The aim of this study was to develop and test an artificial intelligence (AI)-based algorithm for detecting common technical errors in canine thoracic radiography. The algorithm was trained using a database of thoracic radiographs from three veterina...
Journal of magnetic resonance imaging : JMRI
Oct 6, 2023
BACKGROUND: The combination of anatomical MRI and deep learning-based methods such as convolutional neural networks (CNNs) is a promising strategy to build predictive models of multiple sclerosis (MS) prognosis. However, studies assessing the effect ...
BACKGROUND: The macrotrabecular-massive (MTM) is a special subtype of hepatocellular carcinoma (HCC), which has commonly a dismal prognosis. This study aimed to develop a multitask deep learning radiomics (MDLR) model for predicting MTM and HCC patie...
PURPOSE: Manual clinical target volume (CTV) and gross tumor volume (GTV) delineation for rectal cancer neoadjuvant radiotherapy is pivotal but labor-intensive. This study aims to propose a deep learning (DL)-based workflow towards fully automated cl...
Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
Oct 5, 2023
PURPOSE: To compare the impact of deep learning reconstruction (DLR) and hybrid-iterative reconstruction (hybrid-IR) on vertebral mass depiction, detection, and diagnosis of spinal cord compression on computed tomography (CT).
RATIONALE AND OBJECTIVES: To accurately identify the high-risk pathological factors of pulmonary nodules, our study constructed a model combined with clinical features, radiomics features, and deep transfer learning features to predict high-risk path...
BACKGROUND: The nature of the solid component of subsolid nodules (SSNs) can indicate tumor pathological invasiveness. However, preoperative solid component assessment still lacks a reference standard.
Peritoneal metastasis (PM) is a frequent manifestation of advanced abdominal malignancies. Accurately assessing the extent of PM before surgery is essential for patients to receive optimal treatment. Therefore, we propose to construct a deep learning...
PURPOSE: The study was aimed to develop and evaluate a deep learning-based radiomics to predict the histological risk categorization of thymic epithelial tumors (TETs), which can be highly informative for patient treatment planning and prognostic ass...
Cancer imaging : the official publication of the International Cancer Imaging Society
Oct 5, 2023
OBJECTIVES: The goal of this study is to demonstrate the performance of radiomics and CNN-based classifiers in determining the primary origin of gastrointestinal liver metastases for visually indistinguishable lesions.
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