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Unveiling lipoprotein subfractions signature in high-FNPO PCOS: implications for PCOM diagnosis and risk assessment using advanced machine learning models.

BMC medicine
BACKGROUND: Polycystic ovary syndrome (PCOS) is a common reproductive and metabolic disorder in the reproductive-age women. The international evidence-based guideline for the assessment and management of PCOS 2023 now suggests raising the follicle nu...

Deep learning models based on multiparametric magnetic resonance imaging and clinical parameters for identifying synchronous liver metastases from rectal cancer.

BMC medical imaging
OBJECTIVES: To establish and validate deep learning (DL) models based on pre-treatment multiparametric magnetic resonance imaging (MRI) images of primary rectal cancer and basic clinical data for the prediction of synchronous liver metastases (SLM) i...

Exploring dental students' attitudes and perceptions toward artificial intelligence in dentistry in Iran.

BMC medical education
INTRODUCTION: AI has the potential to enhance diagnostics, optimize treatment planning, and improve patient care. However, concerns remain regarding professional autonomy, ethical considerations, and the need for adequate training. This research aims...

Assessing artificial intelligence ability in predicting hospitalization duration for pleural empyema patients managed with uniportal video-assisted thoracoscopic surgery: a retrospective observational study.

BMC surgery
BACKGROUND: This retrospective observational research evaluates the potential applicability of artificial intelligence models to predict the length of hospital stay for patients with pleural empyema who underwent uniportal video-assisted thoracoscopi...

Development of a machine learning prediction model for loss to follow-up in HIV care using routine electronic medical records in a low-resource setting.

BMC medical informatics and decision making
BACKGROUND: Despite the global commitment to ending AIDS by 2030, the loss of follow-up (LTFU) in HIV care remains a significant challenge. To address this issue, a data-driven clinical decision tool is crucial for identifying patients at greater ris...

Multiple deep learning models based on MRI images in discriminating glioblastoma from solitary brain metastases: a multicentre study.

BMC medical imaging
OBJECTIVE: Development of a deep learning model for accurate preoperative identification of glioblastoma and solitary brain metastases by combining multi-centre and multi-sequence magnetic resonance images and comparison of the performance of differe...

Predicting Placebo Responses Using EEG and Deep Convolutional Neural Networks: Correlations with Clinical Data Across Three Independent Datasets.

Neuroinformatics
Identifying likely placebo responders can help design more efficient clinical trials by stratifying participants, reducing sample size requirements, and enhancing the detection of true drug effects. In response to this need, we developed a deep convo...

Translational approach for dementia subtype classification using convolutional neural network based on EEG connectome dynamics.

Scientific reports
Dementia spectrum disorders, characterized by progressive cognitive decline, pose a significant global health burden. Early screening and diagnosis are essential for timely and accurate treatment, improving patient outcomes and quality of life. This ...

Machine learning based clinical decision tool to predict acute kidney injury and survival in therapeutic hypothermia treated neonates.

Scientific reports
Therapeutic hypothermia (TH) significantly reduces mortality and morbidities in neonates with Neonatal Encephalopathy (NE). NE may result in neonatal death and multisystem organ impairment, including acute kidney injury (AKI). Our study aimed to util...