AIMC Topic: Tomography, X-Ray Computed
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Deep transfer learning radiomics combined with explainable machine learning for preoperative thymoma risk prediction based on CT.
European journal of radiology
Jun 26, 2025
OBJECTIVE: To develop and validate a computerized tomography (CT)‑based deep transfer learning radiomics model combined with explainable machine learning for preoperative risk prediction of thymoma.
How well do multimodal LLMs interpret CT scans? An auto-evaluation framework for analyses.
Journal of biomedical informatics
Jun 25, 2025
OBJECTIVE: This study introduces a novel evaluation framework, GPTRadScore, to systematically assess the performance of multimodal large language models (MLLMs) in generating clinically accurate findings from CT imaging. Specifically, GPTRadScore lev...
Computed tomography-derived quantitative imaging biomarkers enable the prediction of disease manifestations and survival in patients with systemic sclerosis.
RMD open
Jun 25, 2025
INTRODUCTION: Systemic sclerosis (SSc) is a complex inflammatory vasculopathy with diverse symptoms and variable disease progression. Despite its known impact on body composition (BC), clinical decision-making has yet to incorporate these biomarkers....
Fully automated workflow for designing patient-specific orthopaedic implants: Application to total knee arthroplasty.
PloS one
Jun 25, 2025
Background Osteoarthritis affects about 528 million people worldwide, causing pain and stiffness in the joints. Arthroplasty is commonly performed to treat joint osteoarthritis, reducing pain and improving mobility. Nevertheless, a significant share ...
Comprehensive predictive modeling in subarachnoid hemorrhage: integrating radiomics and clinical variables.
Neurosurgical review
Jun 24, 2025
Subarachnoid hemorrhage (SAH) is a severe condition with high morbidity and long-term neurological consequences. Radiomics, by extracting quantitative features from Computed Tomograhpy (CT) scans, may reveal imaging biomarkers predictive of outcomes....
[Incidental pulmonary nodules on CT imaging: what to do?].
Nederlands tijdschrift voor geneeskunde
Jun 23, 2025
Incidental pulmonary nodules are very frequently found on CT imaging and may represent (early stage) lung cancers without any signs or symptoms. These incidental findings can be solid lesions or ground glass lesions that may be solitary or multiple. ...
PMFF-Net: A deep learning-based image classification model for UIP, NSIP, and OP.
Computers in biology and medicine
Jun 20, 2025
BACKGROUND: High-resolution computed tomography (HRCT) is helpful for diagnosing interstitial lung diseases (ILD), but it largely depends on the experience of physicians. Herein, our study aims to develop a deep-learning-based classification model to...
Combination of 2D and 3D nnU-Net for ground glass opacity segmentation in CT images of Post-COVID-19 patients.
Computers in biology and medicine
Jun 20, 2025
BACKGROUND AND OBJECTIVE: The COVID-19 pandemic plays a significant roles in the global health, highlighting the imperative for effective management of post-recovery symptoms. Within this context, Ground Glass Opacity (GGO) in lung computed tomograph...
A prior knowledge-supervised fusion network predicts survival after radiotherapy in patients with advanced gastric cancer.
Artificial intelligence in medicine
Jun 19, 2025
BACKGROUND AND OBJECTIVE: Predicting overall survival (OS) for advanced gastric cancer patients after radiotherapy is critical for developing an individualized treatment plan. However, existing studies have focused on gastric cancer CT images with a ...
Performance assessment of an artificial intelligence algorithm for opportunistic screening of abdominal aortic aneurysms.
Clinical imaging
Jun 19, 2025
PURPOSE: Abdominal aortic aneurysm (AAA) is a common incidental finding on CT imaging performed in the acute care setting. Artificial intelligence (AI) algorithms have been developed to automatically measure aortic lumen size and thus facilitate AAA ...
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