Application of artificial intelligence in anterior segment ophthalmic diseases: diversity and standardization

With the advancement of computational power, refinement of learning algorithms and architectures, and availability of big data, artificial intelligence (AI) technology, particularly with machine learning and deep learning, is paving the way for ‘intelligent’ healthcare systems. AI-related research in ophthalmology previously focused on the screening and diagnosis of posterior segment diseases, particularly diabetic retinopathy, age-related macular degeneration and glaucoma. There is now emerging evidence demonstrating the application of AI to the diagnosis and management of a variety of anterior segment conditions. In this review, we provide an overview of AI applications to the anterior segment addressing keratoconus, infectious keratitis, refractive surgery, corneal transplant, adult and paediatric cataracts, angle-closure glaucoma and iris tumour, and highlight important clinical considerations for adoption of AI technologies, potential integration with telemedicine and future directions.

Download Full-text

Big-data and artificial-intelligence-assisted vault prediction and EVO-ICL size selection for myopia correction

British Journal of Ophthalmology ◽

10.1136/bjophthalmol-2021-319618 ◽

2021 ◽

pp. bjophthalmol-2021-319618

Author(s):

Yang Shen ◽

Lin Wang ◽

Weijun Jian ◽

Jianmin Shang ◽

Xin Wang ◽

...

Keyword(s):

Artificial Intelligence ◽

Big Data ◽

Random Forest ◽

Anterior Segment ◽

Big Data Analytics ◽

Area Under The Curve ◽

Gradient Boosting ◽

Implantable Collamer Lens ◽

Surgical Strategies ◽

The Mean

AimsTo predict the vault and the EVO-implantable collamer lens (ICL) size by artificial intelligence (AI) and big data analytics.MethodsSix thousand two hundred and ninety-seven eyes implanted with an ICL from 3536 patients were included. The vault values were measured by the anterior segment analyzer (Pentacam HR). Permutation importance and Impurity-based feature importance are used to investigate the importance between the vault and input parameters. Regression models and classification models are applied to predict the vault. The ICL size is set as the target of the prediction, and the vault and the other input features are set as the new inputs for the ICL size prediction. Data were collected from 2015 to 2020. Random Forest, Gradient Boosting and XGBoost were demonstrated satisfying accuracy and mean area under the curve (AUC) scores in vault predicting and ICL sizing.ResultsIn the prediction of the vault, the Random Forest has the best results in the regression model (R2=0.315), then follows the Gradient Boosting (R2=0.291) and XGBoost (R2=0.285). The maximum classification accuracy is 0.828 in Random Forest, and the mean AUC is 0.765. The Random Forest predicts the ICL size with an accuracy of 82.2% and the Gradient Boosting and XGBoost, which are also compatible with 81.5% and 81.8% accuracy, respectively.ConclusionsRandom Forest, Gradient Boosting and XGBoost models are applicable for vault predicting and ICL sizing. AI may assist ophthalmologists in improving ICL surgery safety, designing surgical strategies, and predicting clinical outcomes.

Download Full-text

Ultrasonographic assessment of ophthalmic diseases in low-income countries

Tropical Doctor ◽

10.1177/0049475518787379 ◽

2018 ◽

Vol 48 (4) ◽

pp. 294-297 ◽

Cited By ~ 3

Author(s):

Amar Pujari ◽

Deepa R Swamy ◽

Rashmi Singh ◽

Ritika Mukhija ◽

Rohan Chawla ◽

...

Keyword(s):

Low Income ◽

Diagnostic Tool ◽

Low Cost ◽

Anterior Segment ◽

Posterior Segment ◽

Low Income Countries ◽

Patient Volume ◽

Non Invasive ◽

Ophthalmic Diseases ◽

Ultrasonographic Assessment

We undertook a study between December 2016 and February 2017 on 1637 of 2101 patients with clearly documented findings. These underwent ocular B-scan ultrasonography (USG). Their ages were in the range of 10 days to 92 years; among these patients, 921 (56.26%) were male and 224 (13.68%) were children. Among the adults, 669 (40.86%) patients had anterior segment and 636 (38.85%) had posterior segment pathology. In addition, there were 108 (6.59%) with orbital pathology. Our experience is that USG is an effective, quick, low-cost and non-invasive diagnostic tool for the diagnosis of various ocular and orbital conditions in high patient volume centres (including children and adults) especially where resources are limited.

Download Full-text

Artificial intelligence as a tool to aid in the differentiation of equine ophthalmic diseases with an emphasis on equine uveitis

Equine Veterinary Journal ◽

10.1111/evj.13528 ◽

2021 ◽

Author(s):

Anna May ◽

Stefan Gesell‐May ◽

Tobias Müller ◽

Wolfgang Ertel

Keyword(s):

Artificial Intelligence ◽

Ophthalmic Diseases

Download Full-text

Advances in Imaging Technology of Anterior Segment of the Eye

Journal of Ophthalmology ◽

10.1155/2021/9539765 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Sang Beom Han ◽

Yu-Chi Liu ◽

Karim Mohamed-Noriega ◽

Jodhbir S. Mehta

Keyword(s):

Artificial Intelligence ◽

Optical Coherence Tomography ◽

Clinical Practice ◽

Computer Science ◽

Anterior Segment ◽

Comprehensive Understanding ◽

New Techniques ◽

Ultrahigh Resolution ◽

Imaging Technology ◽

Precise Assessment

Advances in imaging technology and computer science have allowed the development of newer assessment of the anterior segment, including Corvis ST, Brillouin microscopy, ultrahigh-resolution optical coherence tomography, and artificial intelligence. They enable accurate and precise assessment of structural and biomechanical alterations associated with anterior segment disorders. This review will focus on these 4 new techniques, and a brief overview of these modalities will be introduced. The authors will also discuss the recent advances in research regarding these techniques and potential application of these techniques in clinical practice. Many studies on these modalities have reported promising results, indicating the potential for more detailed comprehensive understanding of the anterior segment tissues.

Download Full-text