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

[The role of iridotrabecular contact in the pathogenesis of primary angle closure disease]

PURPOSE

This study investigated the correlation between iridotrabecular contact (ITC) and clinical-anatomical parameters in patients with primary angle closure disease (PACD) based on anterior segment imaging data obtained via swept-source optical coherence tomography (SS-OCT).

MATERIAL AND METHODS

The study analyzed data from 92 patients aged 32-89 years, including 56 patients with PACD (43 phakic and 13 pseudophakic) and 36 control group participants (21 phakic and 15 pseudophakic). All participants underwent SS-OCT imaging of the anterior segment, including measurements of the ITC index and ITC area.

RESULTS

The study revealed that in phakic PACD patients, ITC parameters (ITC Index and ITC Area) were significantly correlated with anterior chamber depth (ACD; r=-0.42, p=0.01 and r=-0.43, p=0.00, respectively), lens vault (LV; r=0.35, p=0.02 and r=0.36, p=0.02, respectively), lens thickness (LT; r=0.47, p=0.01 and r=0.44, p=0.01, respectively), all anterior chamber angle (ACA) parameters (all p=0.00), and the number of hypotensive medications used (r=0.63, p=0.01 and r=0.68, p=0.01, respectively). In pseudophakic PACD patients, ITC Index and ITC Area were associated with the number of hypotensive medications (r=0.71, p=0.02 and r=0.72, p=0.02, respectively) and the iridotrabecular space area in the nasal sector (r=0.62, p=0.02). No significant correlations were observed in the control group, regardless of lens status.

CONCLUSION

ITC parameters in phakic PACD patients demonstrated the highest number of correlations compared to pseudophakic patients. The persistence of a direct relationship between ITC parameters and the number of hypotensive medications in primary angle-closure glaucoma following lens extraction supports the rationale for early surgical intervention.

[Primary angle closure suspects: application of machine learning method for substantiation of close monitoring]

One of the priority areas in healthcare is the concept of predictive, preventive and personalized medicine, which is based on an individualized approach to the patient, including before the onset of diseases such as glaucoma.

PURPOSE

This study was conducted to substantiate the necessity of close monitoring of primary angle closure suspects (PACs) by comparing their clinical and anatomical parameters with those in normal eyes and in primary angle closure (PAC) before and after lens extraction (LE) or laser peripheral iridotomy (LPI).

MATERIAL AND METHODS

This prospective study included 30 PACs patients. The comparison group consisted of 60 patients with PAC: 30 patients underwent LE with intraocular lens implantation, and 30 patients underwent LPI. Control group - 30 eyes without ophthalmic pathology. All subjects underwent swept-source optical coherence tomography (SS-OCT), including analysis of choroidal thickness in the macula, lens vault (LV), iris thickness and curvature (ICurv), and anterior chamber angle (ACA) profile. Machine learning methods were used, including data driven soft independent modelling of class analogies (DD-SIMCA).

RESULTS

The parameters of PACs eyes occupied an intermediate position between those of PAC before treatment (according to DD-SIMCA classification) and normal eyes, but remained distinct from PAC eyes after treatment, falling outside the "safety zone" relative to normal values. Compared with the PAC group after LE, the PACs group had a shallower anterior chamber (2.60±0.13 mm vs. 3.63±0.199 mm, p=0.00), a narrower ACA profile (all p=0.00), a steeper iris (all p=0.00), lower uncorrected visual acuity (0.50±0.24 vs. 0.95±0.08, p=0.00), and a higher spherical equivalent (SE). Compared with PAC eyes after PLI, the PACs had greater LV (0.84±0.11 mm vs. 0.58±0.07 mm, p=0.00), higher intraocular pressure (19.7±0.8 mm Hg vs. 16.9±2.0 mm Hg, p=0.00), greater ICurv (all p<0.05), higher SE, and a narrower ACA profile.

CONCLUSION

Untreated PACs have significantly worse clinical and anatomical parameters, both in comparison with the norm and with PAC patients after treatment. This substantiates the need for closer monitoring of PACs.

The applications of anterior segment optical coherence tomography in glaucoma: a 20-year bibliometric analysis

Objective

In the past 20 years, the research application of anterior segment optical coherence tomography (AS-OCT) in the field of glaucoma has become a hot topic, but there is still a lack of bibliometric reports on this scientific field. The aim of this study is to explore the research hotspots and trends in the field using bibliometric methods.

Method

Analyzing literature from 2004 to 2023 on AS-OCT in glaucoma within the SCI database, this study utilized Bibliometric, VOS viewer, and Cite Space for a comprehensive bibliometric analysis covering document counts, countries, institutions, journals, authors, references, and keywords.

