ChatGPT enters the room: what it means for patient counseling, physician education, academics, and disease management.

PURPOSE OF REVIEW: This review seeks to provide a summary of the most recent research findings regarding the utilization of ChatGPT, an artificial intelligence (AI)-powered chatbot, in the field of ophthalmology in addition to exploring the limitations and ethical considerations associated with its application. RECENT FINDINGS: ChatGPT has gained widespread recognition and demonstrated potential in enhancing patient and physician education, boosting research productivity, and streamlining administrative tasks. In various studies examining its utility in ophthalmology, ChatGPT has exhibited fair to good accuracy, with its most recent iteration showcasing superior performance in providing ophthalmic recommendations across various ophthalmic disorders such as corneal diseases, orbital disorders, vitreoretinal diseases, uveitis, neuro-ophthalmology, and glaucoma. This proves beneficial for patients in accessing information and aids physicians in triaging as well as formulating differential diagnoses. Despite such benefits, ChatGPT has limitations that require acknowledgment including the potential risk of offering inaccurate or harmful information, dependence on outdated data, the necessity for a high level of education for data comprehension, and concerns regarding patient privacy and ethical considerations within the research domain. SUMMARY: ChatGPT is a promising new tool that could contribute to ophthalmic healthcare education and research, potentially reducing work burdens. However, its current limitations necessitate a complementary role with human expert oversight.

Artificial intelligence enabled smart digital eye wearables.

PURPOSE OF REVIEW: Smart eyewear is a head-worn wearable device that is evolving as the next phase of ubiquitous wearables. Although their applications in healthcare are being explored, they have the potential to revolutionize teleophthalmology care. This review highlights their applications in ophthalmology care and discusses future scope. RECENT FINDINGS: Smart eyewear equips advanced sensors, optical displays, and processing capabilities in a wearable form factor. Rapid technological developments and the integration of artificial intelligence are expanding their reach from consumer space to healthcare applications. This review systematically presents their applications in treating and managing eye-related conditions. This includes remote assessments, real-time monitoring, telehealth consultations, and the facilitation of personalized interventions. They also serve as low-vision assistive devices to help visually impaired, and can aid physicians with operational and surgical tasks. SUMMARY: Wearables such as smart eyewear collects rich, continuous, objective, individual-specific data, which is difficult to obtain in a clinical setting. By leveraging sophisticated data processing and artificial intelligence based algorithms, these data can identify at-risk patients, recognize behavioral patterns, and make timely interventions. They promise cost-effective and personalized treatment for vision impairments in an effort to mitigate the global burden of eye-related conditions and aging.

The impact of artificial intelligence on retinal disease management: Vision Academy retinal expert consensus.

PURPOSE OF REVIEW: The aim of this review is to define the "state-of-the-art" in artificial intelligence (AI)-enabled devices that support the management of retinal conditions and to provide Vision Academy recommendations on the topic. RECENT FINDINGS: Most of the AI models described in the literature have not been approved for disease management purposes by regulatory authorities. These new technologies are promising as they may be able to provide personalized treatments as well as a personalized risk score for various retinal diseases. However, several issues still need to be addressed, such as the lack of a common regulatory pathway and a lack of clarity regarding the applicability of AI-enabled medical devices in different populations. SUMMARY: It is likely that current clinical practice will need to change following the application of AI-enabled medical devices. These devices are likely to have an impact on the management of retinal disease. However, a consensus needs to be reached to ensure they are safe and effective for the overall population.

Current status and practical considerations of artificial intelligence use in screening and diagnosing retinal diseases: Vision Academy retinal expert consensus.

PURPOSE OF REVIEW: The application of artificial intelligence (AI) technologies in screening and diagnosing retinal diseases may play an important role in telemedicine and has potential to shape modern healthcare ecosystems, including within ophthalmology. RECENT FINDINGS: In this article, we examine the latest publications relevant to AI in retinal disease and discuss the currently available algorithms. We summarize four key requirements underlining the successful application of AI algorithms in real-world practice: processing massive data; practicability of an AI model in ophthalmology; policy compliance and the regulatory environment; and balancing profit and cost when developing and maintaining AI models. SUMMARY: The Vision Academy recognizes the advantages and disadvantages of AI-based technologies and gives insightful recommendations for future directions.

