Development of lumbar spine MRI referrals vetting models using machine learning and deep learning algorithms:...

Referrals vetting is a necessary daily task to ensure the appropriateness of radiology referrals. Vetting requires extensive clinical knowledge and may challenge those responsible. This study aims to develop AI models to automate the vetting process and to compare their performance with healthcare professionals.

1020 lumbar spine MRI referrals were collected retrospectively from two Irish hospitals. Three expert MRI radiographers classified the referrals into indicated or not indicated for scanning based on iRefer guidelines. The reference label for each referral was assigned based on the majority voting. The corpus was divided into two datasets, one for the models' development with 920 referrals, and one included 100 referrals used as a held-out for the final comparison of the AI models versus national and international MRI radiographers. Three traditional models were developed: SVM, LR, RF, and two deep neural models, including CNN and Bi-LSTM. For the traditional models, four vectorisation techniques applied: BoW, bigrams, trigrams, and TF-IDF. A textual data augmentation technique was applied to investigate the influence of data augmentation on the models' performances.

RF with BoW achieved the highest AUC reaching 0.99. CNN model outperformed Bi-LSTM with AUC = 0.98. With the augmented dataset, the performance significantly improved with an increase in F1 scores ranging from 1% to 7%. All models outperformed the national and international radiographers when compared on the hold-out dataset.

The models assigned the referrals' appropriateness with higher accuracies than the national and international radiographers. Applying data augmentation significantly improved the models' performances.

The outcomes suggest that the use of AI for checking referrals' eligibility could serve as a supporting tool to improve the referrals' management in radiology departments.