Tele-glaucoma versus clinical evaluation: The New Jersey Health Foundation Prospective Clinical Study

Implementation of deep learning artificial intelligence in vision-threatening disease screenings for an underserved community during COVID-19

INTRODUCTION

Age-related macular degeneration, diabetic retinopathy, and glaucoma are vision-threatening diseases that are leading causes of vision loss. Many studies have validated deep learning artificial intelligence for image-based diagnosis of vision-threatening diseases. Our study prospectively investigated deep learning artificial intelligence applications in student-run non-mydriatic screenings for an underserved, primarily Hispanic community during COVID-19.

METHODS

Five supervised student-run community screenings were held in West New York, New Jersey. Participants underwent non-mydriatic 45-degree retinal imaging by medical students. Images were uploaded to a cloud-based deep learning artificial intelligence for vision-threatening disease referral. An on-site tele-ophthalmology grader and remote clinical ophthalmologist graded images, with adjudication by a senior ophthalmologist to establish the gold standard diagnosis, which was used to assess the performance of deep learning artificial intelligence.

RESULTS

A total of 385 eyes from 195 screening participants were included (mean age 52.43  ±  14.5 years, 40.0% female). A total of 48 participants were referred for at least one vision-threatening disease. Deep learning artificial intelligence marked 150/385 (38.9%) eyes as ungradable, compared to 10/385 (2.6%) ungradable as per the human gold standard (p < 0.001). Deep learning artificial intelligence had 63.2% sensitivity, 94.5% specificity, 32.0% positive predictive value, and 98.4% negative predictive value in vision-threatening disease referrals. Deep learning artificial intelligence successfully referred all 4 eyes with multiple vision-threatening diseases. Deep learning artificial intelligence graded images (35.6  ±  13.3 s) faster than the tele-ophthalmology grader (129  ±  41.0) and clinical ophthalmologist (68  ±  21.9, p < 0.001).

DISCUSSION

Deep learning artificial intelligence can increase the efficiency and accessibility of vision-threatening disease screenings, particularly in underserved communities. Deep learning artificial intelligence should be adaptable to different environments. Consideration should be given to how deep learning artificial intelligence can best be utilized in a real-world application, whether in computer-aided or autonomous diagnosis.

Advanced glaucoma. Clinical practice guideline

OBJECTIVE

To present an update clinical practice guideline that serve as a guide for the detection, evaluation and treatment of adults patients with advanced glaucoma.

METHODS

After defining the objectives and scope of the guide, the working group was formed and structured clinical questions were formulated following the PICO (Patient, Intervention, Comparison, Outcomes) format. Once all the existing clinical evidence had been independently evaluated with the AMSTAR 2 (Assessment of Multiple systematic Rewiews) and Cochrane "Risk of bias" tools by at least two reviewers, recommendations were formulated following the Scottish Intercollegiate methodology. Guideline Network (SIGN).

RESULTS

Recommendations with their corresponding levels of evidence that may be useful in the diagnosis, monitoring and treatment of adults patients with advanced glaucoma.

CONCLUSIONS

Despite the fact that for many of the questions the level of scientific evidence available is not very high, this clinical practice guideline offers an updated review of the different existing aspects related to the evaluation and management of advanced glaucoma.

Telehealth and Screening Strategies in the Diagnosis and Management of Glaucoma

Telehealth has become a viable option for glaucoma screening and glaucoma monitoring due to advances in technology. The ability to measure intraocular pressure without an anesthetic and to take optic nerve photographs without pharmacologic pupillary dilation using portable equipment have allowed glaucoma screening programs to generate enough data for assessment. At home, patients can perform visual acuity testing, web-based visual field testing, rebound tonometry, and video visits with the physician to monitor for glaucomatous progression. Artificial intelligence will enhance the accuracy of data interpretation and inspire confidence in popularizing telehealth for glaucoma.

Teleophthalmology and its evolving role in a COVID-19 pandemic: A scoping review

ABSTRACT Introduction: Teleophthalmology may assist the healthcare sector in adapting to limitations imposed on clinical practice by a viral pandemic. A scoping review is performed in this study to assess the current applications of teleophthalmology for its suitability to diagnose, monitor or manage ophthalmological conditions with accuracy. Methods: A search of PubMed was conducted for teleophthalmology-related articles published from 1 January 2018 to 4 May 2020. Only articles that focused on the use of teleophthalmology in terms of diagnosis and management, as well as its benefits and detriments, were included. The Mixed Methods Appraisal Tool (MMAT) was used to assess the quality of the included articles. Results: A total of 38 articles were assessed at the full-text level. There were 2 qualitative studies and 1 quantitative randomised controlled trial, while the majority were either quantitative descriptive studies (19, 50.0%) or quantitative non-randomised studies (16, 42.1%). Overall, 8 studies described reducing manpower requirements, 4 described reducing direct patient–doctor contact, 17 described storage of medical imaging and clinical data, and 9 described real-time teleconferencing. The MMAT analysis revealed limitations in appropriate sampling strategy in both quantitative non-randomised studies (9 of 16, 56.3%) and quantitative descriptive studies (9 of 19, 47.4%). Cost-effectiveness of teleophthalmology was not performed in any included study. Conclusion: This current review of the various aspects of teleophthalmology describes how it may potentially assist the healthcare sector to cope with the limitations imposed by a viral pandemic through technology. Further research is required to evaluate the cost-effectiveness of the various strategies. Keywords: Artificial intelligence, health informatics, ophthalmology, teleconsultation, telemedicine

Telemedicine in ophthalmology in view of the emerging COVID-19 outbreak

Purpose

Technological advances in recent years have resulted in the development and implementation of various modalities and techniques enabling medical professionals to remotely diagnose and treat numerous medical conditions in diverse medical fields, including ophthalmology. Patients who require prolonged isolation until recovery, such as those who suffer from COVID-19, present multiple therapeutic dilemmas to their caregivers. Therefore, utilizing remote care in the daily workflow would be a valuable tool for the diagnosis and treatment of acute and chronic ocular conditions in this challenging clinical setting. Our aim is to review the latest technological and methodical advances in teleophthalmology and highlight their implementation in screening and managing various ocular conditions. We present them as well as potential diagnostic and treatment applications in view of the recent SARS-CoV-2 virus outbreak.

Methods

A computerized search from January 2017 up to March 2020 of the online electronic database PubMed was performed, using the following search strings: “telemedicine,” “telehealth,” and “ophthalmology.” More generalized complementary contemporary research data regarding the COVID-19 pandemic was also obtained from the PubMed database.

Results

A total of 312 records, including COVID-19-focused studies, were initially identified. After exclusion of non-relevant, non-English, and duplicate studies, a total of 138 records were found eligible. Ninety records were included in the final qualitative analysis.

Conclusion

Teleophthalmology is an effective screening and management tool for a range of adult and pediatric acute and chronic ocular conditions. It is mostly utilized in screening of retinal conditions such as retinopathy of prematurity, diabetic retinopathy, and age-related macular degeneration; in diagnosing anterior segment condition; and in managing glaucoma. With improvements in image processing, and better integration of the patient’s medical record, teleophthalmology should become a more accepted modality, all the more so in circumstances where social distancing is inflicted upon us.