AIMC Topic: Female
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Plasma Lyso-PE 22:6 and Lyso-PE 20:4 are associated with development of mild to moderate depression revealed by metabolomics: a pilot study.
BMC psychiatry
Jul 1, 2025
BACKGROUND: Mild to moderate depression (MMD), as an early stage of depression, has a high incidence and may progress to severe depression, even leading to suicide. The lack of effective screening and treatment is due to the unknown metabolic changes...
Student perceptions of GenAI as a virtual tutor to support collaborative research training for health professionals.
BMC medical education
Jul 1, 2025
BACKGROUND: Research and evaluation skills are essential in healthcare education. Instructors frequently employ collaborative learning models to teach these competencies; however, delivering timely and personalized feedback to multiple groups can be ...
Preoperative MRI-based deep learning reconstruction and classification model for assessing rectal cancer.
BMC medical imaging
Jul 1, 2025
BACKGROUND: To determine whether deep learning reconstruction (DLR) could improve the image quality of rectal MR images, and to explore the discrimination of the TN stage of rectal cancer by different readers and deep learning classification models, ...
Humans
Signal-To-Noise Ratio
Diffusion Magnetic Resonance Imaging
Retrospective Studies
Middle Aged
Female
Deep Learning
Magnetic Resonance Imaging
ROC Curve
Neoplasm Staging
Image Processing, Computer-Assisted
Aged
Rectal Neoplasms
Aged, 80 and over
Image Interpretation, Computer-Assisted
Adult
Male
Large language models versus traditional textbooks: optimizing learning for plastic surgery case preparation.
BMC medical education
Jul 1, 2025
BACKGROUND: Large language models (LLMs), such as ChatGPT-4 and Gemini, represent a new frontier in surgical education by offering dynamic, interactive learning experiences. Despite their potential, concerns about the accuracy, depth of knowledge, an...
Deep learning-based automated classification of choroidal layers in en face swept-source optical coherence tomography images.
BMC ophthalmology
Jul 1, 2025
BACKGROUND: This study aims to develop a deep learning-based algorithm dedicated to the automated classification of choroidal layers in en face swept-source optical coherence tomography (SS-OCT) images of the eye.
Machine learning-based prediction model for arteriovenous fistula thrombosis risk: a retrospective cohort study from 2017 to 2024.
BMC nephrology
Jul 1, 2025
BACKGROUND: Thrombosis of arteriovenous fistulas represents a prevalent complication among patients undergoing hemodialysis, characterized by a notably high incidence rate. Presently, there is an absence of robust assessment tools capable of predicti...
Ultrasound-based machine learning model to predict the risk of endometrial cancer among postmenopausal women.
BMC medical imaging
Jul 1, 2025
BACKGROUND: Current ultrasound-based screening for endometrial cancer (EC) primarily relies on endometrial thickness (ET) and morphological evaluation, which suffer from low specificity and high interobserver variability. This study aimed to develop ...
Computed tomography-based radiomics predicts prognostic and treatment-related levels of immune infiltration in the immune microenvironment of clear cell renal cell carcinoma.
BMC medical imaging
Jul 1, 2025
OBJECTIVES: The composition of the tumour microenvironment is very complex, and measuring the extent of immune cell infiltration can provide an important guide to clinically significant treatments for cancer, such as immune checkpoint inhibition ther...
Development and validation of a machine learning model for central compartmental lymph node metastasis in solitary papillary thyroid microcarcinoma via ultrasound imaging features and clinical parameters.
BMC medical imaging
Jul 1, 2025
BACKGROUND: Papillary thyroid microcarcinoma (PTMC) is the most common malignant subtype of thyroid cancer. Preoperative assessment of the risk of central compartment lymph node metastasis (CCLNM) can provide scientific support for personalized treat...
Radiomics and machine learning for osteoporosis detection using abdominal computed tomography: a retrospective multicenter study.
BMC medical imaging
Jul 1, 2025
OBJECTIVE: This study aimed to develop and validate a predictive model to detect osteoporosis using radiomic features and machine learning (ML) approaches from lumbar spine computed tomography (CT) images during an abdominal CT examination.
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