Automated cornea diagnosis using deep convolutional neural networks based on cornea topography maps

Artificial intelligence applications in cataract and refractive surgeries

PURPOSE OF REVIEW

This review highlights the recent advancements in the applications of artificial intelligence within the field of cataract and refractive surgeries. Given the rapid evolution of artificial intelligence technologies, it is essential to provide an updated overview of the significant strides and emerging trends in this field.

RECENT FINDINGS

Key themes include artificial intelligence-assisted diagnostics and intraoperative support, image analysis for anterior segment surgeries, development of artificial intelligence-based diagnostic scores and calculators for early disease detection and treatment planning, and integration of generative artificial intelligence for patient education and postoperative monitoring.

SUMMARY

The impact of artificial intelligence on cataract and refractive surgeries is becoming increasingly evident through improved diagnostic accuracy, enhanced patient education, and streamlined clinical workflows. These advancements hold significant implications for clinical practice, promising more personalized patient care and facilitating early disease detection and intervention. Equally, the review also highlights the fact that only some of this work reaches the clinical stage, successful integration of which may benefit from our focus.

Deep Learning Models Used in the Diagnostic Workup of Keratoconus: A Systematic Review and Exploratory Meta-Analysis

PURPOSE

The prevalence of keratoconus in the general population is reported to be up to 1 of 84. Over the past 2 decades, diagnosis and management evolved rapidly, but keratoconus screening in clinical practice is still challenging and asks for improving the accuracy of keratoconus detection. Deep learning (DL) offers considerable promise for improving the accuracy and speed of medical imaging interpretation. We establish an inventory of studies conducted with DL algorithms that have attempted to diagnose keratoconus.

METHODS

This systematic review was conducted according to the recommendations of the PRISMA statement. We searched (Pre-)MEDLINE, Embase, Science Citation Index, Conference Proceedings Citation Index, arXiv document server, and Google Scholar from inception to February 18, 2022. We included studies that evaluated the performance of DL algorithms in the diagnosis of keratoconus. The main outcome was diagnostic performance measured as sensitivity and specificity, and the methodological quality of the included studies was assessed using QUADAS-2.

RESULTS

Searches retrieved 4100 nonduplicate records, and we included 19 studies in the qualitative synthesis and 10 studies in the exploratory meta-analysis. The overall study quality was limited because of poor reporting of patient selection and the use of inadequate reference standards. We found a pooled sensitivity of 97.5% (95% confidence interval, 93.6%-99.0%) and a pooled specificity of 97.2% (95% confidence interval, 85.7%-99.5%) for topography images as input.

CONCLUSIONS

Our systematic review found that the overall diagnostic performance of DL models to detect keratoconus was good, but the methodological quality of included studies was modest.

Blast injury: Impact to the cornea

Visual disorders are common even after mild traumatic brain injury (mTBI) or blast exposure. The cost of blast-induced vision loss in civilians, military personnel, and veterans is significant. The visual consequences of blasts associated with TBI are elusive. Active military personnel and veterans report various ocular pathologies including corneal disorders post-combat blasts. The wars and conflicts in Afghanistan, Iraq, Syria, and Ukraine have significantly increased the number of corneal and other ocular disorders among military personnel and veterans. Binocular vision, visual fields, and other visual functions could be impaired following blast-mediated TBI. Blast-associated injuries can cause visual disturbances, binocular system problems, and visual loss. About 25% of veterans exposed to blasts report corneal injury. Blast exposure induces corneal edema, corneal opacity, increased corneal thickness, damage of corneal epithelium, corneal abrasions, and stromal and endothelial abnormality including altered endothelial density, immune cell infiltration, corneal neovascularization, Descemet membrane rupture, and increased pain mediators in animal models and the blast-exposed military personnel including veterans. Immune response exacerbates blast-induced ocular injury. TBI is associated with dry eyes and pain in veterans. Subjects exposed to blasts that cause TBI should undergo immediate clinical visual and ocular examinations. Delayed visual care may lead to progressive vision loss, lengthening/impairing rehabilitation and ultimately may lead to permanent vision problems and blindness. Open-field blast exposure could induce corneal injuries and immune responses in the cornea. Further studies are warranted to understand corneal pathology after blast exposure. A review of current advancements in blast-induced corneal injury will help elucidate novel targets for potential therapeutic options. This review discusses the impact of blast exposure-associated corneal disorders.

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.

Keratoconus disease classification with multimodel fusion and vision transformer: a pretrained model approach

OBJECTIVE

Our objective is to develop a novel keratoconus image classification system that leverages multiple pretrained models and a transformer architecture to achieve state-of-the-art performance in detecting keratoconus.

METHODS AND ANALYSIS

Three pretrained models were used to extract features from the input images. These models have been trained on large datasets and have demonstrated strong performance in various computer vision tasks.The extracted features from the three pretrained models were fused using a feature fusion technique. This fusion aimed to combine the strengths of each model and capture a more comprehensive representation of the input images. The fused features were then used as input to a vision transformer, a powerful architecture that has shown excellent performance in image classification tasks. The vision transformer learnt to classify the input images as either indicative of keratoconus or not.The proposed method was applied to the Shahroud Cohort Eye collection and keratoconus detection dataset. The performance of the model was evaluated using standard evaluation metrics such as accuracy, precision, recall and F1 score.

RESULTS

The research results demonstrated that the proposed model achieved higher accuracy compared with using each model individually.

CONCLUSION

The findings of this study suggest that the proposed approach can significantly improve the accuracy of image classification models for keratoconus detection. This approach can serve as an effective decision support system alongside physicians, aiding in the diagnosis of keratoconus and potentially reducing the need for invasive procedures such as corneal transplantation in severe cases.

A Deep Feature Fusion of Improved Suspected Keratoconus Detection with Deep Learning

Detection of early clinical keratoconus (KCN) is a challenging task, even for expert clinicians. In this study, we propose a deep learning (DL) model to address this challenge. We first used Xception and InceptionResNetV2 DL architectures to extract features from three different corneal maps collected from 1371 eyes examined in an eye clinic in Egypt. We then fused features using Xception and InceptionResNetV2 to detect subclinical forms of KCN more accurately and robustly. We obtained an area under the receiver operating characteristic curves (AUC) of 0.99 and an accuracy range of 97-100% to distinguish normal eyes from eyes with subclinical and established KCN. We further validated the model based on an independent dataset with 213 eyes examined in Iraq and obtained AUCs of 0.91-0.92 and an accuracy range of 88-92%. The proposed model is a step toward improving the detection of clinical and subclinical forms of KCN.