Use of Machine Learning on Contact Lens Sensor-Derived Parameters for the Diagnosis of Primary Open-angle Glaucoma

The biomedical applications of artificial intelligence: an overview of decades of research

A significant area of computer science called artificial intelligence (AI) is successfully applied to the analysis of intricate biological data and the extraction of substantial associations from datasets for a variety of biomedical uses. AI has attracted significant interest in biomedical research due to its features: (i) better patient care through early diagnosis and detection; (ii) enhanced workflow; (iii) lowering medical errors; (v) lowering medical costs; (vi) reducing morbidity and mortality; (vii) enhancing performance; (viii) enhancing precision; and (ix) time efficiency. Quantitative metrics are crucial for evaluating AI implementations, providing insights, enabling informed decisions, and measuring the impact of AI-driven initiatives, thereby enhancing transparency, accountability, and overall impact. The implementation of AI in biomedical fields faces challenges such as ethical and privacy concerns, lack of awareness, technology unreliability, and professional liability. A brief discussion is given of the AI techniques, which include Virtual screening (VS), DL, ML, Hidden Markov models (HMMs), Neural networks (NNs), Generative models (GMs), Molecular dynamics (MD), and Structure-activity relationship (SAR) models. The study explores the application of AI in biomedical fields, highlighting its enhanced predictive accuracy, treatment efficacy, diagnostic efficiency, faster decision-making, personalised treatment strategies, and precise medical interventions.

Application of machine learning techniques in GlaucomAI system for glaucoma diagnosis and collaborative research support

This paper proposes an architecture of the system that provides support for collaborative research focused on analysis of data acquired using Triggerfish contact lens sensor and devices for continuous monitoring of cardiovascular system properties. The system enables application of machine learning (ML) models for glaucoma diagnosis without direct intraocular pressure measurement and independently of complex imaging techniques used in clinical practice. We describe development of ML models based on sensor data and measurements of corneal biomechanical properties. Application scenarios involve collection, sharing and analysis of multi-sensor data. We give a view of issues concerning interpretability and evaluation of ML model predictions. We also refer to the problems related to personalized medicine and transdisciplinary research. The system can be a base for community-wide initiative including ophthalmologists, data scientists and machine learning experts that has the potential to leverage data acquired by the devices to understand glaucoma risk factors and the processes related to progression of the disease.

Artificial Intelligence and Ophthalmic Clinical Registries

PURPOSE

The recent advances in artificial intelligence (AI) represent a promising solution to increasing clinical demand and ever limited health resources. Whilst powerful, AI models require vast amounts of representative training data to output meaningful predictions in the clinical environment. Clinical registries represent a promising source of large volume real-world data which could be used to train more accurate and widely applicable AI models. This review aims to provide an overview of the current applications of AI to ophthalmic clinical registry data.

DESIGN AND METHODS

A systematic search of EMBASE, Medline, PubMed, Scopus and Web of Science for primary research articles that applied AI to ophthalmic clinical registry data was conducted in July 2024.

RESULTS

Twenty-three primary research articles applying AI to ophthalmic clinic registries (n = 14) were found. Registries were primarily defined by the condition captured and the most common conditions where AI was applied were glaucoma (n = 3) and neovascular age-related macular degeneration (n = 3). Tabular clinical data was the most common form of input into AI algorithms and outputs were primarily classifiers (n = 8, 40%) and risk quantifier models (n = 7, 35%). The AI algorithms applied were almost exclusively supervised conventional machine learning models (n = 39, 85%) such as decision tree classifiers and logistic regression, with only 7 applications of deep learning or natural language processing algorithms. Significant heterogeneity was found with regards to model validation methodology and measures of performance.

CONCLUSIONS

Limited applications of deep learning algorithms to clinical registry data have been reported. The lack of standardized validation methodology and heterogeneity of performance outcome reporting suggests that the application of AI to clinical registries is still in its infancy constrained by the poor accessibility of registry data and reflecting the need for a standardization of methodology and greater involvement of domain experts in the future development of clinically deployable AI.

