Identifying "Preperimetric" Glaucoma in Standard Automated Perimetry Visual Fields

Cardiac Surgery Patients Have Reduced Vascularity and Structural Defects of the Retina Similar to Persons with Open-Angle Glaucoma

(1) Background: Growing evidence suggests impairment of ocular blood flow in open-angle glaucoma (OAG) pathology, but little is known about the effect of an impaired cardiovascular supply on the structural and vascular parameters of the retina. This study aims to investigate the variations of these parameters in OAG patients compared to patients undergoing cardiac surgery (CS) with cardiopulmonary bypass. (2) Methods: Prospective observational study with 82 subjects (30 controls, 33 OAG patients, and 19 CS patients) who underwent ophthalmological assessment by swept-source OCT and CDI in one randomly selected eye. (3) Results: In the CS group, OA and SPCA PSV and EDV were significantly lower, OA and SPCA RI were significantly higher compared to the OAG and healthy subjects (p = 0.000-0.013), and SPCA EDV correlated with linear CDR (r = -0.508, p = 0.027). Temporal ONH sectors of GCL++ and GCL+ layers in the CS group did not differ significantly compared to the OAG patients (p = 0.085 and p = 0.220). The CS patients had significantly thinner GCL++ and GCL+ layers in the inner sectors (p = 0.000-0.038) compared to healthy subjects, and these layers correlated with the CRA PSV, EDV, and RI and SPCA PSV (p = 0.005-0.047). (4) Conclusions: CS patients had lower vascular and structural parameters in the ONH, and macula compared to the healthy controls that were similar to persons with OAG.

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.

Rationale and Development of an OCT-Based Method for Detection of Glaucomatous Optic Neuropathy

A specific, sensitive, and intersubjectively verifiable definition of disease for clinical care and research remains an important unmet need in the field of glaucoma. Using an iterative, consensus-building approach and employing pilot data, an optical coherence tomography (OCT)-based method to aid in the detection of glaucomatous optic neuropathy was sought to address this challenge. To maximize the chance of success, we utilized all available information from the OCT circle and cube scans, applied both quantitative and semiquantitative data analysis methods, and aimed to limit the use of perimetry to cases where it is absolutely necessary. The outcome of this approach was an OCT-based method for the diagnosis of glaucomatous optic neuropathy that did not require the use of perimetry for initial diagnosis. A decision tree was devised for testing and implementation in clinical practice and research that can be used by reading centers, researchers, and clinicians. While initial pilot data were encouraging, future testing and validation will be needed to establish its utility in clinical practice, as well as for research.

The Role of Artificial Intelligence in the Diagnosis and Management of Glaucoma

Glaucomatous optic neuropathy is the leading cause of irreversible blindness worldwide. Diagnosis and monitoring of disease involves integrating information from the clinical examination with subjective data from visual field testing and objective biometric data that includes pachymetry, corneal hysteresis, and optic nerve and retinal imaging. This intricate process is further complicated by the lack of clear definitions for the presence and progression of glaucomatous optic neuropathy, which makes it vulnerable to clinician interpretation error. Artificial intelligence (AI) and AI-enabled workflows have been proposed as a plausible solution. Applications derived from this field of computer science can improve the quality and robustness of insights obtained from clinical data that can enhance the clinician's approach to patient care. This review clarifies key terms and concepts used in AI literature, discusses the current advances of AI in glaucoma, elucidates the clinical advantages and challenges to implementing this technology, and highlights potential future applications.

A Case for The Use of Artificial Intelligence in Glaucoma Assessment

We hypothesize that artificial intelligence applied to relevant clinical testing in glaucoma has the potential to enhance the ability to detect glaucoma. This premise was discussed at the recent Collaborative Community for Ophthalmic Imaging meeting, "The Future of Artificial Intelligence-Enabled Ophthalmic Image Interpretation: Accelerating Innovation and Implementation Pathways," held virtually September 3-4, 2020. The Collaborative Community in Ophthalmic Imaging (CCOI) is an independent self-governing consortium of stakeholders with broad international representation from academic institutions, government agencies, and the private sector whose mission is to act as a forum for the purpose of helping speed innovation in healthcare technology. It was one of the first two such organizations officially designated by the FDA in September 2019 in response to their announcement of the collaborative community program as a strategic priority for 2018-2020. Further information on the CCOI can be found online at their website (https://www.cc-oi.org/about). Artificial intelligence for glaucoma diagnosis would have high utility globally, as access to care is limited in many parts of the world and half of all people with glaucoma are unaware of their illness. The application of artificial intelligence technology to glaucoma diagnosis has the potential to broadly increase access to care worldwide, in essence flattening the Earth by providing expert level evaluation to individuals even in the most remote regions of the planet.