Deep Learning System Outperforms Clinicians in Identifying Optic Disc Abnormalities.

BACKGROUND: The examination of the optic nerve head (optic disc) is mandatory in patients with headache, hypertension, or any neurological symptoms, yet it is rarely or poorly performed in general clinics. We recently developed a brain and optic nerve study with artificial intelligence-deep learning system (BONSAI-DLS) capable of accurately detecting optic disc abnormalities including papilledema (swelling due to elevated intracranial pressure) on digital fundus photographs with a comparable classification performance to expert neuro-ophthalmologists, but its performance compared to first-line clinicians remains unknown. METHODS: In this international, cross-sectional multicenter study, the DLS, trained on 14,341 fundus photographs, was tested on a retrospectively collected convenience sample of 800 photographs (400 normal optic discs, 201 papilledema and 199 other abnormalities) from 454 patients with a robust ground truth diagnosis provided by the referring expert neuro-ophthalmologists. The areas under the receiver-operating-characteristic curves were calculated for the BONSAI-DLS. Error rates, accuracy, sensitivity, and specificity of the algorithm were compared with those of 30 clinicians with or without ophthalmic training (6 general ophthalmologists, 6 optometrists, 6 neurologists, 6 internists, 6 emergency department [ED] physicians) who graded the same testing set of images. RESULTS: With an error rate of 15.3%, the DLS outperformed all clinicians (average error rates 24.4%, 24.8%, 38.2%, 44.8%, 47.9% for general ophthalmologists, optometrists, neurologists, internists and ED physicians, respectively) in the overall classification of optic disc appearance. The DLS displayed significantly higher accuracies than 100%, 86.7% and 93.3% of clinicians (n = 30) for the classification of papilledema, normal, and other disc abnormalities, respectively. CONCLUSIONS: The performance of the BONSAI-DLS to classify optic discs on fundus photographs was superior to that of clinicians with or without ophthalmic training. A trained DLS may offer valuable diagnostic aid to clinicians from various clinical settings for the screening of optic disc abnormalities harboring potentially sight- or life-threatening neurological conditions.

Digital Gonioscopy Based on Three-dimensional Anterior-Segment OCT: An International Multicenter Study.

PURPOSE: To develop and evaluate the performance of a 3-dimensional (3D) deep-learning-based automated digital gonioscopy system (DGS) in detecting 2 major characteristics in eyes with suspected primary angle-closure glaucoma (PACG): (1) narrow iridocorneal angles (static gonioscopy, Task I) and (2) peripheral anterior synechiae (PAS) (dynamic gonioscopy, Task II) on OCT scans. DESIGN: International, cross-sectional, multicenter study. PARTICIPANTS: A total of 1.112 million images of 8694 volume scans (2294 patients) from 3 centers were included in this study (Task I, training/internal validation/external testing: 4515, 1101, and 2222 volume scans, respectively; Task II, training/internal validation/external testing: 378, 376, and 102 volume scans, respectively). METHODS: For Task I, a narrow angle was defined as an eye in which the posterior pigmented trabecular meshwork was not visible in more than 180° without indentation in the primary position captured in the dark room from the scans. For Task II, PAS was defined as the adhesion of the iris to the trabecular meshwork. The diagnostic performance of the 3D DGS was evaluated in both tasks with gonioscopic records as reference. MAIN OUTCOME MEASURES: The area under the curve (AUC), sensitivity, and specificity of the 3D DGS were calculated. RESULTS: In Task I, 29.4% of patients had a narrow angle. The AUC, sensitivity, and specificity of 3D DGS on the external testing datasets were 0.943 (0.933-0.953), 0.867 (0.838-0.895), and 0.878 (0.859-0.896), respectively. For Task II, 13.8% of patients had PAS. The AUC, sensitivity, and specificity of 3D DGS were 0.902 (0.818-0.985), 0.900 (0.714-1.000), and 0.890 (0.841-0.938), respectively, on the external testing set at quadrant level following normal clinical practice; and 0.885 (0.836-0.933), 0.912 (0.816-1.000), and 0.700 (0.660-0.741), respectively, on the external testing set at clock-hour level. CONCLUSIONS: The 3D DGS is effective in detecting eyes with suspected PACG. It has the potential to be used widely in the primary eye care community for screening of subjects at high risk of developing PACG.