Results

A total of 931 eligible articles were collected, showing a continuous increase in annual research output over the past 20 years. The United States, China, and Singapore were the top three countries in terms of publication volume, with 288, 231, and 124 articles, respectively, and there was close cooperation among these countries. The NATIONAL UNIVERSITY OF SINGAPORE, SUN YAT SEN UNIVERSITY, and SINGAPORE NATIONAL EYE CENTRE were the most productive institutions with 93, 92, and 87 articles, respectively. JOURNAL OF GLAUCOMA, INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE, and OPHTHALMOLOGY were the journals with the highest number of publications, with 86, 69, and 46 articles, respectively. PROGRESS IN RETINAL AND EYE RESEARCH, published in the United States, was the top-cited journal. Researchers Aung Tin, He Mingguang, and David S. Friedman were highlighted for their contributions. The reference clustering was divided into 12 categories, among which "deep learning, anterior segment" were the most cited categories. The keywords of research frontiers include deep learning, classification, progression, and management.

Conclusion

This article analyses the academic publications on AS-OCT in the diagnosis and treatment of glaucoma over the last 20 years. Among them, the United States contributed the largest number of publications in this field, with the highest number of literature citations and mediated centrality. Among the prolific authors, aung, tin topped the list with 77 publications and 3,428 citations. Since the beginning of 2018, advances in artificial intelligence have shifted the focus of research in this field from manual measurements to automated detection and identification of relevant indicators.

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 Anterior Chamber Evaluation: A Systematic Review and Meta-Analysis

PRCIS

In this meta-analysis of 6 studies and 5269 patients, deep learning algorithms applied to AS-OCT demonstrated excellent diagnostic performance for closed angle compared with gonioscopy, with a pooled sensitivity and specificity of 94% and 93.6%, respectively.

PURPOSE

This study aimed to review the literature and compare the accuracy of deep learning algorithms (DLA) applied to anterior segment optical coherence tomography images (AS-OCT) against gonioscopy in detecting angle closure in patients with glaucoma.

METHODS

We performed a systematic review and meta-analysis evaluating DLA in AS-OCT images for the diagnosis of angle closure compared with gonioscopic evaluation. PubMed, Scopus, Embase, Lilacs, Scielo, and Cochrane Central Register of Controlled Trials were searched. The bivariate model was used to calculate pooled sensitivity and specificity.

RESULTS

The initial search identified 214 studies, of which 6 were included for final analysis. The total study population included 5269 patients. The combined sensitivity of the DLA compared with gonioscopy was 94.0% (95% CI: 83.8%-97.9%), whereas the pooled specificity was 93.6% (95% CI: 85.7%-97.3%). Sensitivity analyses removing each individual study showed a pooled sensitivity in the range of 90.1%-95.1%. Similarly, specificity results ranged from 90.3% to 94.5% with the removal of each individual study and recalculation of pooled specificity.

CONCLUSION

DLA applied to AS-OCT has excellent sensitivity and specificity in the identification of angle closure. This technology may be a valuable resource in the screening of populations without access to experienced ophthalmologists who perform gonioscopy.

Diagnostic criteria of anterior segment swept-source optical coherence tomography to detect gonioscopic angle closure

AIMS

To compare the diagnostic performance of 360° anterior segment optical coherence tomography assessment by applying normative percentile cut-offs versus iris trabecular contact (ITC) for detecting gonioscopic angle closure.

METHODS

In this multicentre study, 394 healthy individuals were included in the normative dataset to derive the age-specific and angle location-specific normative percentiles of angle open distance (AOD500) and trabecular iris space area (TISA500) which were measured every 10° for 360°. 119 healthy participants and 170 patients with angle closure by gonioscopy were included in the test dataset to investigate the diagnostic performance of three sets of criteria for detection of gonioscopic angle closure: (1) the 10th and (2) the 5th percentiles of AOD500/TISA500, and (3) ITC (ie, AOD500/TISA500=0 mm/mm2). The number of angle locations with angle closure defined by each set of the criteria for each eye was used to generate the receiver operating characteristic (ROC) curve for the discrimination between gonioscopic angle closure and open angle.

RESULTS

Of the three sets of diagnostic criteria examined, the area under the ROC curve was greatest for the 10th percentile of AOD500 (0.933), whereas the ITC criterion AOD500=0 mm showed the smallest area under the ROC (0.852) and the difference was statistically significant with or without adjusting for age and axial length (p<0.001). The criterion ≥90° of AOD500 below the 10th percentile attained the best sensitivity 87.6% and specificity 84.9% combination for detecting gonioscopic angle closure.

CONCLUSIONS

Applying the normative percentiles of angle measurements yielded a higher diagnostic performance than ITC for detecting angle closure on gonioscopy.

Artificial intelligence and big data integration in anterior segment imaging for glaucoma

The integration of artificial intelligence (AI) and big data in anterior segment (AS) imaging represents a transformative approach to glaucoma diagnosis and management. This article explores various AS imaging techniques, such as AS optical coherence tomography, ultrasound biomicroscopy, and goniophotography, highlighting their roles in identifying angle-closure diseases. The review focuses on advancements in AI, including machine learning and deep learning, which enhance image analysis and automate complex processes in glaucoma care, and provides current evidence on the performance and clinical applications of these technologies. In addition, the article discusses the integration of big data, detailing its potential to revolutionize medical imaging by enabling comprehensive data analysis, fostering enhanced clinical decision-making, and facilitating personalized treatment strategies. In this article, we address the challenges of standardizing and integrating diverse data sets and suggest that future collaborations and technological advancements could substantially improve the management and research of glaucoma. This synthesis of current evidence and new technologies emphasizes their clinical relevance, offering insights into their potential to change traditional approaches to glaucoma evaluation and care.