Prediction of Visual Field Progression from OCT Structural Measures in Moderate to Advanced Glaucoma

PURPOSE

To test the hypothesis that visual field (VF) progression can be predicted from baseline and longitudinal optical coherence tomography (OCT) structural measurements.

DESIGN

Prospective cohort study.

METHODS

A total of 104 eyes (104 patients) with ≥3 years of follow-up and ≥5 VF examinations were enrolled. We defined VF progression based on pointwise linear regression on 24-2 VF (≥3 locations with slope less than or equal to -1.0 dB/year and P < .01). We used elastic net logistic regression (ENR) and machine learning to predict VF progression with demographics, baseline circumpapillary retinal nerve fiber layer (RNFL), macular ganglion cell/inner plexiform layer (GCIPL) thickness, and RNFL and GCIPL change rates at central 24 superpixels and 3 eccentricities, 3.4°, 5.5°, and 6.8°, from fovea and hemimaculas. Areas-under-ROC curves (AUC) were used to compare models.

RESULTS

Average ± SD follow-up and VF examinations were 4.5 ± 0.9 years and 8.7 ± 1.6, respectively. VF progression was detected in 23 eyes (22%). ENR selected rates of change of superotemporal RNFL sector and GCIPL change rates in 5 central superpixels and at 3.4° and 5.6° eccentricities as the best predictor subset (AUC = 0.79 ± 0.12). Best machine learning predictors consisted of baseline superior hemimacular GCIPL thickness and GCIPL change rates at 3.4° eccentricity and 3 central superpixels (AUC = 0.81 ± 0.10). Models using GCIPL-only structural variables performed better than RNFL-only models.

CONCLUSIONS

VF progression can be predicted with clinically relevant accuracy from baseline and longitudinal structural data. Further refinement of proposed models would assist clinicians with timely prediction of functional glaucoma progression and clinical decision making.

Evaluation of ganglion cell-inner plexiform layer thickness in the diagnosis of preperimetric glaucoma and comparison to retinal nerve fiber layer

Purpose:

The aim of this study was to evaluate the diagnostic ability of optic nerve head (ONH), RNFL, and GC-IPL parameters in differentiating eyes with PPG from normals.

Methods:

This was a retrospective, cross-sectional, observational study. We studied 73 eyes of 41 patients and compared them to 65 eyes of 34 normal persons. Each patient underwent detailed ocular examination, standard automated perimetry, GC-IPL, ONH, and RNFL analysis. PPG was defined as eyes with normal visual field results and one or more localized RNFL defects that were associated with a glaucomatous disc appearance (e.g., notching or thinning of neuroretinal rim) and IOP more than 21 mm Hg. Diagnostic abilities of GC-IPL, ONH, and RNFL parameters were computed using area under receiver-operating curve (AUROC), sensitivity and specificity, and likelihood ratios (LRs).

Results:

All GC-IPL parameters differed significantly from normal. The ONH, RNFL, and GC-IPL parameters with best area under curves (AUCs) to differentiate PPG were vertical cup to disc ratio (0.76), inferior quadrant RNFL thickness (0.79), and inferotemporal quadrant GC-IPL thickness (0.73), respectively. Similarly, best LRs were found for clock hour 5, 6, and 12 thicknesses among RNFL; inferior sector and inferotemporal sector thicknesses among GC-IPL parameters.

Conclusion:

Diagnostic abilities of GC-IPL parameters were comparable to RNFL parameters in differentiating PPG patients from normals. The likelihood of ruling in a disease was greater with GC-IPL parameters.

