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Combining Clinical-Radiomics Features With Machine Learning Methods for Building Models to Predict Postoperative Recurrence in Patients With Chronic Subdural Hematoma: Retrospective Cohort Study.

Journal of medical Internet research
BACKGROUND: Chronic subdural hematoma (CSDH) represents a prevalent medical condition, posing substantial challenges in postoperative management due to risks of recurrence. Such recurrences not only cause physical suffering to the patient but also ad...

Development of predictive model for the neurological deterioration among mild traumatic brain injury patients using machine learning algorithms.

Neurosurgical review
BACKGROUND: Mild traumatic brain injury (mTBI) comprises a majority of traumatic brain injury (TBI) cases. While some mTBI would suffer neurological deterioration (ND) and therefore have poorer prognosis. This study was designed to develop the predic...

A neural network approach to predict opioid misuse among previously hospitalized patients using electronic health records.

PloS one
Can Electronic Health Records (EHR) predict opioid misuse in general patient populations? This research trained three backpropagation neural networks to explore EHR predictors using existing patient data. Model 1 used patient diagnosis codes and was ...

AI can see you: Machiavellianism and extraversion are reflected in eye-movements.

PloS one
INTRODUCTION: Recent studies showed an association between personality traits and individual patterns of visual behaviour in laboratory and other settings. The current study extends previous research by measuring multiple personality traits in natura...

Improving Hand Gesture Recognition Robustness to Dynamic Posture Variations by Multimodal Deep Feature Fusion.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Surface electromyography (sEMG), a human-machine interface for gesture recognition, has shown promising potential for decoding motor intentions, but a variety of nonideal factors restrict its practical application in assistive robots. In this paper, ...

Performance of AI-Enabled Electrocardiogram in the Prediction of Metabolic Dysfunction-Associated Steatotic Liver Disease.

Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association
BACKGROUND AND AIMS: Accessible noninvasive screening tools for metabolic dysfunction-associated steatotic liver disease (MASLD) are needed. We aim to explore the performance of a deep learning-based artificial intelligence (AI) model in distinguishi...

Heatstroke death identification using ATR-FTIR spectroscopy combined with a novel multi-organ machine learning approach.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
With global warming, the number of deaths due to heatstroke has drastically increased. Nevertheless, there are still difficulties with the forensic assessment of heatstroke deaths, including the absence of particular organ pathological abnormalities ...

Accelerated T2-weighted MRI of the Bowel at 3T Using a Single-shot Technique with Deep Learning-based Image Reconstruction: Impact on Image Quality and Disease Detection.

Academic radiology
RATIONALE AND OBJECTIVE: A single-shot T2-weighted deep-learning-based image reconstruction (DL-HASTE) has been recently developed allowing for shorter acquisition time than conventional half-Fourier acquisition single-shot turbo-spin echo (HASTE). T...

Perspectives of medical imaging professionals about the impact of AI on Swiss radiographers.

Journal of medical imaging and radiation sciences
INTRODUCTION: Artificial Intelligence (AI) is increasingly implemented in medical imaging practice, however, its impact on radiographers practice is not well studied. The aim of this study was to explore the perceived impact of AI on radiographers' a...

Developing probabilistic ensemble machine learning models for home-based sleep apnea screening using overnight SpO2 data at varying data granularity.

Sleep & breathing = Schlaf & Atmung
PURPOSE: This study aims to develop sleep apnea screening models with overnight SpO2 data, and to investigate the impact of the SpO2 data granularity on model performance.