AIMC Topic: Retrospective Studies

Clear Filters Showing 3871 to 3880 of 9989 articles

Improving the Efficacy of ACR TI-RADS Through Deep Learning-Based Descriptor Augmentation.

Journal of digital imaging
Thyroid nodules occur in up to 68% of people, 95% of which are benign. Of the 5% of malignant nodules, many would not result in symptoms or death, yet 600,000 FNAs are still performed annually, with a PPV of 5-7% (up to 30%). Artificial intelligence ...

Learning Curves for Robot-Assisted Pedicle Screw Placement: Analysis of Operative Time for 234 Cases.

Operative neurosurgery (Hagerstown, Md.)
BACKGROUND AND OBJECTIVES: Robot-assisted pedicle screw placement is associated with greater accuracy, reduced radiation, less blood loss, shorter hospital stays, and fewer complications than freehand screw placement. However, it can be associated wi...

Physics-informed deep learning for T2-deblurred superresolution turbo spin echo MRI.

Magnetic resonance in medicine
PURPOSE: Deep learning superresolution (SR) is a promising approach to reduce MRI scan time without requiring custom sequences or iterative reconstruction. Previous deep learning SR approaches have generated low-resolution training images by simple k...

Comparisons of the Safety and Effectiveness of Robot-Assisted Laparoscopic Partial Nephrectomy for Central Renal Angiomyolipomas: A Propensity Score-Matched Analysis Study.

Journal of endourology
To compare the safety and effectiveness of robot-assisted partial nephrectomy (RAPN) laparoscopic partial nephrectomy (LPN) in the treatment of central renal angiomyolipomas (AMLs). We retrospectively analyzed the clinical data of 103 patients who...

Deep learning based automatic segmentation of organs-at-risk for 0.35 T MRgRT of lung tumors.

Radiation oncology (London, England)
BACKGROUND AND PURPOSE: Magnetic resonance imaging guided radiotherapy (MRgRT) offers treatment plan adaptation to the anatomy of the day. In the current MRgRT workflow, this requires the time consuming and repetitive task of manual delineation of or...

Deep learning-based radiomics model can predict extranodal soft tissue metastasis in gastric cancer.

Medical physics
BACKGROUND: The potential prognostic value of extranodal soft tissue metastasis (ESTM) has been confirmed by increasing studies about gastric cancer (GC). However, the gold standard of ESTM is determined by pathologic examination after surgery, and t...

Federated Learning: A Cross-Institutional Feasibility Study of Deep Learning Based Intracranial Tumor Delineation Framework for Stereotactic Radiosurgery.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Deep learning-based segmentation algorithms usually required large or multi-institute data sets to improve the performance and ability of generalization. However, protecting patient privacy is a key concern in the multi-institutional stud...

Deep learning-based assessment of CT markers of sarcopenia and myosteatosis for outcome assessment in patients with advanced pancreatic cancer after high-intensity focused ultrasound treatment.

European radiology
OBJECTIVES: To evaluate the prognostic value of CT-based markers of sarcopenia and myosteatosis in comparison to the Eastern Cooperative Oncology Group (ECOG) score for survival of patients with advanced pancreatic cancer treated with high-intensity ...

Towards reproducible radiomics research: introduction of a database for radiomics studies.

European radiology
OBJECTIVES: To investigate the model-, code-, and data-sharing practices in the current radiomics research landscape and to introduce a radiomics research database.

A Deep Learning Approach to Using Wearable Seismocardiography (SCG) for Diagnosing Aortic Valve Stenosis and Predicting Aortic Hemodynamics Obtained by 4D Flow MRI.

Annals of biomedical engineering
In this paper, we explored the use of deep learning for the prediction of aortic flow metrics obtained using 4-dimensional (4D) flow magnetic resonance imaging (MRI) using wearable seismocardiography (SCG) devices. 4D flow MRI provides a comprehensiv...