AIMC Topic: Female

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Deep learning-based approach to third molar impaction analysis with clinical classifications.

Scientific reports
This study developed a deep learning model for the automated detection and classification of impacted third molars using the Pell and Gregory Classification, Winter's Classification, and Pederson Difficulty Index. Panoramic radiographs of patients tr...

Influence of cognitive networks and task performance on fMRI-based state classification using DNN models.

Scientific reports
Deep neural networks (DNNs) excel at extracting insights from complex data across various fields, however, their application in cognitive neuroscience remains limited, largely due to the lack of approaches with interpretability. Here, we employ two d...

Development of a single-center predictive model for conventional in vitro fertilization outcomes excluding total fertilization failure: implications for protocol selection.

Journal of ovarian research
OBJECTIVES: To develop a multidimensional clinical indicator-based prediction model for identifying high-risk patients with fertilization failure conventional in vitro fertilization (c-IVF) cycles, thereby optimizing therapeutic decision-making.

A review of the use of tumour DNA methylation for breast cancer subtyping and prediction of outcomes.

Clinical epigenetics
DNA methylation in breast tumours has been extensively studied and has provided valuable insights into the clinical heterogeneity of breast cancer. In this review, we summarise the current literature that has used DNA methylation markers to subtype b...

Solicitude toward artificial intelligence among health care providers and its relation to their patient's safety culture in Saudi Arabia.

BMC health services research
BACKGROUND: The healthcare sector is undergoing a digital transformation, where the integration of Artificial Intelligence (AI) plays a vital role in reshaping healthcare practices. AI technologies promise to improve work procedures, mitigate future ...

Integrating CT radiomics and clinical features using machine learning to predict post-COVID pulmonary fibrosis.

Respiratory research
BACKGROUND: The lack of reliable biomarkers for the early detection and risk stratification of post-COVID-19 pulmonary fibrosis (PCPF) underscores the urgency advanced predictive tools. This study aimed to develop a machine learning-based predictive ...

Exploring the complex associations between community public spaces and healthy aging: an explainable analysis using catboost and SHAP.

BMC public health
BACKGROUND: As global aging accelerates, community public spaces (CPS) are increasingly recognized as vital for promoting healthy aging. However, existing research often employs linear analytical methods or focuses on single health dimensions, overlo...

Deep learning for automated dental plaque index assessment: validation against expert evaluations.

BMC oral health
BACKGROUND: The integration of artificial intelligence (AI) into healthcare has led to promising advancements in clinical decision-making and diagnostic accuracy. In dentistry, automated methods to evaluate oral hygiene measures, such as dental plaqu...

Machine learning-based integration identifies plasma cells-related gene signature ST6GAL1 in idiopathic pulmonary fibrosis.

BMC pulmonary medicine
BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a rare, progressive, and fibrotic disease with poor prognosis that lacks treatment options. As a major component of the lung adaptive immune system, plasma cells play a crucial regulatory role during...

Crisis-line workers' perspectives on AI in suicide prevention: a qualitative exploration of risk and opportunity.

BMC public health
BACKGROUND: Crisis support services offer crucial intervention for individuals in acute distress, providing timely access to trained volunteers whose human connection is key to the effectiveness of these services. However, there are significant dispa...