Relationship between Vision-Related Quality of Life and Central 10° of the Binocular Integrated Visual Field in Advanced Glaucoma

To investigate the relationships between sensitivity loss in various subfields of the central 10° of the binocular integrated visual field (IVF) and vision-related quality of life (VRQoL) in 172 patients with advanced glaucoma. Using the Random Forest algorithm, which controls for inter-correlations among various subfields of the IVF, we analysed the relationships among the Rasch analysis-derived person ability index (RADPAI), age, best-corrected visual acuity (BCVA), mean total deviations (mTDs) of eight quadrant subfields in the IVF measured with the Humphrey Field Analyzer (HFA) 10-2 program (10-2 IVF), and mTDs of the upper/lower hemifields in the IVF measured with the HFA 24-2 program (24-2 IVF). Significant contributors to RADPAIs were as follows: the inner and outer lower-right quadrants of the 10-2 IVF contributed to the dining and total tasks; the lower-left quadrant of the 10-2 IVF contributed to the walking, going out and total tasks; the lower hemifield of the 24-2 IVF contributed to the walking, going out, dining, miscellaneous and total tasks; and BCVA contributed more to the letter, sentence, dressing and miscellaneous tasks than to others. The impact of damage in different 10-2 IVF subfields differed significantly across daily tasks in patients with advanced glaucoma.

Machine learning models based on the dimensionality reduction of standard automated perimetry data for glaucoma diagnosis

INTRODUCTION

Visual field testing via standard automated perimetry (SAP) is a commonly used glaucoma diagnosis method. Applying machine learning techniques to the visual field test results, a valid clinical diagnosis of glaucoma solely based on the SAP data is provided. In order to reflect structural-functional patterns of glaucoma on the automated diagnostic models, we propose composite variables derived from anatomically grouped visual field clusters to improve the prediction performance. A set of machine learning-based diagnostic models are designed that implement different input data manipulation, dimensionality reduction, and classification methods.

METHODS

Visual field testing data of 375 healthy and 257 glaucomatous eyes were used to build the diagnostic models. Three kinds of composite variables derived from the Garway-Heath map and the glaucoma hemifield test (GHT) sector map were included in the input variables in addition to the 52 SAP visual filed locations. Dimensionality reduction was conducted to select important variables so as to alleviate high-dimensionality problems. To validate the proposed methods, we applied four classifiers-linear discriminant analysis, naïve Bayes classifier, support vector machines, and artificial neural networks-and four dimensionality reduction methods-Pearson correlation coefficient-based variable selection, Markov blanket variable selection, the minimum redundancy maximum relevance algorithm, and principal component analysis- and compared their classification performances.

RESULTS

For all tested combinations, the classification performance improved when the proposed composite variables and dimensionality reduction techniques were implemented. The combination of total deviation values, the GHT sector map, support vector machines, and Markov blanket variable selection obtains the best performance: an area under the receiver operating characteristic curve (AUC) of 0.912.

CONCLUSION

A glaucoma diagnosis model giving an AUC of 0.912 was constructed by applying machine learning techniques to SAP data. The results show that dimensionality reduction not only reduces dimensions of the input space but also enhances the classification performance. The variable selection results show that the proposed composite variables from visual field clustering play a key role in the diagnosis model.

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.

Applications of Artificial Intelligence in Ophthalmology: General Overview

With the emergence of unmanned plane, autonomous vehicles, face recognition, and language processing, the artificial intelligence (AI) has remarkably revolutionized our lifestyle. Recent studies indicate that AI has astounding potential to perform much better than human beings in some tasks, especially in the image recognition field. As the amount of image data in imaging center of ophthalmology is increasing dramatically, analyzing and processing these data is in urgent need. AI has been tried to apply to decipher medical data and has made extraordinary progress in intelligent diagnosis. In this paper, we presented the basic workflow for building an AI model and systematically reviewed applications of AI in the diagnosis of eye diseases. Future work should focus on setting up systematic AI platforms to diagnose general eye diseases based on multimodal data in the real world.

The Relationship between the Waveform Parameters from the Ocular Response Analyzer and the Progression of Glaucoma

PURPOSE

To investigate the usefulness of waveform parameters measured with the Ocular Response Analyzer (Reichert Ophthalmic Instruments, Depew, NY) in assessing the progression of glaucomatous visual field (VF).

DESIGN

Observational cross-sectional study.

PARTICIPANTS

One hundred and one eyes with primary open-angle glaucoma in 68 patients with 8 reliable VFs using the Humphrey Field Analyzer (Carl Zeiss Meditec, Inc., Dublin, CA).

METHODS

The mean of total deviation (mTD) value of the 52 test points in the 24-2 Humphrey Field Analyzer VF test pattern was calculated, and the progression rate of mTD was determined using 8 VFs. Ocular Response Analyzer measurement was performed 3 times in the same day, and the average values of the 3 measurements were used in the analysis. Then, the optimal linear mixed model was selected using 7 parameters: age, mean and standard deviation of intraocular pressure with the Goldmann applanation tonometry during the observation period, central corneal thickness, axial length, mTD in the initial VF, and corneal hysteresis (CH) other than waveform parameters, henceforth known as the basic model. In addition, using the 37 waveform parameters, the optimal model for the mTD progression rate was identified, according to the second-order bias-corrected Akaike information criterion (AICc) index, using 15 preselected waveform parameters with the least absolute shrinkage and selection operator regression (henceforth known as the waveform model).

