AIMC Topic: Tomography, X-Ray Computed
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Multiple Tumor-related autoantibodies test enhances CT-based deep learning performance in diagnosing lung cancer with diameters < 70 mm: a prospective study in China.
BMC pulmonary medicine
Jul 29, 2025
BACKGROUND: Deep learning (DL) demonstrates high sensitivity but low specificity in lung cancer (LC) detection during CT screening, and the seven Tumor-associated antigens autoantibodies (7-TAAbs), known for its high specificity in LC, was employed t...
AI generated annotations for Breast, Brain, Liver, Lungs, and Prostate cancer collections in the National Cancer Institute Imaging Data Commons.
Scientific data
Jul 29, 2025
The Artificial Intelligence in Medical Imaging (AIMI) initiative aims to enhance the National Cancer Institute's (NCI) Image Data Commons (IDC) by releasing fully reproducible nnU-Net models, along with AI-assisted segmentation for cancer radiology i...
A radiomics-based interpretable model integrating delayed-phase CT and clinical features for predicting the pathological grade of appendiceal pseudomyxoma peritonei.
BMC medical imaging
Jul 28, 2025
OBJECTIVE: This study aimed to develop an interpretable machine learning model integrating delayed-phase contrast-enhanced CT radiomics with clinical features for noninvasive prediction of pathological grading in appendiceal pseudomyxoma peritonei (P...
Hyperparameter tuned deep learning-driven medical image analysis for intracranial hemorrhage detection.
PloS one
Jul 28, 2025
Intracranial haemorrhage (ICH) is a crucial medical emergency that entails prompt assessment and management. Compared to conventional clinical tests, the need for computerized medical assistance for properly recognizing brain haemorrhage from compute...
FedGAN: Federated diabetic retinopathy image generation.
PloS one
Jul 24, 2025
Deep learning models for diagnostic applications require large amounts of sensitive patient data, raising privacy concerns under centralized training paradigms. We propose FedGAN, a federated learning framework for synthetic medical image generation ...
Establishment of AI-assisted diagnosis of the infraorbital posterior ethmoid cells based on deep learning.
BMC medical imaging
Jul 21, 2025
OBJECTIVE: To construct an artificial intelligence (AI)-assisted model for identifying the infraorbital posterior ethmoid cells (IPECs) based on deep learning using sagittal CT images.
Lysophospholipid metabolism, clinical characteristics, and artificial intelligence-based quantitative assessments of chest CT in patients with stable COPD and healthy smokers.
Scientific reports
Jul 21, 2025
The specific role of lysophospholipids (LysoPLs) in the pathogenesis of chronic obstructive pulmonary disease (COPD) is not yet fully understood. We determined serum LysoPLs in 20 patients with stable COPD and 20 healthy smokers using liquid chromato...
Sex estimation with parameters of the facial canal by computed tomography using machine learning algorithms and artificial neural networks.
BMC medical imaging
Jul 18, 2025
BACKGROUND: The skull is highly durable and plays a significant role in sex determination as one of the most dimorphic bones. The facial canal (FC), a clinically significant canal within the temporal bone, houses the facial nerve. This study aims to ...
Integrative radiomics of intra- and peri-tumoral features for enhanced risk prediction in thymic tumors: a multimodal analysis of tumor microenvironment contributions.
BMC medical imaging
Jul 17, 2025
OBJECTIVES: This study aims to explore the role of intra- and peri-tumoral radiomics features in tumor risk prediction, with a particular focus on the impact of peri-tumoral characteristics on the tumor microenvironment.
Opportunistic computed tomography (CT) assessment of osteoporosis in patients undergoing transcatheter aortic valve replacement (TAVR).
Archives of osteoporosis
Jul 17, 2025
UNLABELLED: CT-based opportunistic screening using artificial intelligence finds a high prevalence (43%) of osteoporosis in CT scans obtained for planning of transcatheter aortic valve replacement. Thus, opportunistic screening may be a cost-effectiv...
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