AI Medical Compendium Journal:
AJR. American journal of roentgenology

Showing 101 to 110 of 169 articles

Fully Automated Deep Learning Tool for Sarcopenia Assessment on CT: L1 Versus L3 Vertebral Level Muscle Measurements for Opportunistic Prediction of Adverse Clinical Outcomes.

AJR. American journal of roentgenology
Sarcopenia is associated with adverse clinical outcomes. CT-based skeletal muscle measurements for sarcopenia assessment are most commonly performed at the L3 vertebral level. The purpose of this article is to compare the utility of fully automated...

Mammographic Surveillance After Breast-Conserving Therapy: Impact of Digital Breast Tomosynthesis and Artificial Intelligence-Based Computer-Aided Detection.

AJR. American journal of roentgenology
Postoperative mammograms present interpretive challenges due to postoperative distortion and hematomas. The application of digital breast tomosyn-thesis (DBT) and artificial intelligence-based computer-aided detection (AI-CAD) after breast-conservin...

Ensuring Adequate Development and Appropriate Use of Artificial Intelligence in Pediatric Medical Imaging.

AJR. American journal of roentgenology
Of over 100 FDA-cleared artificial intelligence (AI) tools for triage, detection, or diagnosis in medical imaging, only one is cleared for use in children. Thus, children may be unable to benefit from the advances that AI provides to adults. Furtherm...

Development of MRI-Based Radiomics Model to Predict the Risk of Recurrence in Patients With Advanced High-Grade Serous Ovarian Carcinoma.

AJR. American journal of roentgenology
The purpose of our study was to develop a radiomics model based on preoperative MRI and clinical information for predicting recurrence-free survival (RFS) in patients with advanced high-grade serous ovarian carcinoma (HGSOC). This retrospective stu...

Protocol Optimization Considerations for Implementing Deep Learning CT Reconstruction.

AJR. American journal of roentgenology
Previous advances over filtered back projection (FBP) have incorporated model-based iterative reconstruction. The purpose of this study was to characterize the latest advance in image reconstruction, that is, deep learning. The focus was on applying...

A 3D-2D Hybrid U-Net Convolutional Neural Network Approach to Prostate Organ Segmentation of Multiparametric MRI.

AJR. American journal of roentgenology
OBJECTIVE: Prostate cancer is the most commonly diagnosed cancer in men in the United States with more than 200,000 new cases in 2018. Multiparametric MRI (mpMRI) is increasingly used for prostate cancer evaluation. Prostate organ segmentation is an ...