AIMC Topic: Tomography, X-Ray Computed
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Data extraction from free-text stroke CT reports using GPT-4o and Llama-3.3-70B: the impact of annotation guidelines.
European radiology experimental
Jun 19, 2025
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
Jun 18, 2025
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
Jun 17, 2025
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...
Earlier discharge from pulmonary nodule follow-up using artificial intelligence based volume measurements in computed tomography.
European journal of radiology
Jun 17, 2025
BACKGROUND: Lung cancer is the leading cause of cancer death worldwide. Effective screening and early detection are critical in reducing mortality. Artificial intelligence (AI) methods have been proved useful in the diagnosis of pulmonary nodules and...
Solitary Pulmonary Nodule
Early Detection of Cancer
Radiographic Image Interpretation, Computer-Assisted
Patient Discharge
Lung Neoplasms
Follow-Up Studies
Multiple Pulmonary Nodules
Sensitivity and Specificity
Male
Middle Aged
Humans
Artificial Intelligence
Retrospective Studies
Reproducibility of Results
Aged
Tomography, X-Ray Computed
Female
Development and interpretation of machine learning-based prognostic models for predicting high-risk prognostic pathological components in pulmonary nodules: integrating clinical features, serum tumor marker and imaging features.
Journal of cancer research and clinical oncology
Jun 17, 2025
BACKGROUND: With the improvement of imaging, the screening rate of Pulmonary nodules (PNs) has further increased, but their identification of High-Risk Prognostic Pathological Components (HRPPC) is still a major challenge. In this study, we aimed to ...
Quasi-supervised MR-CT image conversion based on unpaired data.
Physics in medicine and biology
Jun 17, 2025
. In radiotherapy planning, acquiring both magnetic resonance (MR) and computed tomography (CT) images is crucial for comprehensive evaluation and treatment. However, simultaneous acquisition of MR and CT images is time-consuming, economically expens...
The value of a deep learning image reconstruction algorithm for assessing vertebral compression fractures using dual-energy computed tomography.
European journal of radiology
Jun 16, 2025
PURPOSE: To evaluate the value of deep learning image reconstruction (DLIR) in improving image quality of virtual non-hydroxyapatite (VNHAP) and virtual monoenergetic images (VMIs), and radiologists' performance in detecting acute vertebral compressi...
Radiogenomic correlation of hypoxia-related biomarkers in clear cell renal cell carcinoma.
Journal of cancer research and clinical oncology
Jun 12, 2025
PURPOSE: This study aimed to evaluate radiomic models' ability to predict hypoxia-related biomarker expression in clear cell renal cell carcinoma (ccRCC).
The performance of artificial intelligence in image-based prediction of hematoma enlargement: a systematic review and meta-analysis.
Annals of medicine
Jun 11, 2025
BACKGROUND: Accurately predicting hematoma enlargement (HE) is crucial for improving the prognosis of patients with cerebral haemorrhage. Artificial intelligence (AI) is a potentially reliable assistant for medical image recognition. This study syste...
A machine learning approach for personalized breast radiation dosimetry in CT: Integrating radiomics and deep neural networks.
European journal of radiology
Jun 11, 2025
PURPOSE: To develop a machine learning-based workflow for patient-specific breast radiation dosimetry in CT.
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