Identification of ocular refraction based on deep learning algorithm as a novel retinoscopy method

Retinoscopes: Past and present

BACKGROUND

Retinoscopy is arguably the most important method in the eye clinic for diagnosing and managing refractive errors. Advantages of retinoscopy include its non-invasive nature, ability to assess patients of all ages, and usefulness in patients with limited cooperation or communication skills.

AIM

To discuss the history of retinoscopes and examine current literature on the subject.

METHODS

A search was conducted on the PubMed and with the reference citation analysis (https://www.referencecitationanalysis.com) database using the term “Retinoscopy,” with a range restricted to the last 10 years (2013-2023). The search string algorithm was: "Retinoscopy" (MeSH Terms) OR "Retinoscopy" (All Fields) OR "Retinoscopes" (All Fields) AND [(All Fields) AND 2013: 2023 (pdat)].

RESULTS

This systematic review included a total of 286 records. Publications reviewed iterations of the retinoscope into autorefractors, infrared photo retinoscope, television retinoscopy, and the Wifi enabled digital retinoscope.

CONCLUSION

The retinoscope has evolved significantly since its discovery, with a significant improvement in its diagnostic capabilities. While it has advantages such as non-invasiveness and broad applicability, limitations exist, and the need for skilled interpretation remains. With ongoing research, including the integration of artificial intelligence, retinoscopy is expected to continue advancing and playing a vital role in eye care.

Ocular image-based deep learning for predicting refractive error: A systematic review

Background

Uncorrected refractive error is a major cause of vision impairment worldwide and its increasing prevalent necessitates effective screening and management strategies. Meanwhile, deep learning, a subset of Artificial Intelligence, has significantly advanced ophthalmological diagnostics by automating tasks that required extensive clinical expertise. Although recent studies have investigated the use of deep learning models for refractive power detection through various imaging techniques, a comprehensive systematic review on this topic is has yet be done. This review aims to summarise and evaluate the performance of ocular image-based deep learning models in predicting refractive errors.

Main text

We search on three databases (PubMed, Scopus, Web of Science) up till June 2023, focusing on deep learning applications in detecting refractive error from ocular images. We included studies that had reported refractive error outcomes, regardless of publication years. We systematically extracted and evaluated the continuous outcomes (sphere, SE, cylinder) and categorical outcomes (myopia), ground truth measurements, ocular imaging modalities, deep learning models, and performance metrics, adhering to PRISMA guidelines. Nine studies were identified and categorised into three groups: retinal photo-based (n ​= ​5), OCT-based (n ​= ​1), and external ocular photo-based (n ​= ​3).For high myopia prediction, retinal photo-based models achieved AUC between 0.91 and 0.98, sensitivity levels between 85.10% and 97.80%, and specificity levels between 76.40% and 94.50%. For continuous prediction, retinal photo-based models reported MAE ranging from 0.31D to 2.19D, and R 2 between 0.05 and 0.96. The OCT-based model achieved an AUC of 0.79-0.81, sensitivity of 82.30% and 87.20% and specificity of 61.70%-68.90%. For external ocular photo-based models, the AUC ranged from 0.91 to 0.99, sensitivity of 81.13%-84.00% and specificity of 74.00%-86.42%, MAE ranges from 0.07D to 0.18D and accuracy ranges from 81.60% to 96.70%. The reported papers collectively showed promising performances, in particular the retinal photo-based and external eye photo -based DL models.

Conclusions

The integration of deep learning model and ocular imaging for refractive error detection appear promising. However, their real-world clinical utility in current screening workflow have yet been evaluated and would require thoughtful consideration in design and implementation.

Deep learning-based estimation of axial length using macular optical coherence tomography images

Background

This study aimed to develop deep learning models using macular optical coherence tomography (OCT) images to estimate axial lengths (ALs) in eyes without maculopathy.

Methods

A total of 2,664 macular OCT images from 444 patients' eyes without maculopathy, who visited Beijing Hospital between March 2019 and October 2021, were included. The dataset was divided into training, validation, and testing sets with a ratio of 6:2:2. Three pre-trained models (ResNet 18, ResNet 50, and ViT) were developed for binary classification (AL ≥ 26 mm) and regression task. Ten-fold cross-validation was performed, and Grad-CAM analysis was employed to visualize AL-related macular features. Additionally, retinal thickness measurements were used to predict AL by linear and logistic regression models.

Results

ResNet 50 achieved an accuracy of 0.872 (95% Confidence Interval [CI], 0.840-0.899), with high sensitivity of 0.804 (95% CI, 0.728-0.867) and specificity of 0.895 (95% CI, 0.861-0.923). The mean absolute error for AL prediction was 0.83 mm (95% CI, 0.72-0.95 mm). The best AUC, and accuracy of AL estimation using macular OCT images (0.929, 87.2%) was superior to using retinal thickness measurements alone (0.747, 77.8%). AL-related macular features were on the fovea and adjacent regions.

Conclusion

OCT images can be effectively utilized for estimating AL with good performance via deep learning. The AL-related macular features exhibit a localized pattern in the macula, rather than continuous alterations throughout the entire region. These findings can lay the foundation for future research in the pathogenesis of AL-related maculopathy.