Taking Matters into Your Own Hands
Author:
Affiliation:
1. From the Department of Radiology, Stanford University School of Medicine, 300 Pasteur Dr, MC 5105, Stanford, CA 94305.
Publisher
Radiological Society of North America (RSNA)
Subject
Artificial Intelligence,Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology
Link
http://pubs.rsna.org/doi/pdf/10.1148/ryai.2020200150
Reference10 articles.
1. Fully Automated Deep Learning System for Bone Age Assessment
2. Performance of a Deep-Learning Neural Network Model in Assessing Skeletal Maturity on Pediatric Hand Radiographs
3. The RSNA Pediatric Bone Age Machine Learning Challenge
4. Rethinking Greulich and Pyle: A Deep Learning Approach to Pediatric Bone Age Assessment Using Pediatric Trauma Hand Radiographs
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1. Bone Age Prediction under Stress;Radiology: Artificial Intelligence;2024-05-01
2. How to apply evidence-based practice to the use of artificial intelligence in radiology (EBRAI) using the data algorithm training output (DATO) method;The British Journal of Radiology;2023-10
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