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Responsible AI for cardiovascular disease detection: Towards a privacy-preserving and interpretable model.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Cardiovascular disease (CD) is a major global health concern, affecting millions with symptoms like fatigue and chest discomfort. Timely identification is crucial due to its significant contribution to global mortality. In h...

AI-based automated evaluation of image quality and protocol tailoring in patients undergoing MRI for suspected prostate cancer.

European journal of radiology
PURPOSE: To develop and validate an artificial intelligence (AI) application in a clinical setting to decide whether dynamic contrast-enhanced (DCE) sequences are necessary in multiparametric prostate MRI.

Uncovering the most robust predictors of problematic pornography use: A large-scale machine learning study across 16 countries.

Journal of psychopathology and clinical science
Problematic pornography use (PPU) is the most common manifestation of the newly introduced compulsive sexual behavior disorder diagnosis in the 11th revision of the International Classification of Diseases. Research related to PPU has proliferated in...

Prediction of short-term progression of COVID-19 pneumonia based on chest CT artificial intelligence: during the Omicron epidemic.

BMC infectious diseases
BACKGROUND AND PURPOSE: The persistent progression of pneumonia is a critical determinant of adverse outcomes in patients afflicted with COVID-19. This study aimed to predict personalized COVID-19 pneumonia progression between the duration of two wee...

An explanatory study of factors influencing engagement in AI education at the K-12 Level: an extension of the classic TAM model.

Scientific reports
Artificial intelligence (AI) holds immense promise for K-12 education, yet understanding the factors influencing students' engagement with AI courses remains a challenge. This study addresses this gap by extending the technology acceptance model (TAM...

A novel blood-based epigenetic biosignature in first-episode schizophrenia patients through automated machine learning.

Translational psychiatry
Schizophrenia (SCZ) is a chronic, severe, and complex psychiatric disorder that affects all aspects of personal functioning. While SCZ has a very strong biological component, there are still no objective diagnostic tests. Lately, special attention ha...

Study of machine learning techniques for outcome assessment of leptospirosis patients.

Scientific reports
Leptospirosis is a global disease that impacts people worldwide, particularly in humid and tropical regions, and is associated with significant socio-economic deficiencies. Its symptoms are often confused with other syndromes, which can compromise cl...

CACSNet for automatic robust classification and segmentation of carotid artery calcification on panoramic radiographs using a cascaded deep learning network.

Scientific reports
Stroke is one of the major causes of death worldwide, and is closely associated with atherosclerosis of the carotid artery. Panoramic radiographs (PRs) are routinely used in dental practice, and can be used to visualize carotid artery calcification (...

Deep learning-based classification of erosion, synovitis and osteitis in hand MRI of patients with inflammatory arthritis.

RMD open
OBJECTIVES: To train, test and validate the performance of a convolutional neural network (CNN)-based approach for the automated assessment of bone erosions, osteitis and synovitis in hand MRI of patients with inflammatory arthritis.

A machine learning model based on clinical features and ultrasound radiomics features for pancreatic tumor classification.

Frontiers in endocrinology
OBJECTIVE: This study aimed to construct a machine learning model using clinical variables and ultrasound radiomics features for the prediction of the benign or malignant nature of pancreatic tumors.