AIMC Topic: Image Interpretation, Computer-Assisted
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SCIseg: Automatic Segmentation of Intramedullary Lesions in Spinal Cord Injury on T2-weighted MRI Scans.
Radiology. Artificial intelligence
Jan 1, 2025
Purpose To develop a deep learning tool for the automatic segmentation of the spinal cord and intramedullary lesions in spinal cord injury (SCI) on T2-weighted MRI scans. Materials and Methods This retrospective study included MRI data acquired betwe...
Image detection method for multi-category lesions in wireless capsule endoscopy based on deep learning models.
World journal of gastroenterology
Dec 28, 2024
BACKGROUND: Wireless capsule endoscopy (WCE) has become an important noninvasive and portable tool for diagnosing digestive tract diseases and has been propelled by advancements in medical imaging technology. However, the complexity of the digestive ...
The TabNet Model for Diagnosing Axial Spondyloarthritis Using MRI Imaging Findings and Clinical Risk Factors.
International journal of rheumatic diseases
Dec 1, 2024
OBJECTIVES: The aim of this study is to develop and validate a model for predicting axial spondyloarthritis (axSpA) based on sacroiliac joint (SIJ)-MRI imaging findings and clinical risk factors.
Accelerated Cardiac MRI with Deep Learning-based Image Reconstruction for Cine Imaging.
Radiology. Cardiothoracic imaging
Dec 1, 2024
Purpose To assess the influence of deep learning (DL)-based image reconstruction on acquisition time, volumetric results, and image quality of cine sequences in cardiac MRI. Materials and Methods This prospective study (performed from January 2023 to...
Automated segmentation of brain metastases with deep learning: A multi-center, randomized crossover, multi-reader evaluation study.
Neuro-oncology
Nov 4, 2024
BACKGROUND: Artificial intelligence has been proposed for brain metastasis (BM) segmentation but it has not been fully clinically validated. The aim of this study was to develop and evaluate a system for BM segmentation.
Image-Based Generative Artificial Intelligence in Radiology: Comprehensive Updates.
Korean journal of radiology
Nov 1, 2024
Generative artificial intelligence (AI) has been applied to images for image quality enhancement, domain transfer, and augmentation of training data for AI modeling in various medical fields. Image-generative AI can produce large amounts of unannotat...
Assessing the impact of deep-learning assistance on the histopathological diagnosis of serous tubal intraepithelial carcinoma (STIC) in fallopian tubes.
The journal of pathology. Clinical research
Nov 1, 2024
In recent years, it has become clear that artificial intelligence (AI) models can achieve high accuracy in specific pathology-related tasks. An example is our deep-learning model, designed to automatically detect serous tubal intraepithelial carcinom...
Addressing the Generalizability of AI in Radiology Using a Novel Data Augmentation Framework with Synthetic Patient Image Data: Proof-of-Concept and External Validation for Classification Tasks in Multiple Sclerosis.
Radiology. Artificial intelligence
Nov 1, 2024
Artificial intelligence (AI) models often face performance drops after deployment to external datasets. This study evaluated the potential of a novel data augmentation framework based on generative adversarial networks (GANs) that creates synthetic p...
External Validation of a Previously Developed Deep Learning-based Prostate Lesion Detection Algorithm on Paired External and In-House Biparametric MRI Scans.
Radiology. Imaging cancer
Nov 1, 2024
Purpose To evaluate the performance of an artificial intelligence (AI) model in detecting overall and clinically significant prostate cancer (csPCa)-positive lesions on paired external and in-house biparametric MRI (bpMRI) scans and assess performanc...
Boosting Deep Learning for Interpretable Brain MRI Lesion Detection through the Integration of Radiology Report Information.
Radiology. Artificial intelligence
Nov 1, 2024
Purpose To guide the attention of a deep learning (DL) model toward MRI characteristics of brain lesions by incorporating radiology report-derived textual features to achieve interpretable lesion detection. Materials and Methods In this retrospective...
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