Smartphone Eye Examination: Artificial Intelligence and Telemedicine

Integration of smartphone technology and artificial intelligence for advanced ophthalmic care: A systematic review

Background

The convergence of smartphone technology and artificial intelligence (AI) has revolutionized the landscape of ophthalmic care, offering unprecedented opportunities for diagnosis, monitoring, and management of ocular conditions. Nevertheless, there is a lack of systematic studies on discussing the integration of smartphone and AI in this field.

Main text

This review includes 52 studies, and explores the integration of smartphones and AI in ophthalmology, delineating its collective impact on screening methodologies, disease detection, telemedicine initiatives, and patient management. The collective findings from the curated studies indicate promising performance of the smartphone-based AI screening for various ocular diseases which encompass major retinal diseases, glaucoma, cataract, visual impairment in children and ocular surface diseases. Moreover, the utilization of smartphone-based imaging modalities, coupled with AI algorithms, is able to provide timely, efficient and cost-effective screening for ocular pathologies. This modality can also facilitate patient self-monitoring, remote patient monitoring and enhancing accessibility to eye care services, particularly in underserved regions. Challenges involving data privacy, algorithm validation, regulatory frameworks and issues of trust are still need to be addressed. Furthermore, evaluation on real-world implementation is imperative as well, and real-world prospective studies are currently lacking.

Conclusions

Smartphone ocular imaging merged with AI enables earlier, precise diagnoses, personalized treatments, and enhanced service accessibility in eye care. Collaboration is crucial to navigate ethical and data security challenges while responsibly leveraging these innovations, promising a potential revolution in care access and global eye health equity.

A Beginner's Guide to Artificial Intelligence for Ophthalmologists

The integration of artificial intelligence (AI) in ophthalmology has promoted the development of the discipline, offering opportunities for enhancing diagnostic accuracy, patient care, and treatment outcomes. This paper aims to provide a foundational understanding of AI applications in ophthalmology, with a focus on interpreting studies related to AI-driven diagnostics. The core of our discussion is to explore various AI methods, including deep learning (DL) frameworks for detecting and quantifying ophthalmic features in imaging data, as well as using transfer learning for effective model training in limited datasets. The paper highlights the importance of high-quality, diverse datasets for training AI models and the need for transparent reporting of methodologies to ensure reproducibility and reliability in AI studies. Furthermore, we address the clinical implications of AI diagnostics, emphasizing the balance between minimizing false negatives to avoid missed diagnoses and reducing false positives to prevent unnecessary interventions. The paper also discusses the ethical considerations and potential biases in AI models, underscoring the importance of continuous monitoring and improvement of AI systems in clinical settings. In conclusion, this paper serves as a primer for ophthalmologists seeking to understand the basics of AI in their field, guiding them through the critical aspects of interpreting AI studies and the practical considerations for integrating AI into clinical practice.

Artificial Intelligence in Telemedicine: A Global Perspective Visualization Analysis

Background: The use of artificial intelligence (AI) in telemedicine has been a popular topic in academic research in recent years, resulting in a surge of literature publications. This study provides a scientific overview of AI in telemedicine through bibliometric and visualization analysis. Methods: The Web of Science Core Collection was used as the data source, and the search was conducted on June 1, 2023. A total of 2,860 articles and review studies published in English between 2010 and 2023 were included. This study analyzed general information on the field, trends in publication output, countries/regions, authors, journals, influential articles, keyword usage, and knowledge flows between disciplines. The Bibliometrix R package, VOSviewer, and CiteSpace were used for the analysis. Results: The rate of articles published on AI in telemedicine is increasing by ∼42.1% annually. The United States and China are the top two countries in terms of the number of articles published, accounting for 37.1% of the overall publication volume. In addition to AI and telemedicine, machine learning, digital health, and deep learning are the top three keywords in terms of frequency of occurrence. The keyword time trend graph shows that ChatGPT became one of the important keywords in 2023. The analysis of burst detection suggests that mobile health, based on mobile phones, may be a promising area for future research. Conclusions: This study systematically reviewed the development of AI in telemedicine and identified current research hotspots and future research directions. The results will provide impetus for the innovative development of this field.

Recent developments of telemedicine in glaucoma

PURPOSE OF REVIEW

Telemedicine has an increasingly significant role in the fields of ophthalmology and glaucoma. This review covers recent advancements in the development and optimization of teleglaucoma techniques and applications.

RECENT FINDINGS

Glaucoma monitoring and diagnosis via remote tonometry, perimetry, and fundus imaging have become a possibility based on recent developments. Many applications work in combination with smart devices, virtual reality, and artificial intelligence and have been tested in patient populations against conventional "reference-standard" measurement tools, demonstrating promising results. Of note, there is still much progress to be made in teleglaucoma and telemedicine at large, such as accessibility to internet, broadband, and smart devices, application affordability, and reimbursement for remote services. However, continued development and optimization of these applications suggest that the implementation of remote monitoring will be a mainstay for glaucoma patient care.

SUMMARY

Especially since the beginning of the COVID-19 pandemic, remote patient care has taken on an important role in medicine and ophthalmology. Remote versions of tonometry, perimetry, and fundus imaging may allow for a more patient-centered and accessible future for glaucoma care.

Portable color retinography findings in COVID-19 patients admitted to the ward

Retinal lesions, including cotton-wool exudates, microbleeds, vascular occlusions and vasculitis, occur in a minority of Coronavirus Disease-19 (COVID-19) patients. Retinal assessments using retinography can help document these lesions. The objective of this work was to identify retinal changes in patients admitted to the ward with a positive Real Time Quantitative Polymerase Chain Reaction (RT-qPCR) exam for COVID-19. A cross-sectional, observational study was carried out of patients with mild and moderate symptoms admitted to the Hospital de Base in São José do Rio Preto. The Eyer® portable retinal camera (Phelcom® Technologies) was used to evaluate 30 male and 21 female patients. The ages ranged from 21 to 83 years (mean: 47 years). Systemic arterial hypertension was identified in 21 (41.2 %) and diabetes mellitus in 12 (23.5 %) patients. Six (11.7 %) reported worsening visual acuity, however, none of these patients had ocular findings to justify this complaint. Ten patients (19.6 %) had intraretinal hemorrhages; one (1.9 %) had cotton-wool exudates and seven (13.7 %) had dilations of veins. Thirteen patients (25.4 %) had vascular tortuosity and six (11.7 %) had pathological arteriovenous crossings. Portable retinography is useful to evaluate patients admitted to isolation wards due to COVID-19. It is important to remember that some of the patients investigated had comorbidities like diabetic maculopathy and systemic arterial hypertension. Hence, some care should be taken in attributing these observations uniquely to COVID-19 infection.