Silicone oil emulsification: A literature review and role of widefield imaging and ultra-widefield imaging with navigated central and peripheral optical coherence tomography technology

Silicone oil (SO) emulsification is a significant concern in vitreoretinal surgery, leading to various complications. Despite the high prevalence of SO emulsification within the eye, there is currently no standardized method for its early detection. The recent introduction of widefield (WF) imaging and ultra-WF (UWF) imaging with navigated central and peripheral optical coherence tomography (OCT) techniques have shown promising results in providing high-resolution images of the peripheral vitreous, vitreoretinal interface, retina, and choroid. This enhanced visualization capability enables the early identification of emulsified SO droplets, facilitating a proactive therapeutic approach, and mitigating associated adverse events. This comprehensive literature review aims to provide an updated overview of the topic, focusing on the role of WFimaging and UWF imaging and navigated central and peripheral swept-source OCT (SS-OCT) in the early detection and management of SO emulsification. The review discusses the current understanding of SO emulsification, its associated complications, and the limitations of existing detection methods. In addition, it highlights the potential of WF and UWF imaging and peripheral OCT as advanced imaging modalities for improved visualization of SO emulsification. This review serves as a valuable resource for clinicians and researchers, providing insights into the latest advancements in the field of vitreoretinal surgery and the promising role of WF imaging and UWF imaging and navigated central and peripheral SS-OCT in the management of SO.

The utilization of artificial intelligence in glaucoma: diagnosis versus screening

With advancements in the implementation of artificial intelligence (AI) in different ophthalmology disciplines, it continues to have a significant impact on glaucoma diagnosis and screening. This article explores the distinct roles of AI in specialized ophthalmology clinics and general practice, highlighting the critical balance between sensitivity and specificity in diagnostic and screening models. Screening models prioritize sensitivity to detect potential glaucoma cases efficiently, while diagnostic models emphasize specificity to confirm disease with high accuracy. AI applications, primarily using machine learning (ML) and deep learning (DL), have been successful in detecting glaucomatous optic neuropathy from colored fundus photographs and other retinal imaging modalities. Diagnostic models integrate data extracted from various forms of modalities (including tests that assess structural optic nerve damage as well as those evaluating functional damage) to provide a more nuanced, accurate and thorough approach to diagnosing glaucoma. As AI continues to evolve, the collaboration between technology and clinical expertise should focus more on improving specificity of glaucoma diagnostic models to assess ophthalmologists to revolutionize glaucoma diagnosis and improve patients care.

Anterior Segment OCT for Detection of Narrow Angles: A Community-Based Diagnostic Accuracy Study

PURPOSE

To assess the diagnostic accuracy of anterior segment OCT (AS-OCT) screening for detecting gonioscopically narrow angles.

DESIGN

Population-based cross-sectional study.

PARTICIPANTS

A stratified random sample of individuals aged ≥ 60 years, selected from a door-to-door census performed in low-lying Nepal.

TESTING

Participants underwent AS-OCT, posterior segment OCT, and intraocular pressure (IOP) testing in the community. Those meeting referral criteria in either eye were invited to have a comprehensive eye examination including gonioscopy. Referral criteria included (i) the lowest 2.5% of AS-OCT measurements, (ii) retinal OCT results suggestive of glaucomatous optic neuropathy, diabetic retinopathy, or age-related macular degeneration, and (iii) elevated IOP.

MAIN OUTCOME MEASURES

Sensitivity and specificity of 5 semiautomated AS-OCT parameters relative to gonioscopically narrow angles, defined as the absence of visible trabecular meshwork for ≥ 180° on nonindentation gonioscopy.

RESULTS

Of 17 656 people aged ≥ 60 years enumerated from 102 communities, 12 633 (71.6%) presented for AS-OCT testing. Referral was recommended for 697 participants based on AS-OCT criteria and 2419 participants based on other criteria, of which 858 had gonioscopy performed by a glaucoma specialist. Each of the 5 AS-OCT parameters offered good diagnostic information for predicting eyes with gonioscopically narrow angles, with areas under the receiver operating characteristic curve ranging from 0.85 to 0.89. The angle opening distance at 750 μm from the scleral spur (AOD750) provided the most diagnostic information, providing an optimal sensitivity of 87% (95% confidence interval [CI], 75%-96%) and specificity of 77% (71%-83%) at a cutpoint of 367 μm, and a sensitivity of 65% (95% CI, 54%-74%) when specificity was constrained to 90% (cutpoint, 283 μm).

