Machine Learning-Based Predictive Modeling of Surgical Intervention in Glaucoma Using Systemic Data From Electronic Health Records

Multimodal Artificial Intelligence Models Predicting Glaucoma Progression Using Electronic Health Records and Retinal Nerve Fiber Layer Scans

Purpose

The purpose of this study was to develop models that predict which patients with glaucoma will progress to require surgery, combining structured data from electronic health records (EHRs) and retinal fiber layer optical coherence tomography (RNFL OCT) scans.

Methods

EHR data (demographics and clinical eye examinations) and RNFL OCT scans were identified for patients with glaucoma from an academic center (2008-2023). Comparing the novel TabNet deep learning architecture to a baseline XGBoost model, we trained and evaluated single modality models using either EHR or RNFL features, as well as fusion models combining both EHR and RNFL features as inputs, to predict glaucoma surgery within 12 months (binary).

Results

We had 1472 patients with glaucoma who were included in this study, of which 29.9% (N = 367) progressed to glaucoma surgery. The TabNet fusion model achieved the highest performance on the test set with an area under the receiver operating characteristic curve (AUROC) of 0.832, compared to the XGBoost fusion model (AUROC = 0.747). EHR only models performed with an AUROC of 0.764 and 0.720 for the deep learning model and XGBoost models, respectively. RNFL only models performed with an AUROC of 0.624 and 0.633 for the deep learning and XGBoost models, respectively.

Conclusions

Fusion models which integrate both RNFL with EHR data outperform models only utilizing one datatype or the other to predict glaucoma progression. The deep learning TabNet architecture demonstrated superior performance to traditional XGBoost models.

Translational Relevance

Prediction models that utilize the wealth of structured clinical and imaging data to predict glaucoma progression could form the basis of future clinical decision support tools to personalize glaucoma care.

Machine-learning random forest algorithms predict post-cycloplegic myopic corrections from noncycloplegic clinical data

SIGNIFICANCE

Machine learning random forest algorithms were used to predict objective refractive outcomes after cycloplegic refraction using noncycloplegic clinical data. A classification model predicted post-cycloplegic myopia and could be useful in screening, and a second regression model predicted post-cycloplegic refractive and could provide a useful objective starting point in noncycloplegic subjective refractions.

PURPOSE

A classification model sought to predict post-cycloplegic myopia using noncycloplegic clinical data to enhance myopia screening accuracy, whereas the regression model looked to predict objective refraction outcomes after cycloplegia for use as a starting point for noncycloplegic subjective refraction.

METHODS

A cross-sectional study included data from 2483 eyes. Pre-refraction measurements, such as uncorrected visual acuity, axial length, and corneal curvature radius, were recorded. After cycloplegia, the spherical equivalent was measured. Random forest-based classification and regression models were established with input variables including age, gender, axial length, corneal curvature radius, axial length-to-corneal curvature radius ratio, spherical equivalent, and uncorrected visual acuity. Model performance was assessed using various metrics.

RESULTS

The random forest classification model achieved high out-of-bag validation accuracy (92%), cross-validation accuracy (93%), external validation accuracy (94%), and precision (95%). The external validation sensitivity was 93%, and specificity was 95%. The regression model internal validation showed an out-of-bag validation R2 of 0.86, root mean square error (RMSE) of 0.66, and mean absolute error of 0.49. The 10-fold cross-validation R2 was 0.87, the RMSE was 0.64, and the mean absolute error was 0.48. In the external validation, R2 was 0.88, the RMSE was 0.63, and the mean absolute error was 0.48.

CONCLUSIONS

By analyzing noncycloplegic clinical data, the classification model enables earlier detection of myopia, supporting timely intervention and management. The regression model aims to accurately predict post-cycloplegia myopic corrections, providing reliable initial data for subjective refraction. This could help optometrists perform noncycloplegic subjective refraction more efficiently and is particularly relevant in China, where retinoscopy is not yet fully popularized and many school students decline cycloplegic refraction due to academic pressures and limited free time, primarily because it requires a follow-up the next day.

