AI Medical Compendium Topic:
Image Interpretation, Computer-Assisted

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Deep Learning Based on ResNet-18 for Classification of Prostate Imaging-Reporting and Data System Category 3 Lesions.

Academic radiology Feb 1, 2024
RATIONALE AND OBJECTIVES: To explore the classification and prediction efficacy of the deep learning model for benign prostate lesions, non-clinically significant prostate cancer (non-csPCa) and clinically significant prostate cancer (csPCa) in Prost...
Humans Retrospective Studies Magnetic Resonance Imaging Prostatic Neoplasms Prostate Image Interpretation, Computer-Assisted Aged Male Middle Aged Deep Learning
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Deep learning-based multimodal segmentation of oropharyngeal squamous cell carcinoma on CT and MRI using self-configuring nnU-Net.

European radiology Jan 20, 2024
PURPOSE: To evaluate deep learning-based segmentation models for oropharyngeal squamous cell carcinoma (OPSCC) using CT and MRI with nnU-Net.
Multimodal Imaging Humans Retrospective Studies Image Interpretation, Computer-Assisted Male Tomography, X-Ray Computed Oropharyngeal Neoplasms Carcinoma, Squamous Cell Middle Aged Female Deep Learning Reproducibility of Results Magnetic Resonance Imaging Aged Adult
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AI-Enhanced Detection of Clinically Relevant Structural and Functional Anomalies in MRI: Traversing the Landscape of Conventional to Explainable Approaches.

Journal of magnetic resonance imaging : JMRI Jan 19, 2024
Anomaly detection in medical imaging, particularly within the realm of magnetic resonance imaging (MRI), stands as a vital area of research with far-reaching implications across various medical fields. This review meticulously examines the integratio...
Humans Deep Learning Image Processing, Computer-Assisted Artificial Intelligence Magnetic Resonance Imaging Machine Learning Image Interpretation, Computer-Assisted Brain
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Domain transformation using semi-supervised CycleGAN for improving performance of classifying thyroid tissue images.

International journal of computer assisted radiology and surgery Jan 18, 2024
PURPOSE: A large number of research has been conducted on the classification of medical images using deep learning. The thyroid tissue images can be also classified by cancer types. Deep learning requires a large amount of data, but every medical ins...
Humans Deep Learning Thyroid Gland Thyroid Neoplasms Image Interpretation, Computer-Assisted
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Enhancing Nigrosome-1 Sign Identification via Interpretable AI using True Susceptibility Weighted Imaging.

Journal of magnetic resonance imaging : JMRI Jan 18, 2024
BACKGROUND: Nigrosome 1 (N1), the largest nigrosome region in the ventrolateral area of the substantia nigra pars compacta, is identifiable by the "N1 sign" in long echo time gradient echo MRI. The N1 sign's absence is a vital Parkinson's disease (PD...
Image Interpretation, Computer-Assisted Humans Substantia Nigra Prospective Studies Female Deep Learning Reproducibility of Results Aged ROC Curve Adult Magnetic Resonance Imaging Male Parkinson Disease Middle Aged
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Three-dimensional heavily T2-weighted FLAIR in the detection of blood-labyrinthine barrier leakage in patients with sudden sensorineural hearing loss: comparison with T1 sequences and application of deep learning-based reconstruction.

European radiology Jan 17, 2024
OBJECTIVE: Blood-labyrinthine barrier leakage has been reported in sudden sensorineural hearing loss (SSNHL). We compared immediate post-contrast 3D heavily T2-weighted fluid-attenuated inversion recovery (FLAIR), T1 spin echo (SE), and 3D T1 gradien...
Humans Retrospective Studies Female Aged Young Adult Adult Contrast Media Hearing Loss, Sensorineural Male Magnetic Resonance Imaging Hearing Loss, Sudden Middle Aged Image Interpretation, Computer-Assisted Deep Learning Ear, Inner Imaging, Three-Dimensional
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Deep Learning-Based Analysis of Aortic Morphology From Three-Dimensional MRI.

Journal of magnetic resonance imaging : JMRI Jan 12, 2024
BACKGROUND: Quantification of aortic morphology plays an important role in the evaluation and follow-up assessment of patients with aortic diseases, but often requires labor-intensive and operator-dependent measurements. Automatic solutions would hel...
Aorta Aortic Diseases Turner Syndrome Image Processing, Computer-Assisted Hypertension Magnetic Resonance Imaging Image Interpretation, Computer-Assisted Male Middle Aged Deep Learning Imaging, Three-Dimensional Humans Female Retrospective Studies Reproducibility of Results Aged Adult
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Deep learning based automated left ventricle segmentation and flow quantification in 4D flow cardiac MRI.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance Jan 10, 2024
BACKGROUND: 4D flow MRI enables assessment of cardiac function and intra-cardiac blood flow dynamics from a single acquisition. However, due to the poor contrast between the chambers and surrounding tissue, quantitative analysis relies on the segment...
Heart Ventricles Image Interpretation, Computer-Assisted Automation Blood Flow Velocity Ventricular Function, Left Middle Aged Deep Learning Predictive Value of Tests Coronary Circulation Myocardial Perfusion Imaging Magnetic Resonance Imaging, Cine Humans Databases, Factual Female Reproducibility of Results Male
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Determination of Alzheimer's disease based on morphology and atrophy using machine learning combined with automated segmentation.

Acta radiologica (Stockholm, Sweden : 1987) Jan 9, 2024
BACKGROUND: To evaluate the degree of cerebral atrophy for Alzheimer's disease (AD), voxel-based morphometry has been performed with magnetic resonance imaging. Detailed morphological changes in a specific tissue area having the most evidence of atro...
Machine Learning Brain Female Aged Humans Neuroimaging Gray Matter Atrophy Support Vector Machine Magnetic Resonance Imaging Alzheimer Disease Image Interpretation, Computer-Assisted Aged, 80 and over Male Middle Aged Imaging, Three-Dimensional
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Exploring White Matter Abnormalities in Young Children with Autism Spectrum Disorder: Integrating Multi-shell Diffusion Data and Machine Learning Analysis.

Academic radiology Jan 6, 2024
RATIONALE AND OBJECTIVES: This study employed tract-based spatial statistics (TBSS) to investigate abnormalities in the white matter microstructure among children with autism spectrum disorder (ASD). Additionally, an eXtreme Gradient Boosting (XGBoos...
Diffusion Tensor Imaging Diffusion Magnetic Resonance Imaging Child, Preschool Child Image Interpretation, Computer-Assisted Humans White Matter Machine Learning Autism Spectrum Disorder Female Male
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