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An Automated Machine Learning-Based Quantitative Multiparametric Approach for Mitral Regurgitation Severity Grading.

JACC. Cardiovascular imaging
BACKGROUND: Considering the high prevalence of mitral regurgitation (MR) and the highly subjective, variable MR severity reporting, an automated tool that could screen patients for clinically significant MR (≥ moderate) would streamline the diagnosti...

Deep learning segmentation of mandible with lower dentition from cone beam CT.

Oral radiology
OBJECTIVES: This study aimed to train a 3D U-Net convolutional neural network (CNN) for mandible and lower dentition segmentation from cone-beam computed tomography (CBCT) scans.

Can Patients With Urogenital Cancer Rely on Artificial Intelligence Chatbots for Treatment Decisions?

Clinical genitourinary cancer
OBJECTIVES: In the era of artificial intelligence, almost half of the patients use the internet to get information about their diseases. Our study aims to demonstrate the reliability of the information provided by artificial intelligence chatbots (AI...

Development of deep learning-based novel auto-segmentation for the prostatic urethra on planning CT images for prostate cancer radiotherapy.

Radiological physics and technology
Urinary toxicities are one of the serious complications of radiotherapy for prostate cancer, and dose-volume histogram of prostatic urethra has been associated with such toxicities in previous reports. Previous research has focused on estimating the ...

Identifying neuroimaging biomarkers in major depressive disorder using machine learning algorithms and functional near-infrared spectroscopy (fNIRS) during verbal fluency task.

Journal of affective disorders
One of the most prevalent psychiatric disorders is major depressive disorder (MDD), which increases the probability of suicidal ideation or untimely demise. Abnormal frontal hemodynamic changes detected by functional near-infrared spectroscopy (fNIRS...

Deep learning and its associated factors among Chinese nursing undergraduates: A cross-sectional study.

Nurse education today
BACKGROUND: Adequate professional preparation of nursing undergraduates is conducive to developing health care careers. Deep learning is important for enhancing nursing competencies and the overall quality of students. However, limited research has b...

Artificial intelligence tools utilized in nursing education: Incidence and associated factors.

Nurse education today
BACKGROUND: Artificial intelligence technology is among the most significant advancements that provide students with effective learning opportunities in this digital era. Therefore, the National League for Nursing states that it is necessary to refra...

Attention-based CNN-BiLSTM for sleep state classification of spatiotemporal wide-field calcium imaging data.

Journal of neuroscience methods
BACKGROUND: Wide-field calcium imaging (WFCI) with genetically encoded calcium indicators allows for spatiotemporal recordings of neuronal activity in mice. When applied to the study of sleep, WFCI data are manually scored into the sleep states of wa...

Decoding micro-electrocorticographic signals by using explainable 3D convolutional neural network to predict finger movements.

Journal of neuroscience methods
BACKGROUND: Electroencephalography (EEG) and electrocorticography (ECoG) recordings have been used to decode finger movements by analyzing brain activity. Traditional methods focused on single bandpass power changes for movement decoding, utilizing m...

Immune-based Machine learning Prediction of Diagnosis and Illness State in Schizophrenia and Bipolar Disorder.

Brain, behavior, and immunity
BACKGROUND: Schizophrenia and bipolar disorder frequently face significant delay in diagnosis, leading to being missed or misdiagnosed in early stages. Both disorders have also been associated with trait and state immune abnormalities. Recent machine...