Predicting positive Clostridioides difficile test results using large-scale longitudinal data of demographics and medication history

Background

Clostridioides difficile infection is a major health threat. Healthcare institutions have strong medical and financial incentives to keep infections under control. Blanket testing at admission is in general not recommended, and current predictive models either used moderate sample sizes, over-inflated the number of covariates, or chose non-interpretable algorithms. We aim to develop models using patient data to predict positive Clostridioides difficile test results with discrimination performance, interpretable results, and a reasonable number of covariates that reflect health over a long-time span.

Materials and methods

We processed records from 157,493 University of California San Diego Health patients seen between January 01, 2016-July 03, 2019 with at least 6 months of medication history, excluding pregnant women, patients under 18, and prisoners. Three models (Logistic Regression, Random Forest, and Ensemble) were constructed using hyper-parameters selected through 10-fold cross-validation. Model performance was measured by the Area Under the Receiver Operating Characteristic Curve (AUROC). The model coefficients' odds ratios and p-values were calculated for the Logistic Regression model, as were Gini indices for Random Forest. Decision boundary analysis was conducted using pair-wise false positive and false negative cases each model would predict at a specific threshold.

Results

Logistic Regression, Random Forest, and Ensemble models yielded test AUROCs of 0.839, 0.851, and 0.866, respectively. Significant covariates that may affect risk include age, immuno-compromised treatments, past antibiotic uses, and some medications for the gastrointestinal tract.

Conclusions

The models achieve high discrimination performance (AUROC >0.83). There is a general consensus among different analysis approaches regarding predictors that impact patients' chances of having a positive test, which may influence Clostridioides difficile risk, including features clinically proven to increase susceptibility. These human-interpretable models can help distinguish significant predictors that affect a patient's chance of testing positive, which may influence their Clostridioides difficile risk.

The Impact of Race, Ethnicity, and Sex on Fairness in Artificial Intelligence for Glaucoma Prediction Models

Objective

Despite advances in artificial intelligence (AI) in glaucoma prediction, most works lack multicenter focus and do not consider fairness concerning sex, race, or ethnicity. This study aims to examine the impact of these sensitive attributes on developing fair AI models that predict glaucoma progression to necessitating incisional glaucoma surgery.

Design

Database study.

Participants

Thirty-nine thousand ninety patients with glaucoma, as identified by International Classification of Disease codes from 7 academic eye centers participating in the Sight OUtcomes Research Collaborative.

Methods

We developed XGBoost models using 3 approaches: (1) excluding sensitive attributes as input features, (2) including them explicitly as input features, and (3) training separate models for each group. Model input features included demographic details, diagnosis codes, medications, and clinical information (intraocular pressure, visual acuity, etc.), from electronic health records. The models were trained on patients from 5 sites (N = 27 999) and evaluated on a held-out internal test set (N = 3499) and 2 external test sets consisting of N = 1550 and N = 2542 patients.

Main Outcomes and Measures

Area under the receiver operating characteristic curve (AUROC) and equalized odds on the test set and external sites.

Results

Six thousand six hundred eighty-two (17.1%) of 39 090 patients underwent glaucoma surgery with a mean age of 70.1 (standard deviation 14.6) years, 54.5% female, 62.3% White, 22.1% Black, and 4.7% Latinx/Hispanic. We found that not including the sensitive attributes led to better classification performance (AUROC: 0.77-0.82) but worsened fairness when evaluated on the internal test set. However, on external test sites, the opposite was true: including sensitive attributes resulted in better classification performance (AUROC: external #1 - [0.73-0.81], external #2 - [0.67-0.70]), but varying degrees of fairness for sex and race as measured by equalized odds.

Conclusions

Artificial intelligence models predicting whether patients with glaucoma progress to surgery demonstrated bias with respect to sex, race, and ethnicity. The effect of sensitive attribute inclusion and exclusion on fairness and performance varied based on internal versus external test sets. Prior to deployment, AI models should be evaluated for fairness on the target population.

