AIMC Topic: Female
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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, ...
Diffusion Magnetic Resonance Imaging
Deep Learning
ROC Curve
Image Processing, Computer-Assisted
Magnetic Resonance Imaging
Neoplasm Staging
Rectal Neoplasms
Image Interpretation, Computer-Assisted
Signal-To-Noise Ratio
Aged, 80 and over
Adult
Male
Middle Aged
Humans
Retrospective Studies
Female
Aged
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.
Automated 3D segmentation of the hyoid bone in CBCT using nnU-Net v2: a retrospective study on model performance and potential clinical utility.
BMC medical imaging
Jul 1, 2025
OBJECTIVE: This study aimed to identify the hyoid bone (HB) using the nnU-Net based artificial intelligence (AI) model in cone beam computed tomography (CBCT) images and assess the model's success in automatic segmentation.
Contrast-enhanced mammography-based interpretable machine learning model for the prediction of the molecular subtype breast cancers.
BMC medical imaging
Jul 1, 2025
OBJECTIVE: This study aims to establish a machine learning prediction model to explore the correlation between contrast-enhanced mammography (CEM) imaging features and molecular subtypes of mass-type breast cancer.
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