Impact and Challenges of Integrating Artificial Intelligence and Telemedicine into Clinical Ophthalmology

A Review of Intraocular Lens Power Calculation Formulas Based on Artificial Intelligence

PURPOSE

The proper selection of an intraocular lens power calculation formula is an essential aspect of cataract surgery. This study evaluated the accuracy of artificial intelligence-based formulas.

DESIGN

Systematic review.

METHODS

This review comprises articles evaluating the exactness of artificial intelligence-based formulas published from 2017 to July 2023. The papers were identified by a literature search of various databases (Pubmed/MEDLINE, Google Scholar, Crossref, Cochrane Library, Web of Science, and SciELO) using the terms "IOL formulas", "FullMonte", "Ladas", "Hill-RBF", "PEARL-DGS", "Kane", "Karmona", "Hoffer QST", and "Nallasamy". In total, 25 peer-reviewed articles in English with the maximum sample and the largest number of compared formulas were examined.

RESULTS

The scores of the mean absolute error and percentage of patients within ±0.5 D and ±1.0 D were used to estimate the exactness of the formulas. In most studies the Kane formula obtained the smallest mean absolute error and the highest percentage of patients within ±0.5 D and ±1.0 D. Second place was typically achieved by the PEARL DGS formula. The limitations of the studies were also discussed.

CONCLUSIONS

Kane seems to be the most accurate artificial intelligence-based formula. PEARL DGS also gives very good results. Hoffer QST, Karmona, and Nallasamy are the newest, and need further evaluation.

An Artificial Intelligence System for Screening and Recommending the Treatment Modalities for Retinopathy of Prematurity

PURPOSE

The purpose of this study was to develop an artificial intelligence (AI) system for the identification of disease status and recommending treatment modalities for retinopathy of prematurity (ROP).

METHODS

This retrospective cohort study included a total of 24,495 RetCam images from 1075 eyes of 651 preterm infants who received RetCam examination at the Shenzhen Eye Hospital in Shenzhen, China, from January 2003 to August 2021. Three tasks included ROP identification, severe ROP identification, and treatment modalities identification (retinal laser photocoagulation or intravitreal injections). The AI system was developed to identify the 3 tasks, especially the treatment modalities of ROP. The performance between the AI system and ophthalmologists was compared using extra 200 RetCam images.

RESULTS

The AI system exhibited favorable performance in the 3 tasks, including ROP identification [area under the receiver operating characteristic curve (AUC), 0.9531], severe ROP identification (AUC, 0.9132), and treatment modalities identification with laser photocoagulation or intravitreal injections (AUC, 0.9360). The AI system achieved an accuracy of 0.8627, a sensitivity of 0.7059, and a specificity of 0.9412 for identifying the treatment modalities of ROP. External validation results confirmed the good performance of the AI system with an accuracy of 92.0% in all 3 tasks, which was better than 4 experienced ophthalmologists who scored 56%, 65%, 71%, and 76%, respectively.

CONCLUSIONS

The described AI system achieved promising outcomes in the automated identification of ROP severity and treatment modalities. Using such algorithmic approaches as accessory tools in the clinic may improve ROP screening in the future.

Trends in Ophthalmological Patents, 2005-2020

Purpose: Technological development drives the optimization of therapeutics in ophthalmology, but quantifiable and systematic review of such innovation is lacking. To fill this gap, we characterize trends in ophthalmology-related patents in the United States from 2005 to 2020. Methods: Publicly available patent data from the US Patent and Trademark Office was analyzed with the R programming language. Ophthalmology-related patents were identified with a keyword search of their titles and claims text. Temporal trends were assessed with the Mann-Kendall trend test (α = 0.05, two-sided). Results: Of 4.5 million collected patents, some 21,000 (0.5%) were ophthalmology related. The number of annually granted ophthalmology patents increased over time (Mann-Kendall test: z = 4.91; P < 0.001), from 619 patents released in 2005 to 2,019 patents in 2020. Patent counts also increased over time for all ophthalmic subspecialties except oculoplastics, with steepest rises in retina (z = 4.91; P < 0.001) and cornea (z = 4.64; P < 0.001). The most cited patents were in biocompatible intraocular implants and implantable controlled-release drug delivery systems, which underscores particular advancement in therapeutic efficacy and safety in devices used in the treatment and management of common yet debilitating eye conditions. Conclusion: This exploratory analysis reveals hotspots for ophthalmology-related innovation in the United States that may predict current and future growth trends in device development and pharmacologic advancement in ophthalmology, paving the way for more diverse and effective treatment options for preserving vision.

