AIMC Topic: Tomography, X-Ray Computed

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From jargon to clarity: Improving the readability of foot and ankle radiology reports with an artificial intelligence large language model.

Foot and ankle surgery : official journal of the European Society of Foot and Ankle Surgeons
BACKGROUND: The purpose of this study was to evaluate the efficacy of an Artificial Intelligence Large Language Model (AI-LLM) at improving the readability foot and ankle orthopedic radiology reports.

Interactive content-based image retrieval with deep learning for CT abdominal organ recognition.

Physics in medicine and biology
Recognizing the most relevant seven organs in an abdominal computed tomography (CT) slice requires sophisticated knowledge. This study proposed automatically extracting relevant features and applying them in a content-based image retrieval (CBIR) sys...

ADGAN: Attribute-Driven Generative Adversarial Network for Synthesis and Multiclass Classification of Pulmonary Nodules.

IEEE transactions on neural networks and learning systems
Lung cancer is the leading cause of cancer-related deaths worldwide. According to the American Cancer Society, early diagnosis of pulmonary nodules in computed tomography (CT) scans can improve the five-year survival rate up to 70% with proper treatm...

Deep learning-based algorithms for low-dose CT imaging: A review.

European journal of radiology
The computed tomography (CT) technique is extensively employed as an imaging modality in clinical settings. The radiation dose of CT, however, is significantly high, thereby raising concerns regarding the potential radiation damage it may cause. The ...

CT-Based Radiomics Analysis of Different Machine Learning Models for Discriminating the Risk Stratification of Pheochromocytoma and Paraganglioma: A Multicenter Study.

Academic radiology
RATIONALE AND OBJECTIVES: Using different machine learning models CT-based radiomics to integrate clinical radiological features to discriminating the risk stratification of pheochromocytoma/paragangliomas (PPGLs).

Deep-Learning Reconstruction of High-Resolution CT Improves Interobserver Agreement for the Evaluation of Pulmonary Fibrosis.

Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
This study aimed to investigate whether deep-learning reconstruction (DLR) improves interobserver agreement in the evaluation of honeycombing for patients with interstitial lung disease (ILD) who underwent high-resolution computed tomography (CT) co...

Automated size-specific dose estimates framework in thoracic CT using convolutional neural network based on U-Net model.

Journal of applied clinical medical physics
PURPOSE: This study aimed to develop an automated method that uses a convolutional neural network (CNN) for calculating size-specific dose estimates (SSDEs) based on the corrected effective diameter (D ) in thoracic computed tomography (CT).

Radiological age assessment based on clavicle ossification in CT: enhanced accuracy through deep learning.

International journal of legal medicine
BACKGROUND: Radiological age assessment using reference studies is inherently limited in accuracy due to a finite number of assignable skeletal maturation stages. To overcome this limitation, we present a deep learning approach for continuous age ass...