AIMC Topic: Adult

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Automated measurement of cardiothoracic ratio based on semantic segmentation integration model using deep learning.

Medical & biological engineering & computing
The objective of this study is to investigate the efficacy of the semantic segmentation model in predicting cardiothoracic ratio (CTR) and heart enlargement and compare its consistency with the reference standard. A total of 650 consecutive chest rad...

Predicting preterm birth using electronic medical records from multiple prenatal visits.

BMC pregnancy and childbirth
This study aimed to predict preterm birth in nulliparous women using machine learning and easily accessible variables from prenatal visits. Elastic net regularized logistic regression models were developed and evaluated using 5-fold cross-validation ...

Identification of hub genes, diagnostic model, and immune infiltration in preeclampsia by integrated bioinformatics analysis and machine learning.

BMC pregnancy and childbirth
PURPOSE: This study aimed to identify novel biomarkers for preeclampsia (PE) diagnosis by integrating Weighted Gene Co-expression Network Analysis (WGCNA) with machine learning techniques.

Evaluation of Pregnancy Risks in Women with Subchorionic Hematoma Using Machine Learning Models.

Medical science monitor : international medical journal of experimental and clinical research
BACKGROUND Subchorionic hematoma (SCH) can lead to blood accumulation and potentially affect pregnancy outcomes. Despite being a relatively common finding in early pregnancy, the effects of SCH on pregnancy outcomes such as miscarriage, stillbirth, a...

Ethical implications of AI-driven clinical decision support systems on healthcare resource allocation: a qualitative study of healthcare professionals' perspectives.

BMC medical ethics
BACKGROUND: Artificial intelligence-driven Clinical Decision Support Systems (AI-CDSS) are increasingly being integrated into healthcare for various purposes, including resource allocation. While these systems promise improved efficiency and decision...

Improving the prediction of chemotherapy dose-limiting toxicity in colon cancer patients using an AI-CT-based 3D body composition of the entire L1-L5 lumbar spine.

Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
PURPOSE: Chemotherapy dose-limiting toxicities (DLT) pose a significant challenge in successful colon cancer treatment. Body composition analysis may enable tailored interventions thereby supporting the mitigation of chemotherapy toxic effects. This ...

Identifying threshold of CT-defined muscle loss after radiotherapy for survival in oral cavity cancer using machine learning.

European radiology
OBJECTIVES: Muscle loss after radiotherapy is associated with poorer survival in patients with oral cavity squamous cell carcinoma (OCSCC). However, the threshold of muscle loss remains unclear. This study aimed to utilize explainable artificial inte...

CT-based clinical-radiomics model to predict progression and drive clinical applicability in locally advanced head and neck cancer.

European radiology
BACKGROUND: Definitive chemoradiation is the primary treatment for locally advanced head and neck carcinoma (LAHNSCC). Optimising outcome predictions requires validated biomarkers, since TNM8 and HPV could have limitations. Radiomics may enhance risk...