AIMC Topic: Tomography, X-Ray Computed
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Deep learning-based segmentation for high-dose-rate brachytherapy in cervical cancer using 3D Prompt-ResUNet.
Physics in medicine and biology
Sep 27, 2024
To develop and evaluate a 3D Prompt-ResUNet module that utilized the prompt-based model combined with 3D nnUNet for rapid and consistent autosegmentation of high-risk clinical target volume (HRCTV) and organ at risk (OAR) in high-dose-rate brachyther...
Identification of Bipolar Disorder and Schizophrenia Based on Brain CT and Deep Learning Methods.
Journal of imaging informatics in medicine
Sep 26, 2024
With the increasing prevalence of mental illness, accurate clinical diagnosis of mental illness is crucial. Compared with MRI, CT has the advantages of wide application, low price, short scanning time, and high patient cooperation. This study aims to...
DELR-Net: a network for 3D multimodal medical image registration in more lightweight application scenarios.
Abdominal radiology (New York)
Sep 26, 2024
PURPOSE: 3D multimodal medical image deformable registration plays a significant role in medical image analysis and diagnosis. However, due to the substantial differences between images of different modalities, registration is challenging and require...
An Artificial Intelligence Algorithm Integrated into the Clinical Workflow Can Ensure High Quality Acute Intracranial Hemorrhage CT Diagnostic.
Clinical neuroradiology
Sep 26, 2024
PURPOSE: Intracranial hemorrhage (ICH) is a life-threatening condition requiring rapid diagnostic and therapeutic action. This study evaluates whether Artificial intelligence (AI) can provide high-quality ICH diagnostics and turnaround times suitable...
Using Natural Language Processing (GPT-4) for Computed Tomography Image Analysis of Cerebral Hemorrhages in Radiology: Retrospective Analysis.
Journal of medical Internet research
Sep 26, 2024
BACKGROUND: Cerebral hemorrhage is a critical medical condition that necessitates a rapid and precise diagnosis for timely medical intervention, including emergency operation. Computed tomography (CT) is essential for identifying cerebral hemorrhage,...
Pretreatment CT-based machine learning radiomics model predicts response in unresectable hepatocellular carcinoma treated with lenvatinib plus PD-1 inhibitors and interventional therapy.
Journal for immunotherapy of cancer
Sep 25, 2024
[How well does artificial intelligence detect fractures in the cervical spine on CT?].
Nederlands tijdschrift voor geneeskunde
Sep 25, 2024
OBJECTIVE: To compare diagnostic accuracy of artificial intelligence (AI) for cervical spine (C-spine) fracture detection on CT with attending radiologists.
A Robust Deep Learning Method with Uncertainty Estimation for the Pathological Classification of Renal Cell Carcinoma Based on CT Images.
Journal of imaging informatics in medicine
Sep 23, 2024
This study developed and validated a deep learning-based diagnostic model with uncertainty estimation to aid radiologists in the preoperative differentiation of pathological subtypes of renal cell carcinoma (RCC) based on computed tomography (CT) ima...
Identification of macrotrabecular-massive hepatocellular carcinoma through multiphasic CT-based representation learning method.
Medical physics
Sep 23, 2024
BACKGROUND: Macrotrabecular-massive hepatocellular carcinoma (MTM-HCC) represents an aggressive subtype of HCC and is associated with poor survival.
Deep learning-based reconstruction improves the image quality of low-dose CT enterography in patients with inflammatory bowel disease.
Abdominal radiology (New York)
Sep 21, 2024
PURPOSE: Lifelong re-examination of CT enterography (CTE) in patients with inflammatory bowel disease (IBD) may be necessary, and reducing radiation exposure during CT examinations is crucial. We investigated the potential application of deep learnin...
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