Novel technical and privacy-preserving technology for artificial intelligence in ophthalmology.

PURPOSE OF REVIEW: The application of artificial intelligence (AI) in medicine and ophthalmology has experienced exponential breakthroughs in recent years in diagnosis, prognosis, and aiding clinical decision-making. The use of digital data has also heralded the need for privacy-preserving technology to protect patient confidentiality and to guard against threats such as adversarial attacks. Hence, this review aims to outline novel AI-based systems for ophthalmology use, privacy-preserving measures, potential challenges, and future directions of each. RECENT FINDINGS: Several key AI algorithms used to improve disease detection and outcomes include: Data-driven, imagedriven, natural language processing (NLP)-driven, genomics-driven, and multimodality algorithms. However, deep learning systems are susceptible to adversarial attacks, and use of data for training models is associated with privacy concerns. Several data protection methods address these concerns in the form of blockchain technology, federated learning, and generative adversarial networks. SUMMARY: AI-applications have vast potential to meet many eyecare needs, consequently reducing burden on scarce healthcare resources. A pertinent challenge would be to maintain data privacy and confidentiality while supporting AI endeavors, where data protection methods would need to rapidly evolve with AI technology needs. Ultimately, for AI to succeed in medicine and ophthalmology, a balance would need to be found between innovation and privacy.

The promise of artificial intelligence: a review of the opportunities and challenges of artificial intelligence in healthcare.

INTRODUCTION: Artificial intelligence (AI) and machine learning (ML) are rapidly evolving fields in various sectors, including healthcare. This article reviews AI's present applications in healthcare, including its benefits, limitations and future scope. SOURCES OF DATA: A review of the English literature was conducted with search terms 'AI' or 'ML' or 'deep learning' and 'healthcare' or 'medicine' using PubMED and Google Scholar from 2000-2021. AREAS OF AGREEMENT: AI could transform physician workflow and patient care through its applications, from assisting physicians and replacing administrative tasks to augmenting medical knowledge. AREAS OF CONTROVERSY: From challenges training ML systems to unclear accountability, AI's implementation is difficult and incremental at best. Physicians also lack understanding of what AI implementation could represent. GROWING POINTS: AI can ultimately prove beneficial in healthcare, but requires meticulous governance similar to the governance of physician conduct. AREAS TIMELY FOR DEVELOPING RESEARCH: Regulatory guidelines are needed on how to safely implement and assess AI technology, alongside further research into the specific capabilities and limitations of its medical use.

Optic Disc Classification by Deep Learning versus Expert Neuro-Ophthalmologists.

OBJECTIVE: To compare the diagnostic performance of an artificial intelligence deep learning system with that of expert neuro-ophthalmologists in classifying optic disc appearance. METHODS: The deep learning system was previously trained and validated on 14,341 ocular fundus photographs from 19 international centers. The performance of the system was evaluated on 800 new fundus photographs (400 normal optic discs, 201 papilledema [disc edema from elevated intracranial pressure], 199 other optic disc abnormalities) and compared with that of 2 expert neuro-ophthalmologists who independently reviewed the same randomly presented images without clinical information. Area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity were calculated. RESULTS: The system correctly classified 678 of 800 (84.7%) photographs, compared with 675 of 800 (84.4%) for Expert 1 and 641 of 800 (80.1%) for Expert 2. The system yielded areas under the receiver operating characteristic curve of 0.97 (95% confidence interval [CI] = 0.96-0.98), 0.96 (95% CI = 0.94-0.97), and 0.89 (95% CI = 0.87-0.92) for the detection of normal discs, papilledema, and other disc abnormalities, respectively. The accuracy, sensitivity, and specificity of the system's classification of optic discs were similar to or better than the 2 experts. Intergrader agreement at the eye level was 0.71 (95% CI = 0.67-0.76) between Expert 1 and Expert 2, 0.72 (95% CI = 0.68-0.76) between the system and Expert 1, and 0.65 (95% CI = 0.61-0.70) between the system and Expert 2. INTERPRETATION: The performance of this deep learning system at classifying optic disc abnormalities was at least as good as 2 expert neuro-ophthalmologists. Future prospective studies are needed to validate this system as a diagnostic aid in relevant clinical settings. ANN NEUROL 2020;88:785-795.