The AI revolution in glaucoma: Bridging challenges with opportunities

Recent advancements in artificial intelligence (AI) herald transformative potentials for reshaping glaucoma clinical management, improving screening efficacy, sharpening diagnosis precision, and refining the detection of disease progression. However, incorporating AI into healthcare usages faces significant hurdles in terms of developing algorithms and putting them into practice. When creating algorithms, issues arise due to the intensive effort required to label data, inconsistent diagnostic standards, and a lack of thorough testing, which often limits the algorithms' widespread applicability. Additionally, the "black box" nature of AI algorithms may cause doctors to be wary or skeptical. When it comes to using these tools, challenges include dealing with lower-quality images in real situations and the systems' limited ability to work well with diverse ethnic groups and different diagnostic equipment. Looking ahead, new developments aim to protect data privacy through federated learning paradigms, improving algorithm generalizability by diversifying input data modalities, and augmenting datasets with synthetic imagery. The integration of smartphones appears promising for using AI algorithms in both clinical and non-clinical settings. Furthermore, bringing in large language models (LLMs) to act as interactive tool in medicine may signify a significant change in how healthcare will be delivered in the future. By navigating through these challenges and leveraging on these as opportunities, the field of glaucoma AI will not only have improved algorithmic accuracy and optimized data integration but also a paradigmatic shift towards enhanced clinical acceptance and a transformative improvement in glaucoma care.

Artificial intelligence in glaucoma: opportunities, challenges, and future directions

Artificial intelligence (AI) has shown excellent diagnostic performance in detecting various complex problems related to many areas of healthcare including ophthalmology. AI diagnostic systems developed from fundus images have become state-of-the-art tools in diagnosing retinal conditions and glaucoma as well as other ocular diseases. However, designing and implementing AI models using large imaging data is challenging. In this study, we review different machine learning (ML) and deep learning (DL) techniques applied to multiple modalities of retinal data, such as fundus images and visual fields for glaucoma detection, progression assessment, staging and so on. We summarize findings and provide several taxonomies to help the reader understand the evolution of conventional and emerging AI models in glaucoma. We discuss opportunities and challenges facing AI application in glaucoma and highlight some key themes from the existing literature that may help to explore future studies. Our goal in this systematic review is to help readers and researchers to understand critical aspects of AI related to glaucoma as well as determine the necessary steps and requirements for the successful development of AI models in glaucoma.

Artificial intelligence in ophthalmology: The path to the real-world clinic

Artificial intelligence (AI) has great potential to transform healthcare by enhancing the workflow and productivity of clinicians, enabling existing staff to serve more patients, improving patient outcomes, and reducing health disparities. In the field of ophthalmology, AI systems have shown performance comparable with or even better than experienced ophthalmologists in tasks such as diabetic retinopathy detection and grading. However, despite these quite good results, very few AI systems have been deployed in real-world clinical settings, challenging the true value of these systems. This review provides an overview of the current main AI applications in ophthalmology, describes the challenges that need to be overcome prior to clinical implementation of the AI systems, and discusses the strategies that may pave the way to the clinical translation of these systems.

24-Hour Monitoring of Intraocular Pressure Fluctuations Using a Contact Lens Sensor: Diagnostic Performance for Glaucoma Progression

Purpose

The purpose of this study was to compare 24-hour intraocular pressure (IOP) related fluctuations monitoring between 2 groups of visual field progression rates in patients with open angle glaucoma (OAG).

Methods

Cross-sectional study performed at Bordeaux University Hospital. Twenty-four-hour monitoring was performed using a contact lens sensor (CLS; Triggerfish; SENSIMED, Etagnières, Switzerland). Progression rate was calculated using a linear regression of the mean deviation (MD) parameter of the visual field test (Octopus; HAAG-STREIT, Switzerland). Patients were allocated into two groups: group 1 with an MD progression rate <-0.5 dB/year and group 2 with an MD progression rate ≥-0.5 dB/year. An automatic signal-processing program was developed and a frequency filtering of the monitoring by wavelet transform analysis was used to compare the output signal between the two groups. A multivariate classifier was performed for prediction of the faster progression group.

Results

Fifty-four eyes of 54 patients were included. The mean progression rate was -1.09 ± 0.60 dB/year in group 1 (n = 22) and -0.12 ± 0.13 dB/year in group 2 (n = 32). Twenty-four-hour magnitude and absolute area under the monitoring curve were significantly higher in group 1 than in group 2 (group 1: 343.1 ± 62.3 millivolts [mVs] and 8.28 ± 2.10 mVs, respectively, group 2: 274.0 ± 75.0 mV and 6.82 ± 2.70 mVs respectively, P < 0.05). Magnitude and area under the wavelet curve for short frequency periods ranging from 60 to 220 minutes were also significantly higher in group 1 (P < 0.05).