Financial Disclosures

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Federated Learning in Glaucoma: A Comprehensive Review and Future Perspectives

CLINICAL RELEVANCE

Glaucoma is a complex eye condition with varied morphological and clinical presentations, making diagnosis and management challenging. The lack of a consensus definition for glaucoma or glaucomatous optic neuropathy further complicates the development of universal diagnostic tools. Developing robust artificial intelligence (AI) models for glaucoma screening is essential for early detection and treatment but faces significant obstacles. Effective deep learning algorithms require large, well-curated datasets from diverse patient populations and imaging protocols. However, creating centralized data repositories is hindered by concerns over data sharing, patient privacy, regulatory compliance, and intellectual property. Federated Learning (FL) offers a potential solution by enabling data to remain locally hosted while facilitating distributed model training across multiple sites.

METHODS

A comprehensive literature review was conducted on the application of Federated Learning in training AI models for glaucoma screening. Publications from 1950 to 2024 were searched using databases such as PubMed and IEEE Xplore with keywords including "glaucoma," "federated learning," "artificial intelligence," "deep learning," "machine learning," "distributed learning," "privacy-preserving," "data sharing," "medical imaging," and "ophthalmology." Articles were included if they discussed the use of FL in glaucoma-related AI tasks or addressed data sharing and privacy challenges in ophthalmic AI development.

RESULTS

FL enables collaborative model development without centralizing sensitive patient data, addressing privacy and regulatory concerns. Studies show that FL can improve model performance and generalizability by leveraging diverse datasets while maintaining data security. FL models have achieved comparable or superior accuracy to those trained on centralized data, demonstrating effectiveness in real-world clinical settings.

CONCLUSIONS

Federated Learning presents a promising strategy to overcome current obstacles in developing AI models for glaucoma screening. By balancing the need for extensive, diverse training data with the imperative to protect patient privacy and comply with regulations, FL facilitates collaborative model training without compromising data security. This approach offers a pathway toward more accurate and generalizable AI solutions for glaucoma detection and management.

FINANCIAL DISCLOSURE(S)

Proprietary or commercial disclosure may be found after the references in the Footnotes and Disclosures at the end of this article.

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.

Novel Approaches for the Early Detection of Glaucoma Using Artificial Intelligence

BACKGROUND

If left untreated, glaucoma-the second most common cause of blindness worldwide-causes irreversible visual loss due to a gradual neurodegeneration of the retinal ganglion cells. Conventional techniques for identifying glaucoma, like optical coherence tomography (OCT) and visual field exams, are frequently laborious and dependent on subjective interpretation. Through the fast and accurate analysis of massive amounts of imaging data, artificial intelligence (AI), in particular machine learning (ML) and deep learning (DL), has emerged as a promising method to improve the early detection and management of glaucoma.

AIMS

The purpose of this study is to examine the current uses of AI in the early diagnosis, treatment, and detection of glaucoma while highlighting the advantages and drawbacks of different AI models and algorithms. In addition, it aims to determine how AI technologies might transform glaucoma treatment and suggest future lines of inquiry for this area of study.

METHODS

A thorough search of databases, including Web of Science, PubMed, and Scopus, was carried out to find pertinent papers released until August 2024. The inclusion criteria were limited to research published in English in peer-reviewed publications that used AI, ML, or DL to diagnose or treat glaucoma in human subjects. Articles were chosen and vetted according to their quality, contribution to the field, and relevancy.

RESULTS

Convolutional neural networks (CNNs) and other deep learning algorithms are among the AI models included in this paper that have been shown to have excellent sensitivity and specificity in identifying glaucomatous alterations in fundus photos, OCT scans, and visual field tests. By automating standard screening procedures, these models have demonstrated promise in distinguishing between glaucomatous and healthy eyes, forecasting the course of the disease, and possibly lessening the workload of physicians. Nonetheless, several significant obstacles remain, such as the requirement for various training datasets, outside validation, decision-making transparency, and handling moral and legal issues.

CONCLUSIONS

Artificial intelligence (AI) holds great promise for improving the diagnosis and treatment of glaucoma by facilitating prompt and precise interpretation of imaging data and assisting in clinical decision making. To guarantee wider accessibility and better patient results, future research should create strong generalizable AI models validated in various populations, address ethical and legal matters, and incorporate AI into clinical practice.

