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Implementation of an AI model to triage paediatric brain magnetic resonance imaging orders

2022; Academy of Medicine, Singapore; Volume: 51; Issue: 11 Linguagem: Inglês

10.47102/annals-acadmedsg.2022104

0304-4602

Phua Hwee Tang, Alwin Yaoxian Zhang, Sean Shao Wei Lam, Marcus Eng Hock Ong, Ling Ling Chan,

Radiology practices and education

Artificial intelligence (AI) is viewed as the most important recent advancement in radiology with the potential to achieve Singapore's objective of delivering value-based patient-centric care. 1 We have developed and implemented a deep-learning model using bidirectional long short-term memory (Bi-LSTM) neural network to enable automated triage of unstructured free-text paediatric magnetic resonance imaging (MRI) brain orders in conformance to the American College of Radiology (ACR) criteria 2 for appropriate utilisation of MRI.These ACR guidelines assist clinicians in the appropriate triaging of brain MRI orders for routine imaging, versus ultrafast MRI screening protocols for less appropriate orders.After approval of waiver of consent from the Institution Review Board (CIRB reference number 2017/2078), data comprising 5,181 retrospective paediatric MRI brain orders (online Supplementary Table S1) extracted from 2006 to 2017 (excluding those with additional scans of other body parts and follow-up scans) were manually labelled for conformance to the ACR guidelines 2 under supervision of a senior paediatric radiologist.These were used as ground truth to develop a Bi-LSTM and other machine learning models to classify these free-text orders based on adherence to the ACR guidelines.Initially 2,470 orders from 2006 to 2013 were used for model training (80-20 training and validation split), and 2,711 orders from 2014 to 2017 for model testing, using receiver operating characteristics to measure model performance (online Supplementary Table S2).Another 50 orders from a 2020 audit were used for simulated implementation of the best performing model predicting MRI orders conforming to ACR guidelines, 2 comparing its performance against radiology staff with variable experience (including the aforesaid senior paediatric radiologist as gold standard), using Cohen's kappa statistics (online Supplementary Table S3).The model graphic user interface (Fig. 1) and details of its creation and testing are attached in the online Supplementary Materials.The highest accuracy and area under the curve (AUC) were seen with the Bi-LSTM model (Supplementary Table S2).This model, utilised by a non-medical staff