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

Li, Yalsin Yik Sum; Vardhanabhuti, Varut; Tsougenis, Efstratios; Lam, Wai Ching; Shih, Kendrick Co

doi:10.1007/s40123-021-00405-7

Cited by 5 publications

(2 citation statements)

References 39 publications

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“…The rationale for setting an operating threshold during the training phase must be described, and sensitivity and specificity must be demonstrated on independent datasets using a consistent operating threshold [ 32 ]. The development process typically involves the separation of the dataset into a development subset and a test subset [ 33 ]. It is imperative that this partitioning is executed in a manner that preserves the overall representativeness of the datasets and adequately addresses any potential class imbalances, leveraged to refine the model’s architecture and optimize its training phase.…”

Section: Machine Learning In Ophthalmologymentioning

confidence: 99%

A Beginner’s Guide to Artificial Intelligence for Ophthalmologists

Kang,

Wu,

Yuan

et al. 2024

Ophthalmol Ther

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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.

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Section: Machine Learning In Ophthalmologymentioning

confidence: 99%

A Beginner’s Guide to Artificial Intelligence for Ophthalmologists

Kang,

Wu,

Yuan

et al. 2024

Ophthalmol Ther

View full text Add to dashboard Cite

show abstract

“…Machine learning (ML) is being increasingly applied to improve clinical workflow efficiency and has the potential to enhance the accuracy of triage, optimising service allocation. 7 Within triage, ML has the capability to process high dimensionality structured data and the potential to achieve superior performance compared to rule-based algorithms by abstracting complex non-linear patterns between patients’ clinical presentation and their clinical risk. One study proposed an ophthalmic self-triage model using metadata and smartphone images but was tested only on 103 patients, included only 18 possible differentials, and did not consider the potential increase of non-urgent presentations to emergency departments, aggravating professional burden and increasing healthcare costs.…”

Section: Introductionmentioning

confidence: 99%