MAIN OUTCOME MEASURES

Optimal linear mixed models for the mTD progression rate, as determined by AICc index.

RESULTS

The mean ± standard deviation mTD progression rate was -0.25±0.31 dB/year. The basic model was mTD progression rate = -0.94 + 0.075 × CH (AICc = 46.71). The waveform model was mTD progression rate = 1.25 - 0.066 × path2 - 0.000099 × p2area + 0.0021 × mslew2 (AICc = 44.95). The relative likelihood of the latter model being the optimal model was 6.23 times greater than that of the former model.

CONCLUSIONS

Ocular Response Analyzer waveform parameters were correlated significantly with glaucomatous VF progression and showed a stronger than correlation with VF progression than CH.

Hybrid Deep Learning on Single Wide-field Optical Coherence tomography Scans Accurately Classifies Glaucoma Suspects

PURPOSE

Existing summary statistics based upon optical coherence tomographic (OCT) scans and/or visual fields (VFs) are suboptimal for distinguishing between healthy and glaucomatous eyes in the clinic. This study evaluates the extent to which a hybrid deep learning method (HDLM), combined with a single wide-field OCT protocol, can distinguish eyes previously classified as either healthy suspects or mild glaucoma.

METHODS

In total, 102 eyes from 102 patients, with or suspected open-angle glaucoma, had previously been classified by 2 glaucoma experts as either glaucomatous (57 eyes) or healthy/suspects (45 eyes). The HDLM had access only to information from a single, wide-field (9×12 mm) swept-source OCT scan per patient. Convolutional neural networks were used to extract rich features from maps derived from these scans. Random forest classifier was used to train a model based on these features to predict the existence of glaucomatous damage. The algorithm was compared against traditional OCT and VF metrics.

RESULTS

The accuracy of the HDLM ranged from 63.7% to 93.1% depending upon the input map. The retinal nerve fiber layer probability map had the best accuracy (93.1%), with 4 false positives, and 3 false negatives. In comparison, the accuracy of the OCT and 24-2 and 10-2 VF metrics ranged from 66.7% to 87.3%. The OCT quadrants analysis had the best accuracy (87.3%) of the metrics, with 4 false positives and 9 false negatives.

CONCLUSIONS

The HDLM protocol outperforms standard OCT and VF clinical metrics in distinguishing healthy suspect eyes from eyes with early glaucoma. It should be possible to further improve this algorithm and with improvement it might be useful for screening.

The association between ocular surface measurements with visual field reliability indices and gaze tracking results in preperimetric glaucoma

Background/aims

To investigate the relationship between gaze tracking (GT) results and ocular surface condition in glaucoma.

Method

The Humphrey 24–2 visual field (VF) was measured in 34 eyes of 30 patients with open-angle glaucoma without VF damage. Tear break-up time, Schirmer’s test, tear meniscus volume (TMV) and presence of superficial punctate keratopathy (SPK) were also measured in order to describe the condition of the ocular surface. Various GT parameters were calculated: the average frequency of eye movements per stimulus between 1° and 2° (move1-2), the average frequency of eye movements per stimulus between 3° and 5° (move3-5), the average frequency of eye movements per stimulus more than 6° (move≥6), the average tracking failure frequency per stimulus (TFF) and the average blinking frequency. The relationship between GT parameters, reliability indices and ocular surface measurements was investigated using linear mixed modelling.

Results

SPK was positively associated with high rates of move3-5 (coefficient=0.12 for SPK+, p=0.003) and move≥6 (coefficient=0.052 for SPK+, p=0.023). High TMV was significantly related to TFF (coefficient=0.37, p=0.023). Fixation losses, false-positives and false-negatives were not significantly associated with any GT parameters or ocular surface measurements.

Conclusion

SPK is associated with increased frequency of eye movements (move3-5 and move≥6). In addition, large TMV is associated with increased rate of TFF. Careful attention should be paid when interpreting GT parameters in patients with SPK or a large TMV.