Conclusions

The 24-hour IOP related fluctuations characteristics, as assessed by a CLS, may act as a risk factor for progression in OAG. In association with other predictive factors of glaucoma progression, the CLS may help adjust treatment strategy earlier.

Using 24-h intraocular pressure-related patterns to identify open-angle glaucoma in thyroid eye disease

PURPOSE

Our study aims to develop a diagnostic model using 24-h intraocular pressure (IOP) patterns to differentiate between open-angle glaucoma (OAG) and dysthyroid optic neuropathy (DON) in thyroid eye disease (TED) patients with glaucoma-like symptoms.

METHODS

TED patients with elevated IOP, abnormal optic disc, and/or visual fields were prospectively recruited. The subjects whose symptoms were relieved by DON first-line treatments were divided into the DON group, and the subjects with previous diagnosis of OAG before TED onset were divided into the OAG group. The 24-h IOP was monitored by Tono-Pen in a sitting position during awake time and in a supine position during sleep time. All subjects were divided into a training set and a testing set. The diagnostic models were generated from training set by using either IOP curve-derived parameters or principal component (PC) factors. The discrimination ability was tested in training set based on area under curve (AUC), and the calibration ability was verified in testing set by Hosmer-Lemeshow goodness-of-fit. The sensitivity and specificity were calculated by two-by-two table with the cutoff value determined by Youden's index.

RESULTS

Thirty-two cases were recruited in each group. The 24-h IOP curves revealed a nocturnal pattern in both groups, with the acrophase moving slightly forward in the DON group (21:00 pm-24:00 pm) compared to the OAG group (22:00 pm-3:00 am). Several IOP curve-derived parameters differed between the two groups, with larger amplitude during sleep time (P < 0.000) and longer duration of IOP ≥ 21 mmHg at awake time (P = 0.004) in the DON group than the OAG group. However, the diagnostic model generated from IOP parameters showed poor reliability (P = 0.001) in calibration test and was rejected. The other model built on PC factors achieved good performance of discrimination (AUC = 0.943) and calibration (P = 0.139) with a sensitivity of 87.50% and a specificity of 95.83% at cutoff value of 0.538 to identify OAG cases.

CONCLUSION

The diagnostic model facilitates discrimination between OAG and DON in TED patients based on 24-h IOP-related patterns.

TRIAL REGISTRATION

This work was registered on Chinese Clinical Trial Registry (ChiCTR1900025394).

The upcoming role of Artificial Intelligence (AI) for retinal and glaucomatous diseases

In recent years, the role of artificial intelligence (AI) and deep learning (DL) models is attracting increasing global interest in the field of ophthalmology. DL models are considered the current state-of-art among the AI technologies. In fact, DL systems have the capability to recognize, quantify and describe pathological clinical features. Their role is currently being investigated for the early diagnosis and management of several retinal diseases and glaucoma. The application of DL models to fundus photographs, visual fields and optical coherence tomography (OCT) imaging has provided promising results in the early detection of diabetic retinopathy (DR), wet age-related macular degeneration (w-AMD), retinopathy of prematurity (ROP) and glaucoma. In this review we analyze the current evidence of AI applied to these ocular diseases, as well as discuss the possible future developments and potential clinical implications, without neglecting the present limitations and challenges in order to adopt AI and DL models as powerful tools in the everyday routine clinical practice.

Lab-on-a-Contact Lens: Recent Advances and Future Opportunities in Diagnostics and Therapeutics

The eye is one of the most complex organs in the human body, containing rich and critical physiological information (e.g., intraocular pressure, corneal temperature, and pH) as well as a library of metabolite biomarkers (e.g., glucose, proteins, and specific ions). Smart contact lenses (SCLs) can serve as a wearable intelligent ocular prosthetic device capable of noninvasive and continuous monitoring of various essential physical/biochemical parameters and drug loading/delivery for the treatment of ocular diseases. Advances in SCL technologies and the growing public interest in personalized health are accelerating SCL research more than ever before. Here, the current status and potential of SCL development through a comprehensive review from fabrication to applications to commercialization are discussed. First, the material, fabrication, and platform designs of the SCLs for the diagnostic and therapeutic applications are discussed. Then, the latest advances in diagnostic and therapeutic SCLs for clinical translation are reviewed. Later, the established techniques for wearable power transfer and wireless data transmission applied to current SCL devices are summarized. An outlook, future opportunities, and challenges for developing next-generation SCL devices are also provided. With the rise in interest of SCL development, this comprehensive and essential review can serve as a new paradigm for the SCL devices.

