECTIVE: To utilize machine learning techniques to screen contrast-enhanced ultrasound (CEUS) parameters and clinical characteristics, aiming to differentiate diabetic nephropathy (DN) from non-diabetic renal disease (NDRD) in patients with diabeti...

The ferroptosis-related gene MAFG screened by machine learning is associated with the diagnosis and prognosis of sepsis.

Clinical and experimental medicine
Ferroptosis is a novel form of cell death induced by ferrous ions and lipid peroxidation. However, the mechanisms of ferroptosis-related genes (FRGs) in sepsis have not been studied thoroughly. We performed differential analysis using GSE65682, and t...

Multimodal pathomics and clinical features predict postresection permanent hydrocephalus in pediatric medulloblastoma.

Journal of neuro-oncology
PURPOSE: Predicting postoperative persistent hydrocephalus risk in pediatric medulloblastoma remains challenging using conventional clinical features. We investigated whether deep learning (DL) of pathomic features could improve postoperative hydroce...