The value of visual field testing in the era of advanced imaging: clinical and psychophysical perspectives

White‐on‐white standard automated perimetry (SAP) is widely used in clinical and research settings for assessment of contrast sensitivity using incremental light stimuli across the visual field. It is one of the main functional measures of the effect of disease upon the visual system. SAP has evolved over the last 40 years to become an indispensable tool for comprehensive assessment of visual function. In modern clinical practice, a range of objective measurements of ocular structure, such as optical coherence tomography, have also become invaluable additions to the arsenal of the ophthalmic examination. Although structure‐function correlation is a highly desirable determinant of an unambiguous clinical picture for a patient, in practice, clinicians are often faced with discordance of structural and functional results, which presents them with a challenge. The construction principles behind the development of SAP are used to discuss the interpretation of visual fields, as well as the problem of structure‐function discordance. Through illustrative clinical examples, we provide useful insights to assist clinicians in combining a range of clinical results obtained from SAP and from advanced imaging techniques into a coherent picture that can help direct clinical management.

Detecting Preperimetric Glaucoma with Standard Automated Perimetry Using a Deep Learning Classifier

PURPOSE

To differentiate the visual fields (VFs) of preperimetric open-angle glaucoma (OAG) patients from the VFs of healthy eyes using a deep learning (DL) method.

DESIGN

Cohort study.

PARTICIPANTS

One hundred seventy-one preperimetric glaucoma VFs (PPGVFs) from 53 eyes in 51 OAG patients and 108 healthy eyes of 87 healthy participants.

METHODS

Preperimetric glaucoma VFs were defined as all VFs before a first diagnosis of manifest glaucoma (Anderson-Patella's criteria). In total, 171 PPGVFs from 53 eyes in 51 OAG patients and 108 VFs from 108 healthy eyes in 87 healthy participants were analyzed (all VFs were tested using the Humphrey Field Analyzer 30-2 program; Carl Zeiss Meditec, Dublin, CA). The 52 total deviation, mean deviation, and pattern standard deviation values were used as predictors in the DL classifier: a deep feed-forward neural network (FNN), along with other machine learning (ML) methods, including random forests (RF), gradient boosting, support vector machine, and neural network (NN). The area under the receiver operating characteristic curve (AUC) was used to evaluate the accuracy of discrimination for each method.

MAIN OUTCOME MEASURES

The AUCs obtained with each classifier method.

RESULTS

A significantly larger AUC of 92.6% (95% confidence interval [CI], 89.8%-95.4%) was obtained using the deep FNN classifier compared with all other ML methods: 79.0% (95% CI, 73.5%-84.5%) with RF, 77.6% (95% CI, 71.7%-83.5%) with gradient boosting, 71.2% (95% CI, 65.0%-77.5%), and 66.7% (95% CI, 60.1%-73.3%) with NN.

CONCLUSIONS

Preperimetric glaucoma VFs can be distinguished from healthy VFs with very high accuracy using a deep FNN classifier.

Machine Learning Techniques in Clinical Vision Sciences

This review presents and discusses the contribution of machine learning techniques for diagnosis and disease monitoring in the context of clinical vision science. Many ocular diseases leading to blindness can be halted or delayed when detected and treated at its earliest stages. With the recent developments in diagnostic devices, imaging and genomics, new sources of data for early disease detection and patients' management are now available. Machine learning techniques emerged in the biomedical sciences as clinical decision-support techniques to improve sensitivity and specificity of disease detection and monitoring, increasing objectively the clinical decision-making process. This manuscript presents a review in multimodal ocular disease diagnosis and monitoring based on machine learning approaches. In the first section, the technical issues related to the different machine learning approaches will be present. Machine learning techniques are used to automatically recognize complex patterns in a given dataset. These techniques allows creating homogeneous groups (unsupervised learning), or creating a classifier predicting group membership of new cases (supervised learning), when a group label is available for each case. To ensure a good performance of the machine learning techniques in a given dataset, all possible sources of bias should be removed or minimized. For that, the representativeness of the input dataset for the true population should be confirmed, the noise should be removed, the missing data should be treated and the data dimensionally (i.e., the number of parameters/features and the number of cases in the dataset) should be adjusted. The application of machine learning techniques in ocular disease diagnosis and monitoring will be presented and discussed in the second section of this manuscript. To show the clinical benefits of machine learning in clinical vision sciences, several examples will be presented in glaucoma, age-related macular degeneration, and diabetic retinopathy, these ocular pathologies being the major causes of irreversible visual impairment.