Anterior Segment and Others in Teleophthalmology: Past, Present, and Future

ABSTRACT

Teleophthalmology, a subfield of telemedicine, has recently been widely applied in ophthalmic disease management, accelerated by ubiquitous connectivity via mobile computing and communication applications. Teleophthalmology has strengths in overcoming geographic barriers and broadening access to medical resources, as a supplement to face-to-face clinical settings. Eyes, especially the anterior segment, are one of the most researched superficial parts of the human body. Therefore, ophthalmic images, easily captured by portable devices, have been widely applied in teleophthalmology, boosted by advancements in software and hardware in recent years. This review aims to revise current teleophthalmology applications in the anterior segment and other diseases from a temporal and spatial perspective, and summarize common scenarios in teleophthalmology, including screening, diagnosis, treatment, monitoring, postoperative follow-up, and tele-education of patients and clinical practitioners. Further, challenges in the current application of teleophthalmology and the future development of teleophthalmology are discussed.

Nocturnal Variability of Intraocular Pressure Monitored With Contact Lens Sensor Is Associated With Visual Field Loss in Glaucoma

PURPOSE

The aim was to determine whether 24-hour recording of intraocular pressure (IOP)-related ocular dimensional changes with a contact lens sensor (CLS, Triggerfish) is associated with the rate of visual field (VF) progression in primary open-angle glaucoma (POAG) patients.

DESIGN

This was a retrospective, observational cohort study.

PARTICIPANTS

Patients with POAG were included from the Glaucoma Clinic and Diagnostic Innovations in Glaucoma Study at the Hamilton Glaucoma Center at University of California, San Diego.

METHODS

A session of 24-hour CLS recording was acquired for 1 eye from each patient. The mean follow-up time was 9.9±4.0 years. The association between CLS variables and rate of change of mean deviation was determined by univariate and multivariate mixed linear regression models.

RESULTS

Thirty-two patients, aged 69.8±13.6 years were included, 50% were female. An average of 11.6±5.6 standard automated perimetry examinations was available with a mean rate of mean deviation progression of -0.2±0.4 dB/year. Mean IOP was 17.8±4.2 mm Hg. The mean number of IOP-lowering medications were 1.2±1.0. Each 10-unit larger nocturnal variability of IOP-related ocular dimensional changes measured by CLS recording was significantly associated with -0.25±0.11 dB faster VF loss in POAG patients (P=0.035).

CONCLUSIONS

Twenty-four-hour CLS recording of IOP-related ocular dimensional change was associated with faster VF progression. Such CLS recordings are useful to assess the risk of in progression in POAG patients.

A novel retinal ganglion cell quantification tool based on deep learning

Glaucoma is a disease associated with the loss of retinal ganglion cells (RGCs), and remains one of the primary causes of blindness worldwide. Major research efforts are presently directed towards the understanding of disease pathogenesis and the development of new therapies, with the help of rodent models as an important preclinical research tool. The ultimate goal is reaching neuroprotection of the RGCs, which requires a tool to reliably quantify RGC survival. Hence, we demonstrate a novel deep learning pipeline that enables fully automated RGC quantification in the entire murine retina. This software, called RGCode (Retinal Ganglion Cell quantification based On DEep learning), provides a user-friendly interface that requires the input of RBPMS-immunostained flatmounts and returns the total RGC count, retinal area and density, together with output images showing the computed counts and isodensity maps. The counting model was trained on RBPMS-stained healthy and glaucomatous retinas, obtained from mice subjected to microbead-induced ocular hypertension and optic nerve crush injury paradigms. RGCode demonstrates excellent performance in RGC quantification as compared to manual counts. Furthermore, we convincingly show that RGCode has potential for wider application, by retraining the model with a minimal set of training data to count FluoroGold-traced RGCs.

Weekly and seasonal changes of intraocular pressure measured with an implanted intraocular telemetry sensor

Background/Aims

To better understand seasonal and weekday intraocular pressure (IOP) variations, long-term daily IOP measurements were assessed in patients with glaucoma using an intraocular telemetric sensor.