Novel Technologies in Artificial Intelligence and Telemedicine for Glaucoma Screening

PURPOSE

To provide an overview of novel technologies in telemedicine and artificial intelligence (AI) approaches for cost-effective glaucoma screening.

METHODS/RESULTS

A narrative review was performed by summarizing research results, recent developments in glaucoma detection and care, and considerations related to telemedicine and AI in glaucoma screening. Telemedicine and AI approaches provide the opportunity for novel glaucoma screening programs in primary care, optometry, portable, and home-based settings. These approaches offer several advantages for glaucoma screening, including increasing access to care, lowering costs, identifying patients in need of urgent treatment, and enabling timely diagnosis and early intervention. However, challenges remain in implementing these systems, including integration into existing clinical workflows, ensuring equity for patients, and meeting ethical and regulatory requirements. Leveraging recent work towards standardized data acquisition as well as tools and techniques developed for automated diabetic retinopathy screening programs may provide a model for a cost-effective approach to glaucoma screening.

CONCLUSION

Leveraging novel technologies and advances in telemedicine and AI-based approaches to glaucoma detection show promise for improving our ability to detect moderate and advanced glaucoma in primary care settings and target higher individuals at high risk for having the disease.

Big data to guide glaucoma treatment

Ophthalmology has been at the forefront of the medical application of big data. Often harnessed with a machine learning approach, big data has demonstrated potential to transform ophthalmic care, as evidenced by prior success on clinical tasks such as the screening of ophthalmic diseases and lesions via retinal images. With the recent establishment of various large ophthalmic datasets, there has been greater interest in determining whether the benefits of big data may extend to the downstream process of ophthalmic disease management. An area of substantial investigation has been the use of big data to help guide or streamline management of glaucoma, which remains a leading cause of irreversible blindness worldwide. In this review, we summarize relevant studies utilizing big data and discuss the application of the findings in the risk assessment and treatment of glaucoma.

Big data in visual field testing for glaucoma

Recent technological advancements and the advent of ever-growing databases in health care have fueled the emergence of "big data" analytics. Big data has the potential to revolutionize health care, particularly ophthalmology, given the data-intensive nature of the medical specialty. As one of the leading causes of irreversible blindness worldwide, glaucoma is an ocular disease that receives significant interest for developing innovations in eye care. Among the most vital sources of data in glaucoma is visual field (VF) testing, which stands as a cornerstone for diagnosing and managing the disease. The expanding accessibility of large VF databases has led to a surge in studies investigating various applications of big data analytics in glaucoma. In this study, we review the use of big data for evaluating the reliability of VF tests, gaining insights into real-world clinical practices and outcomes, understanding new disease associations and risk factors, characterizing the patterns of VF loss, defining the structure-function relationship of glaucoma, enhancing early diagnosis or earlier detection of progression, informing clinical decisions, and improving clinical trials. Equally important, we discuss current challenges in big data analytics and future directions for improvement.

Application of artificial intelligence in glaucoma care: An updated review

The application of artificial intelligence (AI) in ophthalmology has been increasingly explored in the past decade. Numerous studies have shown promising results supporting the utility of AI to improve the management of ophthalmic diseases, and glaucoma is of no exception. Glaucoma is an irreversible vision condition with insidious onset, complex pathophysiology, and chronic treatment. Since there remain various challenges in the clinical management of glaucoma, the potential role of AI in facilitating glaucoma care has garnered significant attention. In this study, we reviewed the relevant literature published in recent years that investigated the application of AI in glaucoma management. The main aspects of AI applications that will be discussed include glaucoma risk prediction, glaucoma detection and diagnosis, visual field estimation and pattern analysis, glaucoma progression detection, and other applications.

Integrating Deep Learning with Electronic Health Records for Early Glaucoma Detection: A Multi-Dimensional Machine Learning Approach

BACKGROUND

Recent advancements in deep learning have significantly impacted ophthalmology, especially in glaucoma, a leading cause of irreversible blindness worldwide. In this study, we developed a reliable predictive model for glaucoma detection using deep learning models based on clinical data, social and behavior risk factor, and demographic data from 1652 participants, split evenly between 826 control subjects and 826 glaucoma patients.