CONCLUSIONS

On AS-OCT, the AOD750 parameter detected approximately two-thirds of cases of gonioscopically narrow angles when test specificity was set to 90%. Although such a sensitivity may not be sufficient when screening solely for narrow angles, AS-OCT requires little additional effort if posterior segment OCT is already being performed and thus could provide incremental benefit when performing OCT-based screening.

FINANCIAL DISCLOSURE(S)

The authors have no proprietary or commercial interest in any materials discussed in this article.

SeqCorr-EUNet: A sequence correction dual-flow network for segmentation and quantification of anterior segment OCT image

The accurate segmentation of AS-OCT images is a prerequisite for the morphological details analysis of anterior segment structure and the extraction of clinical biological parameters, which play an essential role in the diagnosis, evaluation, and preoperative prognosis management of many ophthalmic diseases. Manually marking the boundaries of the anterior segment tissue is time-consuming and error-prone, with inherent speckle noise, various artifacts, and some low-quality scanned images further increasing the difficulty of the segmentation task. In this work, we propose a novel model called SeqCorr-EUNet with a dual-flow architecture based on convolutional gated recursive sequence correction for semantic segmentation and quantification of AS-OCT images. An EfficientNet encoder is employed to enhance the intra-slice features extraction ability of semantic segmentation flow. The sequence correction flow based on ConvGRU is introduced to extract inter-slice features from consecutive adjacent slices. Spatio-temporal information is fused to correct the morphological details of pre-segmentation results. And the channel attention gate is inserted into the skip-connection between encoder and decoder to enrich the contextual information and suppress the noise of irrelevant regions. Based on the segmentation results of the anterior segment structures, we achieved automatic extraction of essential clinical parameters, 3D reconstruction of the anterior chamber structure, and measurement of anterior chamber volume. The proposed SeqCorr-EUNet has been evaluated on the public AS-OCT dataset. The experimental results show that our method is competitive compared with the existing methods and significantly improves the segmentation and quantification performance of low-quality imaging structures in AS-OCT images.

[Investigation of iridotrabecular contact in primary angle closure]

PURPOSE

This study compares and evaluates the parameters of iridotrabecular contact (ITC) in patients with primary angle closure disease (PACD) with natural lenses and pseudophakia based on anterior segment imaging data from swept-source optical coherence tomography (SS-OCT).

MATERIAL AND METHODS

This retrospective study analyzed data from 92 patients aged 32 to 89 years, and included 56 patients with PACD (43 with natural lenses and 13 with pseudophakia) and 36 in the control group (21 with natural lenses and 15 with pseudophakia). All participants underwent SS-OCT (CASIA2; Tomey Corporation, Japan), which included an assessment of the ITC Index and ITC Area.

RESULTS

The ITC parameters in PACD, both with and without pseudophakia, were significantly higher than in the corresponding control groups. In pseudophakic PACD patients, the ITC Index was 8.7±7.2%, and the ITC Area was 0.82±0.54 mm², compared to 0.00±0.00% and 0.00±0.00 mm² in the pseudophakic control group (all p=0.01). For the PACD group with natural lenses, the ITC Index was 45.4±21.8%, and the ITC Area was 5.81±3.9 mm², compared to 0.05±0.11% and 0.56±1.17 mm² in the corresponding control group (all p=0.00). Significantly higher ITC values were found in the PACD group with natural lenses compared to those with pseudophakia (ITC Index 45.4±21.8% vs. 8.7±7.2%, respectively, p=0.00; ITC Area 5.81±3.9 mm² vs. 0.82±0.54 mm², respectively, p=0.00).

CONCLUSION

Significantly higher ITC values were demonstrated in PACD patients with natural lenses compared to those with pseudophakia. However, the persistence of residual angle closure in primary angle-closure glaucoma after lens extraction suggests the feasibility of proactive surgical intervention at earlier stages of the disease.

Assessment of angle closure disease in the age of artificial intelligence: A review

Primary angle closure glaucoma is a visually debilitating disease that is under-detected worldwide. Many of the challenges in managing primary angle closure disease (PACD) are related to the lack of convenient and precise tools for clinic-based disease assessment and monitoring. Artificial intelligence (AI)- assisted tools to detect and assess PACD have proliferated in recent years with encouraging results. Machine learning (ML) algorithms that utilize clinical data have been developed to categorize angle closure eyes by disease mechanism. Other ML algorithms that utilize image data have demonstrated good performance in detecting angle closure. Nonetheless, deep learning (DL) algorithms trained directly on image data generally outperformed traditional ML algorithms in detecting PACD, were able to accurately differentiate between angle status (open, narrow, closed), and automated the measurement of quantitative parameters. However, more work is required to expand the capabilities of these AI algorithms and for deployment into real-world practice settings. This includes the need for real-world evaluation, establishing the use case for different algorithms, and evaluating the feasibility of deployment while considering other clinical, economic, social, and policy-related factors.