Surfing the COVID-19 Tsunami with Teleophthalmology: the Advent of New Models of Eye Care

Purpose of Review

In this article, we reviewed the impact resulting from the COVID-19 pandemic on the traditional model of care in ophthalmology.

Recent Findings

Though virtual eye care has been present for more than 20 years, the COVID-19 pandemic has established a precedent to seriously consider its role in the evolving paradigm of vision and eye care. New hybrid models of care have enhanced or replaced traditional synchronous and asynchronous visits. The increased use of smart phoneography and mobile applications enhanced the remote examination of patients. Use of e-learning became a mainstream tool to continue accessing education and training.

Summary

Teleophthalmology has demonstrated its value for screening, examining, diagnosing, monitoring treatment, and increasing access to education. However, much of the progress made following the COVID-19 pandemic is at risk of being lost as society pushes to reestablish normalcy. Further studies during the new norm are required to prove a more permanent role for virtual eye care.

Applications of Artificial Intelligence and Deep Learning in Glaucoma

Diagnosis and detection of progression of glaucoma remains challenging. Artificial intelligence-based tools have the potential to improve and standardize the assessment of glaucoma but development of these algorithms is difficult given the multimodal and variable nature of the diagnosis. Currently, most algorithms are focused on a single imaging modality, specifically screening and diagnosis based on fundus photos or optical coherence tomography images. Use of anterior segment optical coherence tomography and goniophotographs is limited. The majority of algorithms designed for disease progression prediction are based on visual fields. No studies in our literature search assessed the use of artificial intelligence for treatment response prediction and no studies conducted prospective testing of their algorithms. Additional challenges to the development of artificial intelligence-based tools include scarcity of data and a lack of consensus in diagnostic criteria. Although research in the use of artificial intelligence for glaucoma is promising, additional work is needed to develop clinically usable tools.

The Glaucoma Suspect Problem: Ways Forward

The diagnosis of glaucoma depends upon indentification of characteristic damage to the optic nerve and retinal fiber layer. In many cases, however, clinicians find it difficult to ascertain whether glaucomatous damage is present or absent. These patients are often labeled as "glaucoma suspects," which creates a subpopulation of individuals without clear-cut disease who nonetheless must remain under surveillance. Most will never go on to develop glaucoma, yet the need for ongoing monitoring burdens clinics and health care systems. In this perspective, we illustrate possible directions and novel approaches that can be used to remedy this situation by integrating current technologies into clinical practice. In particular, we suggest that optical coherence tomography be better utilized to methodologically classify these eyes into glaucomatous and healthy categories.

Patients Perceptions of Artificial Intelligence in Diabetic Eye Screening

PURPOSE

Artificial intelligence (AI) technology is poised to revolutionize modern delivery of health care services. We set to evaluate the patient perspective of AI use in diabetic retinal screening.

DESIGN

Survey.

METHODS

Four hundred thirty-eight patients undergoing diabetic retinal screening across New Zealand participated in a survey about their opinion of AI technology in retinal screening. The survey consisted of 13 questions covering topics of awareness, trust, and receptivity toward AI systems.

RESULTS

The mean age was 59 years. The majority of participants identified as New Zealand European (50%), followed by Asian (31%), Pacific Islander (10%), and Maori (5%). Whilst 73% of participants were aware of AI, only 58% have heard of it being implemented in health care. Overall, 78% of respondents were comfortable with AI use in their care, with 53% saying they would trust an AI-assisted screening program as much as a health professional. Despite having a higher awareness of AI, younger participants had lower trust in AI systems. A higher proportion of Maori and Pacific participants indicated a preference toward human-led screening. The main perceived benefits of AI included faster diagnostic speeds and greater accuracy.

CONCLUSIONS

There is low awareness of clinical AI applications among our participants. Despite this, most are receptive toward the implementation of AI in diabetic eye screening. Overall, there was a strong preference toward continual involvement of clinicians in the screening process. There are key recommendations to enhance the receptivity of the public toward incorporation of AI into retinal screening programs.