Detection of Optic Disc Abnormalities in Color Fundus Photographs Using Deep Learning.

BACKGROUND: To date, deep learning-based detection of optic disc abnormalities in color fundus photographs has mostly been limited to the field of glaucoma. However, many life-threatening systemic and neurological conditions can manifest as optic disc abnormalities. In this study, we aimed to extend the application of deep learning (DL) in optic disc analyses to detect a spectrum of nonglaucomatous optic neuropathies. METHODS: Using transfer learning, we trained a ResNet-152 deep convolutional neural network (DCNN) to distinguish between normal and abnormal optic discs in color fundus photographs (CFPs). Our training data set included 944 deidentified CFPs (abnormal 364; normal 580). Our testing data set included 151 deidentified CFPs (abnormal 71; normal 80). Both the training and testing data sets contained a wide range of optic disc abnormalities, including but not limited to ischemic optic neuropathy, atrophy, compressive optic neuropathy, hereditary optic neuropathy, hypoplasia, papilledema, and toxic optic neuropathy. The standard measures of performance (sensitivity, specificity, and area under the curve of the receiver operating characteristic curve (AUC-ROC)) were used for evaluation. RESULTS: During the 10-fold cross-validation test, our DCNN for distinguishing between normal and abnormal optic discs achieved the following mean performance: AUC-ROC 0.99 (95 CI: 0.98-0.99), sensitivity 94% (95 CI: 91%-97%), and specificity 96% (95 CI: 93%-99%). When evaluated against the external testing data set, our model achieved the following mean performance: AUC-ROC 0.87, sensitivity 90%, and specificity 69%. CONCLUSION: In summary, we have developed a deep learning algorithm that is capable of detecting a spectrum of optic disc abnormalities in color fundus photographs, with a focus on neuro-ophthalmological etiologies. As the next step, we plan to validate our algorithm prospectively as a focused screening tool in the emergency department, which if successful could be beneficial because current practice pattern and training predict a shortage of neuro-ophthalmologists and ophthalmologists in general in the near future.

The potential application of artificial intelligence for diagnosis and management of glaucoma in adults.

BACKGROUND: Glaucoma is the most frequent cause of irreversible blindness worldwide. There is no cure, but early detection and treatment can slow the progression and prevent loss of vision. It has been suggested that artificial intelligence (AI) has potential application for detection and management of glaucoma. SOURCES OF DATA: This literature review is based on articles published in peer-reviewed journals. AREAS OF AGREEMENT: There have been significant advances in both AI and imaging techniques that are able to identify the early signs of glaucomatous damage. Machine and deep learning algorithms show capabilities equivalent to human experts, if not superior. AREAS OF CONTROVERSY: Concerns that the increased reliance on AI may lead to deskilling of clinicians. GROWING POINTS: AI has potential to be used in virtual review clinics, telemedicine and as a training tool for junior doctors. Unsupervised AI techniques offer the potential of uncovering currently unrecognized patterns of disease. If this promise is fulfilled, AI may then be of use in challenging cases or where a second opinion is desirable. AREAS TIMELY FOR DEVELOPING RESEARCH: There is a need to determine the external validity of deep learning algorithms and to better understand how the 'black box' paradigm reaches results.

Interpretation of artificial intelligence studies for the ophthalmologist.

PURPOSE OF REVIEW: The use of artificial intelligence (AI) in ophthalmology has increased dramatically. However, interpretation of these studies can be a daunting prospect for the ophthalmologist without a background in computer or data science. This review aims to share some practical considerations for interpretation of AI studies in ophthalmology. RECENT FINDINGS: It can be easy to get lost in the technical details of studies involving AI. Nevertheless, it is important for clinicians to remember that the fundamental questions in interpreting these studies remain unchanged - What does this study show, and how does this affect my patients? Being guided by familiar principles like study purpose, impact, validity, and generalizability, these studies become more accessible to the ophthalmologist. Although it may not be necessary for nondomain experts to understand the exact AI technical details, we explain some broad concepts in relation to AI technical architecture and dataset management. SUMMARY: The expansion of AI into healthcare and ophthalmology is here to stay. AI systems have made the transition from bench to bedside, and are already being applied to patient care. In this context, 'AI education' is crucial for ophthalmologists to be confident in interpretation and translation of new developments in this field to their own clinical practice.