[Application of artificial intelligence in glaucoma. Part 2. Neural networks and machine learning in the monitoring and treatment of glaucoma]

The second part of the literature review on the application of artificial intelligence (AI) methods for screening, diagnosing, monitoring, and treating glaucoma provides information on how AI methods enhance the effectiveness of glaucoma monitoring and treatment, presents technologies that use machine learning, including neural networks, to predict disease progression and determine the need for anti-glaucoma surgery. The article also discusses the methods of personalized treatment based on projection machine learning methods and outlines the problems and prospects of using AI in solving tasks related to screening, diagnosing, and treating glaucoma.

[Application of artificial intelligence in glaucoma. Part 1. Neural networks and deep learning in glaucoma screening and diagnosis]

This article reviews literature on the use of artificial intelligence (AI) for screening, diagnosis, monitoring and treatment of glaucoma. The first part of the review provides information how AI methods improve the effectiveness of glaucoma screening, presents the technologies using deep learning, including neural networks, for the analysis of big data obtained by methods of ocular imaging (fundus imaging, optical coherence tomography of the anterior and posterior eye segments, digital gonioscopy, ultrasound biomicroscopy, etc.), including a multimodal approach. The results found in the reviewed literature are contradictory, indicating that improvement of the AI models requires further research and a standardized approach. The use of neural networks for timely detection of glaucoma based on multimodal imaging will reduce the risk of blindness associated with glaucoma.

[Application of artificial intelligence methods in the diagnosis and treatment of primary angle-closure disease]

This article reviews literature on the use of artificial intelligence (AI) methods for the diagnosis and treatment of primary angle-closure disease (PACD). The review describes how AI techniques enhance the efficiency of population screening for anterior chamber angle closure, presents technologies utilizing deep learning, including neural networks, for the analysis of large datasets obtained through anterior segment imaging methods, such as anterior segment optical coherence tomography (AS-OCT), digital gonioscopy, and ultrasound biomicroscopy, and discusses methods for treating PACD with the help of AI. Integration of deep learning and imaging techniques represents a crucial step in optimizing the diagnosis and treatment of PACD, reducing the burden on the healthcare system.

Assessment of the reproducibility and accuracy of the Visia® Complexion Analysis Camera System for objective skin analysis of facial wrinkles and skin age

Objective

This study aimed to investigate the reproducibility and accuracy of the Visia® Complexion Analysis Camera System by Canfield Scientific for objective skin analysis.

Methods

Nineteen participants underwent facial capture with the Visia® camera following a standardised protocol. During the first session, the participants sat down and positioned their faces in a capture rig, closed their eyes and had their faces captured from the left, front and right sides, with threefold repetition of the captures from the front side. After 4 weeks, the participants underwent recapture in a similar manner. Based on the frontal views, data for two measurement methods of the Visia® camera system, the absolute scores and the percentiles, were obtained with regard to the skin criterion wrinkles via automated software calculation. Means and standard deviations were evaluated. Based on the side views, the data for the Truskin Ages® were calculated by the Visia® camera system and compared with the calendrical ages, which served as the gold standard for comparison.

Results

In the assessment of the reproducibility of the data of the capture system the standard deviation from the frontal captures among all participants was about 3% when the absolute scores of the wrinkles were compared with each other; specifically, the average deviation was 3.36% during the first capture session and 3.4% during the second capture session. Meanwhile, the standard deviation of the measurements was about 9% when the percentiles were compared; specifically, the average deviation was 8.2% during the first capture session and 10.7% during the second capture session. In the assessment of the accuracy the correlation between the calendrical age and the calculated Truskin Age® for both facial sides was very high at a correlation coefficient rho value of >0.8 (right side: r=0.896; left side: r=0.827) and statistically significant at a p-value of <0.001. The average calendrical age and Truskin Age® deviated only slightly from each other and did not differ significantly (right side: p=0.174; left side: p=0.190). The Truskin Age® was slightly higher than the calendrical age by a mean value of 1.37 years for both facial sides. The analysis of the absolute differences revealed that in 50% of the cases, there was a maximum difference of 3 years, and in 75% of the cases, there were maximum differences of 4.5 years for the right side and 5.5 years for the left side.

Conclusion

The assessment of the reproducibility and accuracy of the objective measurement method, the Visia® camera system, contributed to the validation of the system. The evaluation of the reproducibility revealed a satisfactory precision of the repeated captures when investigating facial wrinkles. Absolute scores should be preferred over percentiles owing to their better precision. The calculation of the accuracy of the Truskin Age® data from the Visia® camera system revealed only a slight deviation from the true calendrical ages. The correlation between both data groups was highly significant.

Machine learning-couched treatment algorithms tailored to individualized profile of patients with primary anterior chamber angle closure predisposed to the glaucomatous optic neuropathy

Background

Primary angle closure glaucoma (PACG) is still one of the leading causes of irreversible blindness, with a trend towards an increase in the number of patients to 32.04 million by 2040, an increase of 58.4% compared with 2013. Health risk assessment based on multi-level diagnostics and machine learning-couched treatment algorithms tailored to individualized profile of patients with primary anterior chamber angle closure are considered essential tools to reverse the trend and protect vulnerable subpopulations against health-to-disease progression.