METHODS

We extracted structural data from control and glaucoma patients' electronic health records (EHR). Three distinct machine learning classifiers, the Random Forest and Gradient Boosting algorithms, as well as the Sequential model from the Keras library of TensorFlow, were employed to conduct predictive analyses across our dataset. Key performance metrics such as accuracy, F1 score, precision, recall, and the area under the receiver operating characteristics curve (AUC) were computed to both train and optimize these models.

RESULTS

The Random Forest model achieved an accuracy of 67.5%, with a ROC AUC of 0.67, outperforming the Gradient Boosting and Sequential models, which registered accuracies of 66.3% and 64.5%, respectively. Our results highlighted key predictive factors such as intraocular pressure, family history, and body mass index, substantiating their roles in glaucoma risk assessment.

CONCLUSIONS

This study demonstrates the potential of utilizing readily available clinical, lifestyle, and demographic data from EHRs for glaucoma detection through deep learning models. While our model, using EHR data alone, has a lower accuracy compared to those incorporating imaging data, it still offers a promising avenue for early glaucoma risk assessment in primary care settings. The observed disparities in model performance and feature significance show the importance of tailoring detection strategies to individual patient characteristics, potentially leading to more effective and personalized glaucoma screening and intervention.

Prediction Models for Glaucoma in a Multicenter Electronic Health Records Consortium: The Sight Outcomes Research Collaborative

Purpose

Advances in artificial intelligence have enabled the development of predictive models for glaucoma. However, most work is single-center and uncertainty exists regarding the generalizability of such models. The purpose of this study was to build and evaluate machine learning (ML) approaches to predict glaucoma progression requiring surgery using data from a large multicenter consortium of electronic health records (EHR).

Design

Cohort study.

Participants

Thirty-six thousand five hundred forty-eight patients with glaucoma, as identified by International Classification of Diseases (ICD) codes from 6 academic eye centers participating in the Sight OUtcomes Research Collaborative (SOURCE).

Methods

We developed ML models to predict whether patients with glaucoma would progress to glaucoma surgery in the coming year (identified by Current Procedural Terminology codes) using the following modeling approaches: (1) penalized logistic regression (lasso, ridge, and elastic net); (2) tree-based models (random forest, gradient boosted machines, and XGBoost), and (3) deep learning models. Model input features included demographics, diagnosis codes, medications, and clinical information (intraocular pressure, visual acuity, refractive status, and central corneal thickness) available from structured EHR data. One site was reserved as an "external site" test set (N = 1550); of the patients from the remaining sites, 10% each were randomly selected to be in development and test sets, with the remaining 27 999 reserved for model training.

Main Outcome Measures

Evaluation metrics included area under the receiver operating characteristic curve (AUROC) on the test set and the external site.

Results

Six thousand nineteen (16.5%) of 36 548 patients underwent glaucoma surgery. Overall, the AUROC ranged from 0.735 to 0.771 on the random test set and from 0.706 to 0.754 on the external test site, with the XGBoost and random forest model performing best, respectively. There was greatest performance decrease from the random test set to the external test site for the penalized regression models.

Conclusions

Machine learning models developed using structured EHR data can reasonably predict whether glaucoma patients will need surgery, with reasonable generalizability to an external site. Additional research is needed to investigate the impact of protected class characteristics such as race or gender on model performance and fairness.

Financial Disclosures

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Lessons From The Glaucoma Foundation Think Tank 2023: A Patient-Centric Approach to Glaucoma

PRCIS

The main takeaways also included that BIG DATA repositories and AI are important combinatory tools to foster novel strategies to prevent and stabilize glaucoma and, in the future, recover vision loss from the disease.

PURPOSE

To summarize the main topics discussed during the 28th Annual Glaucoma Foundation Think Tank Meeting "A Patient-Centric Approach to Glaucoma" held in New York on June 9 and 10, 2023.

METHODS

The highlights of the sessions on BIG DATA, genetics, modifiable lifestyle risk factors, female sex hormones, and neuroprotection in the field of primary open angle glaucoma (POAG) were summarized.

