AIMC Topic: Tomography, X-Ray Computed

Clear Filters Showing 221 to 230 of 5128 articles

Fully automated workflow for designing patient-specific orthopaedic implants: Application to total knee arthroplasty.

PloS one
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
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
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
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
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
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
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 ...

Data extraction from free-text stroke CT reports using GPT-4o and Llama-3.3-70B: the impact of annotation guidelines.

European radiology experimental
BACKGROUND: To evaluate the impact of an annotation guideline on the performance of large language models (LLMs) in extracting data from stroke computed tomography (CT) reports.

Deep learning model using CT images for longitudinal prediction of benign and malignant ground-glass nodules.

European journal of radiology
OBJECTIVES: To develop and validate a CT image-based multiple time-series deep learning model for the longitudinal prediction of benign and malignant pulmonary ground-glass nodules (GGNs).

Deep learning for orbital fracture detection and reconstruction: A systematic review on diagnostic accuracy and surgical planning.

Journal of cranio-maxillo-facial surgery : official publication of the European Association for Cranio-Maxillo-Facial Surgery
OBJECTIVE: To systematically review the efficacy of deep learning (DL) models in detecting and reconstructing orbital fractures based on computed tomography (CT) imaging, assessing their diagnostic accuracy, processing time, and role in surgical plan...