Aim

To develop a methodology for personalized choice of an effective method of primary angle closure (PAC) treatment based on comparing the prognosis of intraocular pressure (IOP) changes due to laser peripheral iridotomy (LPI) or lens extraction (LE).

Methods

The multi-parametric data analysis was used to develop models predicting individual outcomes of the primary angle closure (PAC) treatment with LPI and LE. For doing this, we suggested a positive dynamics in the intraocular pressure (IOP) after treatment, as the objective measure of a successful treatment. Thirty-seven anatomical parameters have been considered by applying artificial intelligence to the prospective study on 30 (LE) + 30 (LPI) patients with PAC.

Results and data interpretation in the framework of 3P medicine

Based on the anatomical and topographic features of the patients with PAC, mathematical models have been developed that provide a personalized choice of LE or LPI in the treatment. Multi-level diagnostics is the key tool in the overall advanced approach. To this end, for the future application of AI in the area, it is strongly recommended to consider the following:Clinically relevant phenotyping applicable to advanced population screeningSystemic effects causing suboptimal health conditions considered in order to cost-effectively protect affected individuals against health-to-disease transitionClinically relevant health risk assessment utilizing health/disease-specific molecular patterns detectable in body fluids with high predictive power such as a comprehensive tear fluid analysis.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-023-00337-1.

Advancements in high-resolution imaging of the iridocorneal angle

High-resolution imaging methods of the iridocorneal angle (ICA) will lead to enhanced understanding of aqueous humor outflow mechanisms and a characterization of the trabecular meshwork (TM) morphology at the cellular level will help to better understand glaucoma mechanics (e.g., cellular level biomechanics of the particulate glaucomas). This information will translate into immense clinical value, leading to more informed and customized treatment selection, and improved monitoring of procedural interventions that lower intraocular pressure (IOP). Given ICA anatomy, imaging modalities that yield intrinsic optical sectioning or 3D imaging capability will be useful to aid in the visualization of TM layers. This minireview examines advancements in imaging the ICA in high-resolution.

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.

Deep Learning-Based Classification of Subtypes of Primary Angle-Closure Disease With Anterior Segment Optical Coherence Tomography

PRCIS

We developed a deep learning-based classifier that can discriminate primary angle closure suspects (PACS), primary angle closure (PAC)/primary angle closure glaucoma (PACG), and also control eyes with open angle with acceptable accuracy.

PURPOSE

To develop a deep learning-based classifier for differentiating subtypes of primary angle closure disease, including PACS and PAC/PACG, and also normal control eyes.

MATERIALS AND METHODS

Anterior segment optical coherence tomography images were used for analysis with 5 different networks including MnasNet, MobileNet, ResNet18, ResNet50, and EfficientNet. The data set was split with randomization performed at the patient level into a training plus validation set (85%), and a test data set (15%). Then 4-fold cross-validation was used to train the model. In each mentioned architecture, the networks were trained with original and cropped images. Also, the analyses were carried out for single images and images grouped on the patient level (case-based). Then majority voting was applied to the determination of the final prediction.

RESULTS

A total of 1616 images of normal eyes (87 eyes), 1055 images of PACS (66 eyes), and 1076 images of PAC/PACG (66 eyes) eyes were included in the analysis. The mean ± SD age was 51.76 ± 15.15 years and 48.3% were males. MobileNet had the best performance in the model, in which both original and cropped images were used. The accuracy of MobileNet for detecting normal, PACS, and PAC/PACG eyes was 0.99 ± 0.00, 0.77 ± 0.02, and 0.77 ± 0.03, respectively. By running MobileNet in a case-based classification approach, the accuracy improved and reached 0.95 ± 0.03, 0.83 ± 0.06, and 0.81 ± 0.05, respectively. For detecting the open angle, PACS, and PAC/PACG, the MobileNet classifier achieved an area under the curve of 1, 0.906, and 0.872, respectively, on the test data set.

CONCLUSION

The MobileNet-based classifier can detect normal, PACS, and PAC/PACG eyes with acceptable accuracy based on anterior segment optical coherence tomography images.

The application of artificial intelligence in glaucoma diagnosis and prediction

Artificial intelligence is a multidisciplinary and collaborative science, the ability of deep learning for image feature extraction and processing gives it a unique advantage in dealing with problems in ophthalmology. The deep learning system can assist ophthalmologists in diagnosing characteristic fundus lesions in glaucoma, such as retinal nerve fiber layer defects, optic nerve head damage, optic disc hemorrhage, etc. Early detection of these lesions can help delay structural damage, protect visual function, and reduce visual field damage. The development of deep learning led to the emergence of deep convolutional neural networks, which are pushing the integration of artificial intelligence with testing devices such as visual field meters, fundus imaging and optical coherence tomography to drive more rapid advances in clinical glaucoma diagnosis and prediction techniques. This article details advances in artificial intelligence combined with visual field, fundus photography, and optical coherence tomography in the field of glaucoma diagnosis and prediction, some of which are familiar and some not widely known. Then it further explores the challenges at this stage and the prospects for future clinical applications. In the future, the deep cooperation between artificial intelligence and medical technology will make the datasets and clinical application rules more standardized, and glaucoma diagnosis and prediction tools will be simplified in a single direction, which will benefit multiple ethnic groups.

