Understanding Corneal Epithelial Thickness Mapping

Strategies for Early Keratoconus Diagnosis: A Narrative Review of Evaluating Affordable and Effective Detection Techniques

Keratoconus is a progressive corneal disorder that can lead to irreversible visual impairment if not detected early. Despite its high prevalence, early diagnosis is often delayed, especially in low-to-middle-income countries due to limited awareness and restricted access to advanced diagnostic tools such as corneal topography, tomography, optical coherence tomography, and corneal biomechanical assessments. These technologies are essential for identifying early-stage keratoconus, yet their high cost limits accessibility in resource-limited settings. While cost and portability are important for accessibility, the sensitivity and specificity of diagnostic tools must be considered as primary metrics to ensure accurate and effective detection of early keratoconus. This review examines both traditional and advanced diagnostic techniques, including the use of machine learning and artificial intelligence, to enhance early diagnosis. Artificial intelligence-based approaches show significant potential for transforming keratoconus diagnosis by improving the accuracy and sensitivity of early diagnosis, especially when combined with imaging devices. Notable innovations include tools such as SmartKC, a smartphone-based machine-learning application, mobile corneal topography through the null-screen test, and the Smartphone-based Keratograph, providing affordable and portable solutions. Additionally, contrast sensitivity testing demonstrates potential for keratoconus detection, although a precise platform for routine clinical use has yet to be established. The review emphasizes the need for increased awareness among clinicians, particularly in underserved regions, and advocates for the development of accessible, low-cost diagnostic tools. Further research is needed to validate the effectiveness of these emerging technologies in detecting early keratoconus.

Utility of artificial intelligence in the diagnosis and management of keratoconus: a systematic review

Introduction

The application of artificial intelligence (AI) systems in ophthalmology is rapidly expanding. Early detection and management of keratoconus is important for preventing disease progression and the need for corneal transplant. We review studies regarding the utility of AI in the diagnosis and management of keratoconus and other corneal ectasias.

Methods

We conducted a systematic search for relevant original, English-language research studies in the PubMed, Web of Science, Embase, and Cochrane databases from inception to October 31, 2023, using a combination of the following keywords: artificial intelligence, deep learning, machine learning, keratoconus, and corneal ectasia. Case reports, literature reviews, conference proceedings, and editorials were excluded. We extracted the following data from each eligible study: type of AI, input used for training, output, ground truth or reference, dataset size, availability of algorithm/model, availability of dataset, and major study findings.

Results

Ninety-three original research studies were included in this review, with the date of publication ranging from 1994 to 2023. The majority of studies were regarding the use of AI in detecting keratoconus or subclinical keratoconus (n=61). Among studies regarding keratoconus diagnosis, the most common inputs were corneal topography, Scheimpflug-based corneal tomography, and anterior segment-optical coherence tomography. This review also summarized 16 original research studies regarding AI-based assessment of severity and clinical features, 7 studies regarding the prediction of disease progression, and 6 studies regarding the characterization of treatment response. There were only three studies regarding the use of AI in identifying susceptibility genes involved in the etiology and pathogenesis of keratoconus.

Discussion

Algorithms trained on Scheimpflug-based tomography seem promising tools for the early diagnosis of keratoconus that can be particularly applied in low-resource communities. Future studies could investigate the application of AI models trained on multimodal patient information for staging keratoconus severity and tracking disease progression.

Multi-modal imaging for the detection of early keratoconus: a narrative review

Keratoconus is a common progressive corneal disorder that can be associated with significant ocular morbidity. Various corneal imaging techniques have been used for the diagnosis of established cases. However, in the early stages of the disease, which include subclinical keratoconus and forme fruste keratoconus, detection of such cases can be challenging. The importance of detecting such cases is very important because early intervention can halt disease progression, improve visual outcomes and prevent postrefractive surgery ectasia associated with performing corneal refractive procedures in such patients. This narrative review aimed to examine several established and evolving imaging techniques for the detection of early cases of keratoconus. The utilization of combinations of these techniques may further increase their diagnostic ability.