Implementation of an AI model to triage paediatric brain magnetic resonance imaging orders
Author:
Affiliation:
1. KK Women’s and Children’s Hospital, Singapore
2. Health Service Research Centre, SingHealth, Singapore
3. Singapore General Hospital, Singapore
Funder
SingHealth
National Medical Research Council
Publisher
Academy of Medicine, Singapore
Subject
General Medicine
Reference10 articles.
1. Liew CJ, Krishnaswamy P, Cheng LT, et al. Artificial Intelligence and Radiology in Singapore: Championing a New Age of Augmented Imaging for Unsurpassed Patient Care. Ann Acad Med Singap 2019;48:16-24.
2. American College of Radiology. ACR–ASNR–SPR practice parameter for the performance and interpretation of magnetic resonance imaging (MRI) of the brain, 2013. https://www.asnr. org/wp-content/uploads/2019/06/MR-Brain.pdf. Accessed 14 April 2017.
3. Chen MC, Ball RL, Yang L, et al. Deep learning to classify radiology free-text reports. Radiology 2018;286:845-52.
4. Hassanpour S, Langlotz CP, Amrhein TJ, et al. Performance of a Machine Learning Classifier of Knee MRI Reports in Two Large Academic Radiology Practices: A Tool to Estimate Diagnostic Yield. AJR Am J Roentgenol 2017;208:750-3.
5. Huhdanpaa HT, Tan WK, Rundell SD, et al. Using Natural Language Processing of Free-Text Radiology Reports to Identify Type 1 Modic Endplate Changes. J Digit Imaging 2018;31:84-90.
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