Artificial intelligence for diabetic retinopathy screening, prediction and management.

PURPOSE OF REVIEW: Diabetic retinopathy is the most common specific complication of diabetes mellitus. Traditional care for patients with diabetes and diabetic retinopathy is fragmented, uncoordinated and delivered in a piecemeal nature, often in the most expensive and high-resource tertiary settings. Transformative new models incorporating digital technology are needed to address these gaps in clinical care. RECENT FINDINGS: Artificial intelligence and telehealth may improve access, financial sustainability and coverage of diabetic retinopathy screening programs. They enable risk stratifying patients based on individual risk of vision-threatening diabetic retinopathy including diabetic macular edema (DME), and predicting which patients with DME best respond to antivascular endothelial growth factor therapy. SUMMARY: Progress in artificial intelligence and tele-ophthalmology for diabetic retinopathy screening, including artificial intelligence applications in 'real-world settings' and cost-effectiveness studies are summarized. Furthermore, the initial research on the use of artificial intelligence models for diabetic retinopathy risk stratification and management of DME are outlined along with potential future directions. Finally, the need for artificial intelligence adoption within ophthalmology in response to coronavirus disease 2019 is discussed. Digital health solutions such as artificial intelligence and telehealth can facilitate the integration of community, primary and specialist eye care services, optimize the flow of patients within healthcare networks, and improve the efficiency of diabetic retinopathy management.

DIABETIC MACULAR ISCHEMIA: Correlation of Retinal Vasculature Changes by Optical Coherence Tomography Angiography and Functional Deficit.

PURPOSE: To examine the relationship between macular microvasculature parameters and functional changes in persons with diabetic retinopathy (DR). METHODS: Cross-sectional study of 76 eyes with varying levels of DR. Optical coherence tomography angiography (OCTA) quantified superficial and deep perifoveal vessel densities and foveal avascular zone areas. Retinal sensitivity was measured using microperimetry. Optical coherence tomography angiography parameters and retinal sensitivity were correlated. RESULTS: Deep perifoveal vessel density decreased with increasing severity of DR (adjusted mean 51.93 vs. 49.89 vs. 47.96, P-trend = 0.005). Superficial and deep foveal avascular zone area increased with increasing DR severity (adjusted mean: 235.0 µm vs. 303.4 µm vs. 400.9 µm, P-trend = 0.003 [superficial]; 333.1 µm vs. 513.3 µm vs. 530.2 µm, P-trend = 0.001 [deep]). Retinal sensitivity decreased with increasing DR severity (adjusted mean: 25.12 dB vs. 22.34 dB vs. 20.67 dB, P-trend = 0.003). Retinal sensitivity correlated positively with deep perifoveal vessel density (Pearson's ρ = 0.276, P = 0.020) and inversely with superficial foveal avascular zone area (Pearson's ρ = -0.333, P = 0.010). CONCLUSION: Alterations in retinal microvasculature can be observed with OCTA with increasing severity of DR. These changes are correlated with reduced retinal sensitivity. Optical coherence tomography angiography is useful to detect and quantify the microvasculature properties of eyes with diabetic macular ischemia.

Deep Learning and Transfer Learning for Optic Disc Laterality Detection: Implications for Machine Learning in Neuro-Ophthalmology.

BACKGROUND: Deep learning (DL) has demonstrated human expert levels of performance for medical image classification in a wide array of medical fields, including ophthalmology. In this article, we present the results of our DL system designed to determine optic disc laterality, right eye vs left eye, in the presence of both normal and abnormal optic discs. METHODS: Using transfer learning, we modified the ResNet-152 deep convolutional neural network (DCNN), pretrained on ImageNet, to determine the optic disc laterality. After a 5-fold cross-validation, we generated receiver operating characteristic curves and corresponding area under the curve (AUC) values to evaluate performance. The data set consisted of 576 color fundus photographs (51% right and 49% left). Both 30° photographs centered on the optic disc (63%) and photographs with varying degree of optic disc centration and/or wider field of view (37%) were included. Both normal (27%) and abnormal (73%) optic discs were included. Various neuro-ophthalmological diseases were represented, such as, but not limited to, atrophy, anterior ischemic optic neuropathy, hypoplasia, and papilledema. RESULTS: Using 5-fold cross-validation (70% training; 10% validation; 20% testing), our DCNN for classifying right vs left optic disc achieved an average AUC of 0.999 (±0.002) with optimal threshold values, yielding an average accuracy of 98.78% (±1.52%), sensitivity of 98.60% (±1.72%), and specificity of 98.97% (±1.38%). When tested against a separate data set for external validation, our 5-fold cross-validation model achieved the following average performance: AUC 0.996 (±0.005), accuracy 97.2% (±2.0%), sensitivity 96.4% (±4.3%), and specificity 98.0% (±2.2%). CONCLUSIONS: Small data sets can be used to develop high-performing DL systems for semantic labeling of neuro-ophthalmology images, specifically in distinguishing between right and left optic discs, even in the presence of neuro-ophthalmological pathologies. Although this may seem like an elementary task, this study demonstrates the power of transfer learning and provides an example of a DCNN that can help curate large medical image databases for machine-learning purposes and facilitate ophthalmologist workflow by automatically labeling images according to laterality.

