A Proposed Framework for Machine Learning-Aided Triage in Public Specialty Ophthalmology Clinics in Hong Kong

Dual-Mode Imaging System for Early Detection and Monitoring of Ocular Surface Diseases

The global prevalence of ocular surface diseases (OSDs), such as dry eyes, conjunctivitis, and subconjunctival hemorrhage (SCH), is steadily increasing due to factors such as aging populations, environmental influences, and lifestyle changes. These diseases affect millions of individuals worldwide, emphasizing the importance of early diagnosis and continuous monitoring for effective treatment. Therefore, we present a deep learning-enhanced imaging system for the automated, objective, and reliable assessment of these three representative OSDs. Our comprehensive pipeline incorporates processing techniques derived from dual-mode infrared (IR) and visible (RGB) images. It employs a multi-stage deep learning model to enable accurate and consistent measurement of OSDs. This proposed method has achieved a 98.7% accuracy with an F1 score of 0.980 in class classification and a 96.2% accuracy with an F1 score of 0.956 in SCH region identification. Furthermore, our system aims to facilitate early diagnosis of meibomian gland dysfunction (MGD), a primary factor causing dry eyes, by quantitatively analyzing the meibomian gland (MG) area ratio and detecting gland morphological irregularities with an accuracy of 88.1% and an F1 score of 0.781. To enhance convenience and timely OSD management, we are integrating a portable IR camera for obtaining meibography during home inspections. Our system demonstrates notable improvements in expanding dual-mode image-based diagnosis for broader applicability, effectively enhancing patient care efficiency. With its automation, accuracy, and compact design, this system is well-suited for early detection and ongoing assessment of OSDs, contributing to improved eye healthcare in an accessible and comprehensible manner.

A Beginner's Guide to Artificial Intelligence for Ophthalmologists

The integration of artificial intelligence (AI) in ophthalmology has promoted the development of the discipline, offering opportunities for enhancing diagnostic accuracy, patient care, and treatment outcomes. This paper aims to provide a foundational understanding of AI applications in ophthalmology, with a focus on interpreting studies related to AI-driven diagnostics. The core of our discussion is to explore various AI methods, including deep learning (DL) frameworks for detecting and quantifying ophthalmic features in imaging data, as well as using transfer learning for effective model training in limited datasets. The paper highlights the importance of high-quality, diverse datasets for training AI models and the need for transparent reporting of methodologies to ensure reproducibility and reliability in AI studies. Furthermore, we address the clinical implications of AI diagnostics, emphasizing the balance between minimizing false negatives to avoid missed diagnoses and reducing false positives to prevent unnecessary interventions. The paper also discusses the ethical considerations and potential biases in AI models, underscoring the importance of continuous monitoring and improvement of AI systems in clinical settings. In conclusion, this paper serves as a primer for ophthalmologists seeking to understand the basics of AI in their field, guiding them through the critical aspects of interpreting AI studies and the practical considerations for integrating AI into clinical practice.

A machine learning system to optimise triage in an adult ophthalmic emergency department: a model development and validation study

Background

A substantial proportion of attendances to ophthalmic emergency departments are for non-urgent presentations. We developed and evaluated a machine learning system (DemDx Ophthalmology Triage System: DOTS) to optimise triage, with the aim of reducing inappropriate emergency attendances and streamlining case referral when necessary.

Methods

DOTS was built using retrospective tabular data from 11,315 attendances between July 1st, 2021, to June 15th, 2022 at Moorfields Eye Hospital Emergency Department (MEH) in London, UK. Demographic and clinical features were used as inputs and a triage recommendation was given ("see immediately", "see within a week", or "see electively"). DOTS was validated temporally and compared with triage nurses' performance (1269 attendances at MEH) and validated externally (761 attendances at the Federal University of Minas Gerais - UFMG, Brazil). It was also tested for biases and robustness to variations in disease incidences. All attendances from patients aged at least 18 years with at least one confirmed diagnosis were included in the study.

Findings

For identifying ophthalmic emergency attendances, on temporal validation, DOTS had a sensitivity of 94.5% [95% CI 92.3-96.1] and a specificity of 42.4% [38.8-46.1]. For comparison within the same dataset, triage nurses had a sensitivity of 96.4% [94.5-97.7] and a specificity of 25.1% [22.0-28.5]. On external validation at UFMG, DOTS had a sensitivity of 95.2% [92.5-97.0] and a specificity of 32.2% [27.4-37.0]. In simulated scenarios with varying disease incidences, the sensitivity was ≥92.2% and the specificity was ≥36.8%. No differences in sensitivity were found in subgroups of index of multiple deprivation, but the specificity was higher for Q2 when compared to Q4 (Q4 is less deprived than Q2).

Interpretation

At MEH, DOTS had similar sensitivity to triage nurses in determining attendance priority; however, with a specificity of 17.3% higher, DOTS resulted in lower rates of patients triaged to be seen immediately at emergency. DOTS showed consistent performance in temporal and external validation, in social-demographic subgroups and was robust to varying relative disease incidences. Further trials are necessary to validate these findings. This system will be prospectively evaluated, considering human-computer interaction, in a clinical trial.

Funding

The Artificial Intelligence in Health and Care Award (AI_AWARD01671) of the NHS AI Lab under National Institute for Health and Care Research (NIHR) and the Accelerated Access Collaborative (AAC).