Methods

This prospective, open-label, multicentre observational study analysed the IOP variation patterns in 22 eyes of 22 patients with primary open-angle glaucoma (67.8±6.8 years, 36.4% female) who had undergone placement of an intraocular telemetric sensor at the time of cataract surgery. The telemetric system combines an implantable IOP sensor with a hand-held reading device. Patients were instructed to self-measure their IOP as often as desired, but at least four times daily. Analysis of variance and Tukey multiple-comparison correction were used to assess the statistical significance of average and peak IOP variations between individual weekdays and months.

Results

Each enrolled patient recorded daily IOP measurements for an average duration of 721 days. On average, IOPs were highest on Wednesdays and lowest on Fridays (p=0.002). There were significant variations of IOP throughout the year, and IOP showed a seasonal pattern. Between mid-winter (December–January) and mid-summer months, there was a reduction in mean IOP of 8.1% (-1.55 mm Hg, p<0.05).

Conclusion

This study confirms previously observed seasonal variations of IOP. IOP was significantly higher in winter compared with summer months. Moreover, IOP was lower on Friday than on other days. The explanation for these results is not known.

Application of artificial intelligence in anterior segment ophthalmic diseases: diversity and standardization

Artificial intelligence (AI) based on machine learning (ML) and deep learning (DL) techniques has gained tremendous global interest in this era. Recent studies have demonstrated the potential of AI systems to provide improved capability in various tasks, especially in image recognition field. As an image-centric subspecialty, ophthalmology has become one of the frontiers of AI research. Trained on optical coherence tomography, slit-lamp images and even ordinary eye images, AI can achieve robust performance in the detection of glaucoma, corneal arcus and cataracts. Moreover, AI models based on other forms of data also performed satisfactorily. Nevertheless, several challenges with AI application in ophthalmology have also arisen, including standardization of data sets, validation and applicability of AI models, and ethical issues. In this review, we provided a summary of the state-of-the-art AI application in anterior segment ophthalmic diseases, potential challenges in clinical implementation and our prospects.

Monitoring Glaucomatous Functional Loss Using an Artificial Intelligence-Enabled Dashboard

PURPOSE

To develop an artificial intelligence (AI) dashboard for monitoring glaucomatous functional loss.

DESIGN

Retrospective, cross-sectional, longitudinal cohort study.

PARTICIPANTS

Of 31 591 visual fields (VFs) on 8077 subjects, 13 231 VFs from the most recent visit of each patient were included to develop the AI dashboard. Longitudinal VFs from 287 eyes with glaucoma were used to validate the models.

METHOD

We entered VF data from the most recent visit of glaucomatous and nonglaucomatous patients into a "pipeline" that included principal component analysis (PCA), manifold learning, and unsupervised clustering to identify eyes with similar global, hemifield, and local patterns of VF loss. We visualized the results on a map, which we refer to as an "AI-enabled glaucoma dashboard." We used density-based clustering and the VF decomposition method called "archetypal analysis" to annotate the dashboard. Finally, we used 2 separate benchmark datasets-one representing "likely nonprogression" and the other representing "likely progression"-to validate the dashboard and assess its ability to portray functional change over time in glaucoma.

MAIN OUTCOME MEASURES

The severity and extent of functional loss and characteristic patterns of VF loss in patients with glaucoma.

RESULTS

After building the dashboard, we identified 32 nonoverlapping clusters. Each cluster on the dashboard corresponded to a particular global functional severity, an extent of VF loss into different hemifields, and characteristic local patterns of VF loss. By using 2 independent benchmark datasets and a definition of stability as trajectories not passing through over 2 clusters in a left or downward direction, the specificity for detecting "likely nonprogression" was 94% and the sensitivity for detecting "likely progression" was 77%.

CONCLUSIONS

The AI-enabled glaucoma dashboard, developed using a large VF dataset containing a broad spectrum of visual deficit types, has the potential to provide clinicians with a user-friendly tool for determination of the severity of glaucomatous vision deficit, the spatial extent of the damage, and a means for monitoring the disease progression.

Can a contact lens sensor predict the success of trabectome surgery?

PURPOSE

We examined whether a contact lens sensor (CLS) is useful for the postoperative evaluation of trabectome surgery. We investigated the correlations between the outcomes of trabectome surgery and the output of a CLS.