Automated and Computer-Assisted Detection, Classification, and Diagnosis of Diabetic Retinopathy.

Background: The introduction of artificial intelligence (AI) in medicine has raised significant ethical, economic, and scientific controversies. Introduction: Because an explicit goal of AI is to perform processes previously reserved for human clinicians and other health care personnel, there is justified concern about the impact on patient safety, efficacy, equity, and liability. Discussion: Systems for computer-assisted and fully automated detection, triage, and diagnosis of diabetic retinopathy (DR) from retinal images show great variation in design, level of autonomy, and intended use. Moreover, the degree to which these systems have been evaluated and validated is heterogeneous. We use the term DR AI system as a general term for any system that interprets retinal images with at least some degree of autonomy from a human grader. We put forth these standardized descriptors to form a means to categorize systems for computer-assisted and fully automated detection, triage, and diagnosis of DR. The components of the categorization system include level of device autonomy, intended use, level of evidence for diagnostic accuracy, and system design. Conclusion: There is currently minimal empirical basis to assert that certain combinations of autonomy, accuracy, or intended use are better or more appropriate than any other. Therefore, at the current stage of development of this document, we have been descriptive rather than prescriptive, and we treat the different categorizations as independent and organized along multiple axes.

CHOROIDAL VASCULAR HYPERPERMEABILITY AS A PREDICTOR OF TREATMENT RESPONSE FOR POLYPOIDAL CHOROIDAL VASCULOPATHY.

PURPOSE: To investigate the influence of choroidal vascular hyperpermeability (CVH) and choroidal thickness on treatment outcomes in eyes with polypoidal choroidal vasculopathy (PCV) undergoing anti-vascular endothelial growth factor monotherapy or combination therapy of photodynamic therapy and anti-vascular endothelial growth factor injections. METHODS: The authors performed a prospective, observational cohort study involving 72 eyes of 72 patients with polypoidal choroidal vasculopathy (mean age 68.6 years, 51% men) treated with either monotherapy (n = 41) or combination therapy (n = 31). Each eye was imaged with color fundus photography, fluorescent angiography, indocyanine green angiography, and spectral domain optical coherence tomography. Indocyanine green angiography images were used to evaluate CVH, and spectral domain optical coherence tomography was used to measure central choroidal thickness. Changes in visual acuity over 12 months, and number of anti-vascular endothelial growth factor injections were investigated. RESULTS: Choroidal vascular hyperpermeability was present in 31 eyes (43.1%). Visual acuity change over 12 months was numerically better in the CVH group compared with the CVH (-) group (-0.099 and -0.366 logarithm of the minimal angle of resolution unit in the CVH (-) and CVH (+) groups, respectively, multivariate P = 0.063) and significantly better in a matched pair analysis (P = 0.033). Furthermore, in the combination therapy group, the number of injection was significantly lower in the CVH (+) group compared with the CVH (-) group (4.68 vs. 2.58 injections/year in the CVH (-) and CVH (+) groups; P = 0.0044). There was no significant relationship between treatment response and choroidal thickening. CONCLUSION: The presence of CVH is associated with better visual outcome in eyes with polypoidal choroidal vasculopathy and lower injection number in combination therapy. Thus, CVH, but not choroidal thickness, should be further evaluated as a potential biomarker for selecting patients for combination therapy.