An improved strabismus screening method with combination of meta-learning and image processing under data scarcity

Artificial Intelligence in Optometry: Current and Future Perspectives

With the global shortage of eye care professionals and the increasing burden of vision impairment, particularly in low- and middle-income countries, there is an urgent need for innovative solutions to bridge gaps in eye care services. Advances in artificial intelligence (AI) over recent decades have significantly impacted healthcare, including the field of optometry. When integrated into optometric workflows, AI has the potential to streamline decision-making processes and enhance system efficiency. To realize this potential, it is essential to develop AI models that can improve each stage of the patient care workflow, including screening, detection, diagnosis, and management. This review explores the application of AI in optometry, focusing on its potential to optimize various aspects of patient care. We examined AI models across key areas in optometry. Our analysis considered crucial parameters, including model selection, sample sizes for training and validation, evaluation metrics, and the explainability of the models. This comprehensive review identified both the strengths and weaknesses of existing AI models. The majority of image-based studies utilized CNN or transfer learning models, while clinical data-based studies primarily employed RF, SVM, and XGBoost. In general, AI models trained on large datasets achieved higher accuracy. However, many optometry-focused models faced limitations due to insufficient sample sizes-28% of studies were trained on fewer than 500 samples, 18% used fewer than 200 samples, and over half validated their models on fewer than 500 samples, with 38% validating on fewer than 200. Additionally, some studies that used the same data for both training and validation experienced overfitting, leading to reduced accuracy. Notably, 20% of the included studies reported accuracy below 80%, limiting their practical applicability in clinical settings. This review provides optometrists with valuable insights into the strengths and weaknesses of AI models in the field, aiding in their informed implementation in clinical settings.

An artificial intelligence platform for the screening and managing of strabismus

OBJECTIVES

Considering the escalating incidence of strabismus and its consequential jeopardy to binocular vision, there is an imperative demand for expeditious and precise screening methods. This study was to develop an artificial intelligence (AI) platform in the form of an applet that facilitates the screening and management of strabismus on any mobile device.

METHODS

The Visual Transformer (VIT_16_224) was developed using primary gaze photos from two datasets covering different ages. The AI model was evaluated by 5-fold cross-validation set and tested on an independent test set. The diagnostic performance of the AI model was assessed by calculating the Accuracy, Precision, Specificity, Sensitivity, F1-Score and Area Under the Curve (AUC).

RESULTS

A total of 6194 photos with corneal light-reflection (with 2938 Exotropia, 1415 Esotropia, 739 Vertical Deviation and 1562 Orthotropy) were included. In the internal validation set, the AI model achieved an Accuracy of 0.980, Precision of 0.941, Specificity of 0.979, Sensitivity of 0.958, F1-Score of 0.951 and AUC of 0.994. In the independent test set, the AI model achieved an Accuracy of 0.967, Precision of 0.980, Specificity of 0.970, Sensitivity of 0.960, F1-Score of 0.975 and AUC of 0.993.

CONCLUSIONS

Our study presents an advanced AI model for strabismus screening which integrates electronic archives for comprehensive patient histories. Additionally, it includes a patient-physician interaction module for streamlined communication. This innovative platform offers a complete solution for strabismus care, from screening to long-term follow-up, advancing ophthalmology through AI technology for improved patient outcomes and eye care quality.

The Role of Telemedicine in Strabismus Assessment: A Narrative Review and Meta-Analysis

Purpose: Strabismus is a common ocular condition requiring precise quantification of gaze deviation and qualification of strabismus category. Telemedicine refers to the use of technology to remotely diagnose and treat medical conditions. This narrative review aimed to assess the efficacy of a variety of telemedicine modalities for the assessment of strabismus. A secondary objective was to quantify overall accuracy, sensitivity, and specificity of automated methods using meta-analysis of available data. Methods: A literature search was conducted using the Ovid MEDLINE, Embase, and Cochrane Library data libraries. Keywords, including "strabismus," "phoria," "telemed*," and "telehealth," were used to locate relevant studies, with Medical Subject Headings terms, free text, and synonyms. No year restrictions were applied. Studies not in English were excluded. Risk of bias was assessed using the QUADAS-2 tool. Results: Thirty-four studies were included. All outcomes relating to accuracy and reliability of telemedicine versus a reference standard were extracted, as well as qualitative observations. High sensitivity, specificity, accuracy, and agreement were consistently shown across studies. Meta-analysis of two subsets featuring automated methods, for which relevant data were available, revealed a pooled accuracy of 0.877 (0.806-0.949), sensitivity of 0.856 (0.805-0.907), and specificity of 0.900 (0.845-0.954). Subcategories "remote standard assessment," "digital image analysis," "wearable devices," "mobile health (mHealth)," and "artificial intelligence" were independently examined. Conclusions: The majority of systems achieved parity with standard physician assessment, with the added benefit of eliminating subjectivity. Meta-analysis results suggest potential introduction of remote automated assessment where conventional assessment is unavailable, although accuracy of current technologies remains limited compared to in-person examination. Telemedicine modalities described offer convenience for patients, shorter examination times, and the potential to go beyond in-person assessments. The evidence gathered in this review supports the beginning of telemedicine integration into the world of strabismus diagnosis.

Reliability of a computerized system for strabismus screening

AIM

To evaluate the reliability of Photo-Hirschberg screening for global strabismus performed by non-specialized personnel.

METHODS

Participants at three sites were enrolled. One person at each site was trained in visual acuity measurement and use of the computerized system. Visual acuity was measured, and strabismus testing was performed using two flash photographs. All data from the three primary observers were sent to an experienced assistant researcher, who was blinded to the primary results, for re-evaluation. The primary and re-evaluation results of the Photo-Hirschberg screenings using weighted kappa for agreement were compared.

RESULTS

The study included 181 participants (88 males and 93 females) and the results for primary and re-evaluation screenings were corresponded. Ten participants with contrasting results presented with unclear corneal light reflex. Sensitivity and specificity were 100% [95% confidence interval (CI): 29.0%-100%] and 99.4% (95%CI: 96.6%-100%), respectively, based on the Agresti test of the primary evaluation, considering the re-evaluated classification as true.

CONCLUSION

The computerized system can be used for primary strabismus screening by non-specialized personnel, with 98.8% agreement with specialists. However, it cannot be used as a substitute for professional examination.

Advances in artificial intelligence models and algorithms in the field of optometry

The rapid development of computer science over the past few decades has led to unprecedented progress in the field of artificial intelligence (AI). Its wide application in ophthalmology, especially image processing and data analysis, is particularly extensive and its performance excellent. In recent years, AI has been increasingly applied in optometry with remarkable results. This review is a summary of the application progress of different AI models and algorithms used in optometry (for problems such as myopia, strabismus, amblyopia, keratoconus, and intraocular lens) and includes a discussion of the limitations and challenges associated with its application in this field.