OBJECTIVES: To develop a radiomics model for identifying fresh or old vertebral compression fractures (VCFs) from CT images, thereby assisting physicians in making more effective decisions.
OBJECTIVE: This study aimed to evaluate the diagnostic performance of an artificial intelligence (AI)-based platform (Diagnocat) in detecting periapical radiolucencies (PARLs) in cone-beam computed tomography (CBCT) scans of molars. Specifically, we ...
RATIONALE AND OBJECTIVES: This study develops a deep-learning method for automatic segmentation of the interventricular septum (IS) in MR images to measure myocardial T2* and estimate cardiac iron deposition in patients with thalassemia.
RATIONALE AND OBJECTIVES: This study seeks to develop a combined model integrating clinical data, radiomics, and deep learning (DL) for predicting the efficacy of posterior lumbar interbody fusion (PLIF) surgery.
OBJECTIVE: Deep learning (DL) has been used to differentiate papilledema from healthy eyes and optic disc elevation on fundus photos. As we described optic nerve head (ONH) and peripapillary retina (PPR) optical coherence tomography (OCT) features th...
PURPOSE: To develop and validate interpretable machine learning models for differentiating glioblastoma (GB) from solitary brain metastasis (SBM) using radiomics features from contrast-enhanced T1-weighted MRI (CE-T1WI), and to compare the impact of ...
This study developed a machine learning (ML) model combining clinical and radiomic features to predict surgical difficulty in pheochromocytomas and paragangliomas (PPGLs), aiming to optimize preoperative planning and reduce perioperative complication...
BACKGROUND: Substantial gaps exist in the neuroprognostication of cardiac arrest patients who remain comatose after the restoration of spontaneous circulation. Most studies focus on predicting survival, a measure confounded by the withdrawal of life-...
RATIONALE AND OBJECTIVES: This study aims to develop and validate a deep learning radiomics signature (DLRS) that integrates radiomics and deep learning features for the non-invasive prediction of microvascular invasion (MVI) in patients with colon c...
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