OBJECTIVES: The purpose of this study was to propose a machine learning model and assess its ability to classify temporomandibular joint (TMJ) disc displacements on MR T1-weighted and proton density-weighted images.
Journal of the American Medical Informatics Association : JAMIA
Jan 1, 2025
BACKGROUND: Acute hepatic porphyria (AHP) is a group of rare but treatable conditions associated with diagnostic delays of 15 years on average. The advent of electronic health records (EHR) data and machine learning (ML) may improve the timely recogn...
The study aimed to compare the morphometric and morphologic analyses of the bone structures of temporomandibular joint and masticatory muscles on Cone beam computed tomography (CBCT) in 62 healthy subjects and 33 subjects with temporomandibular dysfu...
We investigated the impact of donor characteristics on outcomes in allogeneic hematopoietic cell transplantation (HCT) recipients using a novel machine learning approach, the Nonparametric Failure Time Bayesian Additive Regression Trees (NFT BART). N...
This research aims to enhance our comprehensive understanding of the influence of type-2 diabetes on the development of cardiovascular diseases (CVD) risk, its underlying determinants, and to construct precise predictive models capable of accurately ...
Clinical and experimental dental research
Dec 1, 2024
OBJECTIVE: The aim of the study was to assess the prevalence of dental erosion in competitive swimmers using teledentistry and artificial intelligence.
Studies in health technology and informatics
Nov 18, 2024
This paper presents a conceptual prototype that integrates Artificial Intelligence (AI) and Augmented Reality (AR) with the principles of Universal Design (UD) to enhance decision-making in everyday scenarios for a diverse user base, eliminating the ...
BACKGROUND: The demand for fresh strategies to analyze intricate multidimensional data in neuroscience is increasingly evident. One of the most complex events during our neurodevelopment is adolescence, where our nervous system suffers constant chang...
OBJECTIVES: To evaluate the performance of ultrasound-based deep learning (DL) models in distinguishing breast phyllodes tumours (PTs) from fibroadenomas (FAs) and their clinical utility in assisting radiologists with varying diagnostic experiences.
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