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Unveiling the core functional networks of cognition: An ontology-guided machine learning approach.

NeuroImage
Deciphering the functional architecture that underpins diverse cognitive functions is fundamental quest in neuroscience. In this study, we employed an innovative machine learning framework that integrated cognitive ontology with functional connectivi...

Acquisition of Data on Kinematic Responses to Unpredictable Gait Perturbations: Collection and Quality Assurance of Data for Use in Machine Learning Algorithms for (Near-)Fall Detection.

Sensors (Basel, Switzerland)
Slip, trip, and fall (STF) accidents cause high rates of absence from work in many companies. During the 2022 reporting period, the German Social Accident Insurance recorded 165,420 STF accidents, of which 12 were fatal and 2485 led to disability pen...

Diagnosing Sarcopenia with AI-Aided Ultrasound (DINOSAUR)-A Pilot Study.

Nutrients
Sarcopenia has been recognized as a determining factor in surgical outcomes and is associated with an increased risk of postoperative complications and readmission. Diagnosis is currently based on clinical guidelines, which includes assessment of sk...

Breaking the silence: leveraging social interaction data to identify high-risk suicide users online using network analysis and machine learning.

Scientific reports
Suicidal thought and behavior (STB) is highly stigmatized and taboo. Prone to censorship, yet pervasive online, STB risk detection may be improved through development of uniquely insightful digital markers. Focusing on Sanctioned Suicide, an online p...

Fast prediction of personalized abdominal organ doses from CT examinations by radiomics feature-based machine learning models.

Scientific reports
The X-rays emitted during CT scans can increase solid cancer risks by damaging DNA, with the risk tied to patient-specific organ doses. This study aims to establish a new method to predict patient specific abdominal organ doses from CT examinations u...

Machine learning model for non-alcoholic steatohepatitis diagnosis based on ultrasound radiomics.

BMC medical imaging
BACKGROUND: Non-Alcoholic Steatohepatitis (NASH) is a crucial stage in the progression of Non-Alcoholic Fatty Liver Disease(NAFLD). The purpose of this study is to explore the clinical value of ultrasound features and radiological analysis in predict...

Linking disease activity with optical coherence tomography angiography in neovascular age related macular degeneration using artificial intelligence.

Scientific reports
To investigate quantitative associations between AI-assessed disease activity and optical coherence tomography angiography (OCTA)-derived parameters in patients with neovascular age-related macular degeneration (nAMD) undergoing anti-VEGF therapy. OC...

Decoding myasthenia gravis: advanced diagnosis with infrared spectroscopy and machine learning.

Scientific reports
Myasthenia Gravis (MG) is a rare neurological disease. Although there are intensive efforts, the underlying mechanism of MG still has not been fully elucidated, and early diagnosis is still a question mark. Diagnostic paraclinical tests are also time...

An end-to-end deep learning method for mass spectrometry data analysis to reveal disease-specific metabolic profiles.

Nature communications
Untargeted metabolomic analysis using mass spectrometry provides comprehensive metabolic profiling, but its medical application faces challenges of complex data processing, high inter-batch variability, and unidentified metabolites. Here, we present ...