AIMC Topic: Tomography, X-Ray Computed

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The knowledge distillation-assisted multimodal model for osteoporosis screening.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Osteoporosis is characterized by reduced bone mass and deterioration of bone structure, yet screening rates prior to fractures remain low. Given its high prevalence and severe consequences, developing an effective osteoporos...

Radiomics classification of fresh and old vertebral compression fractures: Impact of compression grade and morphology on diagnostic performance.

European journal of radiology
OBJECTIVES: To develop a radiomics model for identifying fresh or old vertebral compression fractures (VCFs) from CT images, thereby assisting physicians in making more effective decisions.

Quantitative computed tomography imaging classification of cement dust-exposed patients-based Kolmogorov-Arnold networks.

Artificial intelligence in medicine
BACKGROUND: Occupational health assessment is critical for detecting respiratory issues caused by harmful exposures, such as cement dust. Quantitative computed tomography (QCT) imaging provides detailed insights into lung structure and function, enha...

Dynamic Multi-scale Feature Integration Network for unsupervised MR-CT synthesis.

Neural networks : the official journal of the International Neural Network Society
Unsupervised MR-CT synthesis presents a significant opportunity to reduce radiation exposure from CT scans and lower costs by eliminating the need for both MR and CT imaging. However, many existing unsupervised methods face limitations in capturing d...

Deep Learning and Radiomic Signatures Associated with Tumor Immune Heterogeneity Predict Microvascular Invasion in Colon Cancer.

Academic radiology
RATIONALE AND OBJECTIVES: This study aims to develop and validate a deep learning radiomics signature (DLRS) that integrates radiomics and deep learning features for the non-invasive prediction of microvascular invasion (MVI) in patients with colon c...

Deep Learning-Based Multimodal Feature Interaction-Guided Fusion: Enhancing the Evaluation of EGFR in Advanced Lung Adenocarcinoma.

Academic radiology
RATIONALE AND OBJECTIVES: The aim of this study is to develop a deep learning-based multimodal feature interaction-guided fusion (DL-MFIF) framework that integrates macroscopic information from computed tomography (CT) images with microscopic informa...

Detection, Classification, and Segmentation of Rib Fractures From CT Data Using Deep Learning Models: A Review of Literature and Pooled Analysis.

Journal of thoracic imaging
PURPOSE: Trauma-induced rib fractures are common injuries. The gold standard for diagnosing rib fractures is computed tomography (CT), but the sensitivity in the acute setting is low, and interpreting CT slices is labor-intensive. This has led to the...

External Validation of a CT-Based Radiogenomics Model for the Detection of EGFR Mutation in NSCLC and the Impact of Prevalence in Model Building by Using Synthetic Minority Over Sampling (SMOTE): Lessons Learned.

Academic radiology
RATIONALE AND OBJECTIVES: Radiogenomics holds promise in identifying molecular alterations in nonsmall cell lung cancer (NSCLC) using imaging features. Previously, we developed a radiogenomics model to predict epidermal growth factor receptor (EGFR) ...

Automatic detection of mandibular fractures on CT scan using deep learning.

Dento maxillo facial radiology
OBJECTIVES: This study explores the application of artificial intelligence (AI), specifically deep learning, in the detection and classification of mandibular fractures using CT scans.