Review of glaucoma management in France

PURPOSE

To conduct a review of glaucoma management in France.

METHOD

A 15-question survey was sent to ophthalmologists listed in the journal Réalités Ophtalmologiques and the Syndicat National des Ophtalmologues de France.

RESULTS

459/471 responses were analyzed. Gonioscopy was performed by 64.7% of respondents with a Goldmann three-mirror lens, by 51.4% with a four-mirror lens, and 8.2% preferred to perform the procedure with anterior segment imaging. The visual field was reported to be interpreted without difficulty by 87.8% of the practitioners, and 54.0% utilize a progression software. Ultrasound biomicroscopy was reported to be interpreted without difficulty by 20.0% of practitioners. In cases of severe ocular hypertonia with flat bleb in early postoperative trabeculectomy, 61.7% chose ocular massage as a first-line treatment, 52.9% chose laser suture lysis, 50.5% utilized needling, and 24.8% employed hypotonizing eyedrops. In case of severe ocular hypertonia with flat bleb in early postoperative deep sclerectomy, 53.2% chose goniopuncture as their first treatment, 34.4% employed needling, 31.8% utilized ocular massage, and 23.3% chose hypotonizing eyedrops. The selective laser trabeculoplasty is used as soon as the diagnosis is made by 37.5%, in association with a mono or dual therapy by 93.2%, after trying different combinations of eyedrops by 45.5%, when the visual field deteriorates despite a normalized intraocular pressure by 46.6%, and in cases of hypertonia after filtering surgery by 19.2%. Concerning management for primary angle-closure glaucoma, 80.8% considered peripheral iridotomy, and 18.7% utilized cataract surgery.

CONCLUSION

The diversity of responses concerning glaucoma management should draw attention to the need for standardized practices.

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.

Detecting eyes with high risk of angle closure among apparently normal eyes by anterior segment OCT: a health examination center-based model

BACKGROUND

The main barriers keeping individuals with high-risk of angle closure from seeking eye-care service are the absence of both disease awareness and convenient and low-cost access to the ocular health care system. Present study described the efficacy of a health examination center-based screening model designed to detect eyes with high risk of angle closure (HRAC) among healthy individuals using anterior segment optical coherence tomography (AS-OCT).

METHODS

From March 1 to April 30, 2017, consecutive individuals aged ≥ 40 years undergoing routine physical examinations at a health examination center were invited to enroll. Presenting visual acuity (PVA), intraocular pressure (IOP) measurement, non-mydriatic fundus photography and AS-OCT were performed by three trained nurses. Participants with PVA < 6/12 in the better-seeing eye, IOP ≥ 24 mmHg, or abnormal fundus photography in either eye were referred to the outpatient clinic, but not included in the analysis. Eyes with HRAC were defined as having trabecular-iris angle < 12 degrees in ≥ 3 quadrants. Configuration of the iris was classified into flat, bowing, bombe, thick peripheral iris and mixed mechanism.

RESULTS

Altogether, 991 participants (77.3%) with readable OCT images (mean age 55.5 ± 9.0 years; 58.4% men) were included. HRAC was diagnosed in 78 eyes (7.9%, 61.3 ± 8.2 years, 41.0% men). The prevalence of HRAC increased with age (p < 0.001) and was much higher among women (11.2%) than men (5.5%) (p = 0.001). The mixed mechanism iris configuration was most common among eyes with HRAC (37/78, 47.4%).

CONCLUSION

HRAC is prevalent among asymptomatic Chinese adults undergoing routine health screening. Health examination center-based eye screening with AS-OCT administered by non-specialists may be a good model to screen narrow angles in the population at large.

Dynamic analysis of iris changes and a deep learning system for automated angle-closure classification based on AS-OCT videos

Background

To study the association between dynamic iris change and primary angle-closure disease (PACD) with anterior segment optical coherence tomography (AS-OCT) videos and develop an automated deep learning system for angle-closure screening as well as validate its performance.

Methods

A total of 369 AS-OCT videos (19,940 frames)—159 angle-closure subjects and 210 normal controls (two datasets using different AS-OCT capturing devices)—were included. The correlation between iris changes (pupil constriction) and PACD was analyzed based on dynamic clinical parameters (pupil diameter) under the guidance of a senior ophthalmologist. A temporal network was then developed to learn discriminative temporal features from the videos. The datasets were randomly split into training, and test sets and fivefold stratified cross-validation were used to evaluate the performance.