METHODS

We examined 24 consecutive eyes of patients with pseudo-exfoliation glaucoma. In each eye, the intraocular pressure (IOP) fluctuations over 24 h were measured with the SENSIMED Triggerfish CLS before and at 3 months after the trabectome surgery. We divided the patients into success (n = 12 eyes) and failure (n = 12 eyes) groups; success was defined as a postoperative IOP level ≤ 21 mmHg plus an IOP reduction ≥ 20% relative to the preoperative IOP value with or without anti-glaucoma medications. We investigated CLS parameters that correlate with surgical outcomes by performing a Cox hazard regression analysis. We determined the maximum value, minimum value, and range of IOP fluctuation as CLS parameters.

RESULTS

The mean follow-up period was 38.0 ± 3.0 months. The success rate was 50%. The postoperative range of IOP fluctuation during the nocturnal period with the CLS was significantly correlated with the surgical results (p = 0.024).

CONCLUSIONS

A smaller range of IOP fluctuation was significantly correlated with better surgical outcomes. We were able to predict the surgical success after trabectome surgery at 3 months using the CLS. Thus, CLS results could be a new surgical evaluation parameter.

Fluctuations of the Intraocular Pressure in Medically Versus Surgically Treated Glaucoma Patients by a Contact Lens Sensor

PURPOSE

To compare fluctuations in intraocular pressure (IOP) in medically vs surgically treated glaucoma patients.

DESIGN

Prospective, nonrandomized case series.

METHODS

IOP-related fluctuations were measured for 24 hours using a contact lens sensor (CLS).

SUBJECTS

We performed monitoring with CLS in 91 eyes of 77 patients; 59 eyes were receiving ocular hypotensive medication and had no previous history of glaucoma surgery (medical group), while 32 eyes with open-angle glaucoma (OAG) had previously undergone glaucoma surgery (surgical group).

MAIN OUTCOME MEASURES

The amplitude, expressed as an indicator of the IOP-related fluctuation, and the presence of a nocturnal acrophase. We also identified maximum and minimum IOP-related values for each patient.

RESULTS

The mean (standard deviation) amplitude of IOP-related CLS signal in the group of surgically treated eyes was 100 (41) mV eq, while in the medically treated group it was 131 (69) mV eq (difference: P = .010). We found that 42.9% of the surgically treated but only 13.8% of the medically treated glaucoma group exhibited an absence of nocturnal acrophase (difference: P = .011). The maximum and minimum IOP-related values for the medical group were statistically higher than the surgical group (P = .001 and P = .006, respectively).

CONCLUSIONS

IOP-related fluctuations were larger in eyes with medically treated glaucoma than in those with surgically treated glaucoma. A significantly larger fraction of the surgical group exhibited an absence of nocturnal acrophase compared to the medically treated group.

Artificial intelligence in glaucoma

PURPOSE OF REVIEW

The use of computers has become increasingly relevant to medical decision-making, and artificial intelligence methods have recently demonstrated significant advances in medicine. We therefore provide an overview of current artificial intelligence methods and their applications, to help the practicing ophthalmologist understand their potential impact on glaucoma care.

RECENT FINDINGS

Techniques used in artificial intelligence can successfully analyze and categorize data from visual fields, optic nerve structure [e.g., optical coherence tomography (OCT) and fundus photography], ocular biomechanical properties, and a combination thereof to identify disease severity, determine disease progression, and/or recommend referral for specialized care. Algorithms have become increasingly complex in recent years, utilizing both supervised and unsupervised methods of artificial intelligence. Impressive performance of these algorithms on previously unseen data has been reported, often outperforming standard global indices and expert observers. However, there remains no clearly defined gold standard for determining the presence and severity of glaucoma, which undermines the training of these algorithms. To improve upon existing methodologies, future work must employ more robust definitions of disease, optimize data inputs for artificial intelligence analysis, and improve methods of extracting knowledge from learned results.

SUMMARY

Artificial intelligence has the potential to revolutionize the screening, diagnosis, and classification of glaucoma, both through the automated processing of large data sets, and by earlier detection of new disease patterns. In addition, artificial intelligence holds promise for fundamentally changing research aimed at understanding the development, progression, and treatment of glaucoma, by identifying novel risk factors and by evaluating the importance of existing ones.