AIMC Topic: Tomography, X-Ray Computed
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Three contrasts in 3 min: Rapid, high-resolution, and bone-selective UTE MRI for craniofacial imaging with automated deep-learning skull segmentation.
Magnetic resonance in medicine
Sep 1, 2024
PURPOSE: Ultrashort echo time (UTE) MRI can be a radiation-free alternative to CT for craniofacial imaging of pediatric patients. However, unlike CT, bone-specific MR imaging is limited by long scan times, relatively low spatial resolution, and a tim...
Machine learning value in the diagnosis of vertebral fractures: A systematic review and meta-analysis.
European journal of radiology
Sep 1, 2024
PURPOSE: To evaluate the diagnostic accuracy of machine learning (ML) in detecting vertebral fractures, considering varying fracture classifications, patient populations, and imaging approaches.
An unsupervised learning model based on CT radiomics features accurately predicts axillary lymph node metastasis in breast cancer patients: diagnostic study.
International journal of surgery (London, England)
Sep 1, 2024
BACKGROUND: The accuracy of traditional clinical methods for assessing the metastatic status of axillary lymph nodes (ALNs) is unsatisfactory. In this study, the authors propose the use of radiomic technology and three-dimensional (3D) visualization ...
Comment on 'Deep learning-assisted detection and segmentation of intracranial hemorrhage in noncontrast computed tomography scans of acute stroke patients: a systematic review and meta-analysis'.
International journal of surgery (London, England)
Sep 1, 2024
Deep learning for contour quality assurance for RTOG 0933: In-silico evaluation.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Aug 31, 2024
PURPOSE: To validate a CT-based deep learning (DL) hippocampal segmentation model trained on a single-institutional dataset and explore its utility for multi-institutional contour quality assurance (QA).
Hybrid clinical-radiomics model based on fully automatic segmentation for predicting the early expansion of spontaneous intracerebral hemorrhage: A multi-center study.
Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
Aug 31, 2024
BACKGROUND: Early prediction of hematoma expansion (HE) is important for the development of therapeutic strategies for spontaneous intracerebral hemorrhage (sICH). Radiomics can help to predict early hematoma expansion in intracerebral hemorrhage. Ho...
Aged, 80 and over
Radiomics
Male
Middle Aged
Risk Assessment
Disease Progression
Deep Learning
China
Female
Reproducibility of Results
Risk Factors
Aged
Prognosis
Predictive Value of Tests
Retrospective Studies
Radiographic Image Interpretation, Computer-Assisted
Humans
Time Factors
Tomography, X-Ray Computed
Hematoma
Decision Support Techniques
Nomograms
Automation
Cerebral Hemorrhage
Initial sarcopenia and body composition changes as prognostic factors in cervical cancer patients treated with concurrent chemoradiation: An artificial intelligence-based volumetric study.
Gynecologic oncology
Aug 31, 2024
OBJECTIVES: This study aimed to investigate the influence of baseline sarcopenia and changes in body composition on survival during cervical cancer treatment.
Enhancing facial feature de-identification in multiframe brain images: A generative adversarial network approach.
Progress in brain research
Aug 31, 2024
The collection of head images for public datasets in the field of brain science has grown remarkably in recent years, underscoring the need for robust de-identification methods to adhere with privacy regulations. This paper elucidates a novel deep le...
Assessing GPT-4 multimodal performance in radiological image analysis.
European radiology
Aug 30, 2024
OBJECTIVES: This study aims to assess the performance of a multimodal artificial intelligence (AI) model capable of analyzing both images and textual data (GPT-4V), in interpreting radiological images. It focuses on a range of modalities, anatomical ...
Deep-learning-based method for the segmentation of ureter and renal pelvis on non-enhanced CT scans.
Scientific reports
Aug 30, 2024
This study aimed to develop a deep-learning (DL) based method for three-dimensional (3D) segmentation of the upper urinary tract (UUT), including ureter and renal pelvis, on non-enhanced computed tomography (NECT) scans. A total of 150 NECT scans wit...
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