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Triaging ophthalmology outpatient referrals with machine learning: A pilot study.

Yiran Tan¹, Stephen Bacchi¹, Robert J Casson¹

¹South Australian Institute of Ophthalmology, Royal Adelaide Hospital, Adelaide, South Australia, Australia.

Clinical & Experimental Ophthalmology

|October 25, 2019

Summary

Machine learning (ML) shows promise in accurately triaging ophthalmology referrals, with convolutional neural networks (CNNs) achieving high accuracy. This AI application could improve patient care and resource allocation in ophthalmology services.

Keywords:

deep learning machine learning natural language processing ophthalmology

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Area of Science:

Ophthalmology
Medical Informatics
Artificial Intelligence

Background:

Efficient triaging of ophthalmology outpatient referrals is crucial for patient care and resource management.
Natural language processing, a subset of machine learning (ML), presents a potential tool for automating referral triage.

Purpose of the Study:

To evaluate the accuracy of machine learning models in predicting triage categories for ophthalmology outpatient referrals.
To compare the performance of different ML models in classifying referral priorities.

Main Methods:

A retrospective cohort study analyzed 208 ophthalmology outpatient referrals.
Synopses and triage categorizations were extracted, pre-processed, and used to train and test ML models.
Models were evaluated using the area under the receiver operator curve (AUC) and accuracy on an unseen test set.

Main Results:

A convolutional neural network (CNN) achieved the highest performance with an AUC of 0.83 and accuracy of 0.81 for binary classification.
An artificial neural network performed second best (AUC 0.81, accuracy 0.77).
CNN accuracy decreased to 0.65 when classifying referrals into three priority categories (one, two, or three).

Conclusions:

Machine learning, particularly CNNs, demonstrates potential for accurately assisting in the triage of ophthalmology referrals.
Further research with multi-center data and larger sample sizes is recommended to validate these findings.
ML-driven triage can potentially enhance efficiency and patient care pathways in ophthalmology.