AIMC Topic: Tomography, X-Ray Computed
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Integrating radiomics and gene expression by mapping on the image with improved DeepInsight for clear cell renal cell carcinoma.
Cancer genetics
Feb 14, 2025
BACKGROUND: Radiomics analysis extracts high-dimensional features from medical images, which are used to predict outcomes in machine learning (ML). Recently, deep-learning methods have become applicable to image data converted from nonimage samples.
A-Eval: A benchmark for cross-dataset and cross-modality evaluation of abdominal multi-organ segmentation.
Medical image analysis
Feb 14, 2025
Although deep learning has revolutionized abdominal multi-organ segmentation, its models often struggle with generalization due to training on small-scale, specific datasets and modalities. The recent emergence of large-scale datasets may mitigate th...
An interpretable ensemble model combining handcrafted radiomics and deep learning for predicting the overall survival of hepatocellular carcinoma patients after stereotactic body radiation therapy.
Journal of cancer research and clinical oncology
Feb 14, 2025
PURPOSE: Hepatocellular carcinoma (HCC) remains a global health concern, marked by increasing incidence rates and poor outcomes. This study seeks to develop a robust predictive model by integrating radiomics and deep learning features with clinical d...
Deep learning on pre-procedural computed tomography and clinical data predicts outcome following stroke thrombectomy.
Journal of neurointerventional surgery
Feb 14, 2025
BACKGROUND: Deep learning using clinical and imaging data may improve pre-treatment prognostication in ischemic stroke patients undergoing endovascular thrombectomy (EVT).
Development and application of AI assisted automatic reconstruction of axial lumbar disc CT images and diagnosis of lumbar disc herniation.
European journal of radiology
Feb 13, 2025
RATIONALE AND OBJECTIVES: To evaluate the value of artificial intelligence (AI) assisted diagnostic system in reconstructing axial lumbar disc CT images and diagnosing lumbar disc herniation.
Evaluation and failure analysis of four commercial deep learning-based autosegmentation software for abdominal organs at risk.
Journal of applied clinical medical physics
Feb 13, 2025
PURPOSE: Deep learning-based segmentation of organs-at-risk (OAR) is emerging to become mainstream in clinical practice because of the superior performance over atlas and model-based autocontouring methods. While several commercial deep learning-base...
Large Language Models-Supported Thrombectomy Decision-Making in Acute Ischemic Stroke Based on Radiology Reports: Feasibility Qualitative Study.
Journal of medical Internet research
Feb 13, 2025
BACKGROUND: The latest advancement of artificial intelligence (AI) is generative pretrained transformer large language models (LLMs). They have been trained on massive amounts of text, enabling humanlike and semantical responses to text-based inputs ...
MLAR-UNet: LDCT image denoising based on U-Net with multiple lightweight attention-based modules and residual reinforcement.
Physics in medicine and biology
Feb 13, 2025
Computed tomography (CT) is a crucial medical imaging technique which uses x-ray radiation to identify cancer tissues. Since radiation poses a significant health risk, low dose acquisition procedures need to be adopted. However, low-dose CT (LDCT) ca...
Artificial intelligence for opportunistic osteoporosis screening with a Hounsfield Unit in chronic obstructive pulmonary disease patients.
Journal of clinical densitometry : the official journal of the International Society for Clinical Densitometry
Feb 13, 2025
INTRODUCTION: To investigate the accuracy of an artificial intelligence (AI) prototype in determining bone mineral density (BMD) in chronic obstructive pulmonary disease (COPD) patients using chest computed tomography (CT) scans.
Diagnostic of fatty liver using radiomics and deep learning models on non-contrast abdominal CT.
PloS one
Feb 13, 2025
PURPOSE: This study aims to explore the potential of non-contrast abdominal CT radiomics and deep learning models in accurately diagnosing fatty liver.
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