Evaluating the Robustness of a Deep Learning Bone Age Algorithm to Clinical Image Variation Using Computational Stress Testing-Reference-Cited by-同舟云学术

Evaluating the Robustness of a Deep Learning Bone Age Algorithm to Clinical Image Variation Using Computational Stress Testing

Published:2024-05-01 Issue:3 Volume:6 Page:
ISSN:2638-6100
Container-title:Radiology: Artificial Intelligence
language:en
Short-container-title:Radiology: Artificial Intelligence

Author:

Santomartino Samantha M.¹^ORCID,Putman Kristin¹^ORCID,Beheshtian Elham¹^ORCID,Parekh Vishwa S.¹,Yi Paul H.¹^ORCID

Affiliation:

1. From the Drexel University College of Medicine, Philadelphia, Pa (S.M.S.); University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, 670 W Baltimore St, 1st Fl, Room 1172, Baltimore, MD 21201 (S.M.S., K.P., E.B., V.S.P., P.H.Y.); and Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, Md (P.H.Y.).

Publisher

Radiological Society of North America (RSNA)

Link

http://pubs.rsna.org/doi/pdf/10.1148/ryai.230240

Reference16 articles.

1. AI for medical imaging goes deep

2. Stress testing reveals gaps in clinic readiness of image-based diagnostic artificial intelligence models

3. Human–computer collaboration for skin cancer recognition

4. The RSNA Pediatric Bone Age Machine Learning Challenge

5. Can AI distinguish a bone radiograph from photos of flowers or cars? Evaluation of bone age deep learning model on inappropriate data inputs

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