Preterm birth and very low birthweight (VP/VLBW) are associated with poorer health-related quality of life (HRQoL) outcomes extending into adulthood, yet it remains unclear how these effects differ across sociodemographic subgroups. This study aimed ...
RATIONALE AND OBJECTIVES: The aim of this study is to develop a deep learning-based multimodal feature interaction-guided fusion (DL-MFIF) framework that integrates macroscopic information from computed tomography (CT) images with microscopic informa...
RATIONALE AND OBJECTIVES: To develop an interpretable machine learning (ML) model based on cardiac magnetic resonance (CMR) multimodal parameters and clinical data to discriminate Takotsubo syndrome (TTS), acute myocardial infarction (AMI), and acute...
BACKGROUND: There are models to predict intraoperative hypotension from arterial pressure waveforms. Selection bias in datasets used for model development and validation could impact model performance. We aimed to evaluate how selection bias affects ...
BACKGROUND: The use of digital technologies is becoming increasingly important in medicine and is having a significant impact on developments in the surgical field. However, there is a great need to improve and implement those new techniques in surgi...
International journal of legal medicine
Sep 1, 2025
The epiphyses of the hand and wrist serve as crucial indicators for assessing skeletal maturity in adolescents. This study aimed to develop a deep learning (DL) model for bone age (BA) assessment using hand and wrist X-ray images, addressing the chal...
BACKGROUND: Determining whether pediatric patients with low-grade gliomas (pLGGs) have tumor-related epilepsy (GAE) is a crucial aspect of preoperative evaluation. Therefore, we aim to propose an innovative, machine learning- and deep learning-based ...
BackgroundThe rapid advancement of Artificial Intelligence (AI) is driving transformative changes across various sectors, reshaping organizational management models and altering work dynamics for employees.ObjectiveThis study employs the framework of...
This paper tested the relevance of two machine learning approaches (decision trees, DTs; and random forest models, RFs) applied to meat authentication. DT allow to select and rank potential biomarkers according to their respective discriminatory powe...
OBJECTIVE: The purpose of this study was to use machine learning models to predict the risk of hyperlipidemia in people with HIV (PWH) for 6 months after starting HAART, to improve early intervention efforts and prevent further progression to cardiov...
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