Results

For dynamic clinical parameter evaluation, the mean velocity of pupil constriction (VPC) was significantly lower in angle-closure eyes (0.470 mm/s) than in normal eyes (0.571 mm/s) (P < 0.001), as was the acceleration of pupil constriction (APC, 3.512 mm/s²vs. 5.256 mm/s²; P < 0.001). For our temporal network, the areas under the curve of the system using AS-OCT images, original AS-OCT videos, and aligned AS-OCT videos were 0.766 (95% CI: 0.610–0.923) vs. 0.820 (95% CI: 0.680–0.961) vs. 0.905 (95% CI: 0.802–1.000) (for Casia dataset) and 0.767 (95% CI: 0.620–0.914) vs. 0.837 (95% CI: 0.713–0.961) vs. 0.919 (95% CI: 0.831–1.000) (for Zeiss dataset).

Conclusions

The results showed, comparatively, that the iris of angle-closure eyes stretches less in response to illumination than in normal eyes. Furthermore, the dynamic feature of iris motion could assist in angle-closure classification.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40662-022-00314-1.

Deep learning and optical coherence tomography in glaucoma: Bridging the diagnostic gap on structural imaging

Glaucoma is a leading cause of progressive blindness and visual impairment worldwide. Microstructural evidence of glaucomatous damage to the optic nerve head and associated tissues can be visualized using optical coherence tomography (OCT). In recent years, development of novel deep learning (DL) algorithms has led to innovative advances and improvements in automated detection of glaucomatous damage and progression on OCT imaging. DL algorithms have also been trained utilizing OCT data to improve detection of glaucomatous damage on fundus photography, thus improving the potential utility of color photos which can be more easily collected in a wider range of clinical and screening settings. This review highlights ten years of contributions to glaucoma detection through advances in deep learning models trained utilizing OCT structural data and posits future directions for translation of these discoveries into the field of aging and the basic sciences.

The Development and Clinical Application of Innovative Optical Ophthalmic Imaging Techniques

The field of ophthalmic imaging has grown substantially over the last years. Massive improvements in image processing and computer hardware have allowed the emergence of multiple imaging techniques of the eye that can transform patient care. The purpose of this review is to describe the most recent advances in eye imaging and explain how new technologies and imaging methods can be utilized in a clinical setting. The introduction of optical coherence tomography (OCT) was a revolution in eye imaging and has since become the standard of care for a plethora of conditions. Its most recent iterations, OCT angiography, and visible light OCT, as well as imaging modalities, such as fluorescent lifetime imaging ophthalmoscopy, would allow a more thorough evaluation of patients and provide additional information on disease processes. Toward that goal, the application of adaptive optics (AO) and full-field scanning to a variety of eye imaging techniques has further allowed the histologic study of single cells in the retina and anterior segment. Toward the goal of remote eye care and more accessible eye imaging, methods such as handheld OCT devices and imaging through smartphones, have emerged. Finally, incorporating artificial intelligence (AI) in eye images has the potential to become a new milestone for eye imaging while also contributing in social aspects of eye care.

A deep-learning system predicts glaucoma incidence and progression using retinal photographs

BackgroundDeep learning has been widely used for glaucoma diagnosis. However, there is no clinically validated algorithm for glaucoma incidence and progression prediction. This study aims to develop a clinically feasible deep-learning system for predicting and stratifying the risk of glaucoma onset and progression based on color fundus photographs (CFPs), with clinical validation of performance in external population cohorts.MethodsWe established data sets of CFPs and visual fields collected from longitudinal cohorts. The mean follow-up duration was 3 to 5 years across the data sets. Artificial intelligence (AI) models were developed to predict future glaucoma incidence and progression based on the CFPs of 17,497 eyes in 9346 patients. The area under the receiver operating characteristic (AUROC) curve, sensitivity, and specificity of the AI models were calculated with reference to the labels provided by experienced ophthalmologists. Incidence and progression of glaucoma were determined based on longitudinal CFP images or visual fields, respectively.ResultsThe AI model to predict glaucoma incidence achieved an AUROC of 0.90 (0.81-0.99) in the validation set and demonstrated good generalizability, with AUROCs of 0.89 (0.83-0.95) and 0.88 (0.79-0.97) in external test sets 1 and 2, respectively. The AI model to predict glaucoma progression achieved an AUROC of 0.91 (0.88-0.94) in the validation set, and also demonstrated outstanding predictive performance with AUROCs of 0.87 (0.81-0.92) and 0.88 (0.83-0.94) in external test sets 1 and 2, respectively.ConclusionOur study demonstrates the feasibility of deep-learning algorithms in the early detection and prediction of glaucoma progression.FUNDINGNational Natural Science Foundation of China (NSFC); the High-level Hospital Construction Project, Zhongshan Ophthalmic Center, Sun Yat-sen University; the Science and Technology Program of Guangzhou, China (2021), the Science and Technology Development Fund (FDCT) of Macau, and FDCT-NSFC.