AIMC Topic: Magnetic Resonance Imaging

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Biological Age Prediction of the Cerebellar Vermis in the Human Lifespan.

Cerebellum (London, England)
The cerebellar vermis undergoes diverse structural changes with aging, yet region-specific aging patterns remain underexplored. Using Brain Structure Age (BSA), a deep learning biomarker from structural magnetic resonance imaging (MRI), we aimed to: ...

Explainable and likelihood aware AI framework for MRI-based pixel-level bladder tumour prediction.

Scientific reports
Bladder tumours (BTs) pose significant clinical challenges due to their high recurrence rates and risk of progression to invasive malignancies, which emphasises the need for early and accurate detection. Magnetic resonance imaging (MRI), with its sup...

Universal black-box attacks against a third-party Alzheimer's diagnostic system.

Biomedical physics & engineering express
Artificial intelligence (AI) systems are increasingly used in medical imaging for disease diagnosis, yet their vulnerability to adversarial attacks poses significant risks for clinical deployment. In this work, we systematically evaluate the suscepti...

MRI multi-sequence deep learning integration with clinical profiles for pediatric viral encephalitis diagnosis.

Scientific reports
Pediatric viral encephalitis is an acute central nervous system infection caused by various viruses, with diverse clinical manifestations and challenges in early diagnosis. The traditional diagnostic methods lack sufficient sensitivity and specificit...

Identify MRI negative temporal lobe epilepsy with resting fMRI indicators and machine learning techniques.

Scientific reports
About 30% of temporal lobe epilepsy (TLE) cases are negative on MRI, so quantitative diagnosis based on clinical symptoms becomes challenging. There is an urgent need for an accurate and reliable method to differentiate patients with MRI-negative TLE...

A novel deep learning system for STEMI prognostic prediction from multi-sequence cardiac magnetic resonance.

Science bulletin
ST-elevation myocardial infarction (STEMI) remains a leading cause of cardiovascular morbidity and mortality worldwide, and accurate early risk stratification is critical for implementing precision therapies in clinical practice. However, existing cl...

An intelligent brain tumor detection model using lightweight hybrid twin attentive pyramid convolutional network.

Scientific reports
Brain tumors (BTs) pose a serious threat to human health, and the optimized treatment and results depend on early and accurate detection. Although MRIs and other medical imaging technologies provide insightful information, it is still difficult to de...

Advantage of grading classification using volumetric artificial intelligence for periventricular hyperintensity and deep subcortical white matter hyperintensity.

Scientific reports
We developed and validated an artificial intelligence (AI) algorithm for the automated grading of periventricular hyperintensity (PVH) and deep subcortical white matter hyperintensity (DWMH) using magnetic resonance imaging. Overall, 246 patients wer...

Subtyping schizophrenia via machine learning by using structural neuroimaging.

Translational psychiatry
Schizophrenia is a heterogeneous disorder with diverse clinical presentations and neuroanatomical alterations. Despite recent advances, we still lack a working hypothesis for the pathophysiology of schizophrenia. One reason might be the heterogeneous...

Distinct neuroimaging subtypes of ADHD among adolescents based on semi-supervised learning.

Translational psychiatry
Attention deficit hyperactivity disorder (ADHD) is a childhood-onset neurodevelopmental disorder diagnosed and subtyped solely based on clinical traits, which are prone to subjective judgment and lack of reliability. Also, the clinical subtyping does...