Showing results (41-50 of 54) with videos related to

Sort By:
Pageof 6
Radiology. Artificial Intelligence|May 3, 2021
Fully Automated Segmentation of Head CT Neuroanatomy Using Deep LearningJason C Cai, Zeynettin Akkus, Kenneth A Philbrick, et al.
Radiology. Artificial Intelligence|December 16, 2022
Patient-specific Hip Arthroplasty Dislocation Risk Calculator: An Explainable Multimodal Machine Learning-based ApproachBardia Khosravi, Pouria Rouzrokh, Hilal Maradit Kremers, et al.
The Journal of Arthroplasty|March 18, 2023
Frank Stinchfield Award: Creation of a Patient-Specific Total Hip Arthroplasty Periprosthetic Fracture Risk CalculatorCody C Wyles, Hilal Maradit-Kremers, Kristin M Fruth, et al.
Research in Diagnostic and Interventional Imaging|July 30, 2024
Development of a deep learning model for the automated detection of green pixels indicative of gout on dual energy CT scanShahriar Faghani, Rhodes G Nicholas, Soham Patel, et al.
Spine Deformity|July 22, 2024
Deep learning classification of pediatric spinal radiographs for use in large scale imaging registriesKellen L Mulford, Christina M Regan, Julia E Todderud, et al.
The Journal of Arthroplasty|March 8, 2021
A Deep Learning Tool for Automated Radiographic Measurement of Acetabular Component Inclination and Version After Total Hip ArthroplastyPouria Rouzrokh, Cody C Wyles, Kenneth A Philbrick, et al.
Gastrointestinal Endoscopy|June 19, 2022
Development of a deep learning model for the histologic diagnosis of dysplasia in Barrett's esophagusShahriar Faghani, D Chamil Codipilly, David Vogelsang, et al.
Radiology. Artificial Intelligence|October 7, 2022
Mitigating Bias in Radiology Machine Learning: 3. Performance MetricsShahriar Faghani, Bardia Khosravi, Kuan Zhang, et al.
Journal of Digital Imaging|July 5, 2023
Reproducibility of Deep Learning Algorithms Developed for Medical Imaging Analysis: A Systematic ReviewMana Moassefi, Pouria Rouzrokh, Gian Marco Conte, et al.
Journal of Imaging Informatics in Medicine|November 18, 2024
RIDGE: Reproducibility, Integrity, Dependability, Generalizability, and Efficiency Assessment of Medical Image Segmentation ModelsFarhad Maleki, Linda Moy, Reza Forghani, et al.
Pageof 6