AIMC Topic: Humans

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Sustainable deep learning-based breast lesion segmentation: impact of breast region segmentation on performance.

BMC medical imaging
PURPOSE: Segmentation of breast lesions in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is critical for effective diagnosis. This study investigates the impact of breast region segmentation (BRS) on the performance of deep learning-...

Decoding IBD progression: a dynamic biomarker atlas for personalized disease stratification.

Journal of translational medicine
BACKGROUND: Accurate staging is pivotal for tailoring treatment intensity, optimizing resource allocation, and improving long-term patient outcomes in IBD. The intestinal microbiota and transcriptional profiles emerge as critical determinants in IBD ...

Effect of artificial intelligence-assisted personalized feedback on radiographic diagnostic performance of dental students: a controlled study.

BMC medical education
BACKGROUND: This study aimed to evaluate the impact of MeSH based personalized learning guides generated by ChatGPT-4o on the radiographic diagnostic performance of dental students and to compare it with the traditional correct/incorrect feedback met...

DNA methylation and machine learning: challenges and perspective toward enhanced clinical diagnostics.

Clinical epigenetics
DNA methylation is an epigenetic modification that regulates gene expression by adding methyl groups to DNA, affecting cellular function and disease development. Machine learning, a subset of artificial intelligence, analyzes large datasets to identi...

Machine learning-based integration of pericoronary adipose tissue and clinical risk factors for cardiovascular risk prediction in type 2 diabetes: a retrospective cohort study.

European journal of medical research
BACKGROUND: Cardiovascular disease remains the predominant cause of morbidity and mortality in individuals with type 2 diabetes mellitus (T2DM). Traditional risk models are limited in predictive accuracy. Pericoronary adipose tissue (PCAT), a novel i...

Investigating the role of depression in obstructive sleep apnea and predicting risk factors for OSA in depressed patients: machine learning-assisted evidence from NHANES.

BMC psychiatry
OBJECTIVE: The relationship between depression and obstructive sleep apnea (OSA) remains controversial. Therefore, this study aims to explore their association and utilize machine learning models to predict OSA among individuals with depression withi...

Integrating AI and RNA biomarkers in cancer: advances in diagnostics and targeted therapies.

Cell communication and signaling : CCS
Early detection and personalized treatment strategies are essential for enhancing patient outcomes, as cancer continues to be a significant cause of mortality on a global basis. In clinical practice, the identification and validation of reliable biom...

Semi-automatic detection of anteriorly displaced temporomandibular joint discs in magnetic resonance images using machine learning.

BMC oral health
BACKGROUND: Accurate diagnosis of anterior disc displacement (ADD) is essential for managing temporomandibular joint disorders (TMJ). This study employed machine learning (ML) to automatically detect anteriorly displaced TMJ discs in magnetic resonan...

Predicting outcomes in pediatric patients with acute kidney injury: a retrospective single-center cohort study using machine learning models.

BMC medical informatics and decision making
OBJECTIVE: To develop and evaluate machine learning models combined with survival analysis for predicting 7-, 14-, and 28-day mortality in critically ill children with acute kidney injury (AKI), identifying key predictors to guide risk stratification...

Can ChatGPT be trusted? Evaluating AI responses to oral health questions among pregnant Arabic-speaking women.

BMC oral health
BACKGROUND: ChatGPT, an artificial intelligence (AI) chatbot developed by OpenAI, is increasingly being used in healthcare, including dentistry, for patient education; this study aimed to assess the usability and quality of ChatGPT's responses to pre...