RESULTS

The researchers discussed the importance of BIG DATA repositories available at national and international levels for POAG research, including the United Kingdom Biobank. Combining genotyped large cohorts worldwide, facilitated by artificial intelligence (AI) and machine-learning approaches, led to the milestone discovery of 312 genome-wide significant disease loci for POAG. While these loci could be combined into a polygenic risk score with clinical utility, Think Tank meeting participants also provided analytical epidemiological evidence that behavioral risk factors modify POAG polygenetic risk, citing specific examples related to caffeine and alcohol use. The impact of female sex hormones on POAG pathophysiology was discussed, as was neuroprotection and the potential use of AI to help mitigate specific challenges faced in clinical trials and speed approval of neuroprotective agents.

CONCLUSIONS

The experts agreed on the importance of genetics in defining individual POAG risk and highlighted the additional crucial role of lifestyle, gender, blood pressure, and vascular risk factors. The main takeaways also included that BIG DATA repositories and AI are important combinatory tools to foster novel strategies to prevent and stabilize glaucoma and, in the future, recover vision loss from the disease.

Proactive Decision Support for Glaucoma Treatment: Predicting Surgical Interventions with Clinically Available Data

A longitudinal ophthalmic dataset was used to investigate multi-modal machine learning (ML) models incorporating patient demographics and history, clinical measurements, optical coherence tomography (OCT), and visual field (VF) testing in predicting glaucoma surgical interventions. The cohort included 369 patients who underwent glaucoma surgery and 592 patients who did not undergo surgery. The data types used for prediction included patient demographics, history of systemic conditions, medication history, ophthalmic measurements, 24-2 VF results, and thickness measurements from OCT imaging. The ML models were trained to predict surgical interventions and evaluated on independent data collected at a separate study site. The models were evaluated based on their ability to predict surgeries at varying lengths of time prior to surgical intervention. The highest performing predictions achieved an AUC of 0.93, 0.92, and 0.93 in predicting surgical intervention at 1 year, 2 years, and 3 years, respectively. The models were also able to achieve high sensitivity (0.89, 0.77, 0.86 at 1, 2, and 3 years, respectively) and specificity (0.85, 0.90, and 0.91 at 1, 2, and 3 years, respectively) at an 0.80 level of precision. The multi-modal models trained on a combination of data types predicted surgical interventions with high accuracy up to three years prior to surgery and could provide an important tool to predict the need for glaucoma intervention.

Deep learning-based identification of eyes at risk for glaucoma surgery

To develop and evaluate the performance of a deep learning model (DLM) that predicts eyes at high risk of surgical intervention for uncontrolled glaucoma based on multimodal data from an initial ophthalmology visit. Longitudinal, observational, retrospective study. 4898 unique eyes from 4038 adult glaucoma or glaucoma-suspect patients who underwent surgery for uncontrolled glaucoma (trabeculectomy, tube shunt, xen, or diode surgery) between 2013 and 2021, or did not undergo glaucoma surgery but had 3 or more ophthalmology visits. We constructed a DLM to predict the occurrence of glaucoma surgery within various time horizons from a baseline visit. Model inputs included spatially oriented visual field (VF) and optical coherence tomography (OCT) data as well as clinical and demographic features. Separate DLMs with the same architecture were trained to predict the occurrence of surgery within 3 months, within 3-6 months, within 6 months-1 year, within 1-2 years, within 2-3 years, within 3-4 years, and within 4-5 years from the baseline visit. Included eyes were randomly split into 60%, 20%, and 20% for training, validation, and testing. DLM performance was measured using area under the receiver operating characteristic curve (AUC) and precision-recall curve (PRC). Shapley additive explanations (SHAP) were utilized to assess the importance of different features. Model prediction of surgery for uncontrolled glaucoma within 3 months had the best AUC of 0.92 (95% CI 0.88, 0.96). DLMs achieved clinically useful AUC values (> 0.8) for all models that predicted the occurrence of surgery within 3 years. According to SHAP analysis, all 7 models placed intraocular pressure (IOP) within the five most important features in predicting the occurrence of glaucoma surgery. Mean deviation (MD) and average retinal nerve fiber layer (RNFL) thickness were listed among the top 5 most important features by 6 of the 7 models. DLMs can successfully identify eyes requiring surgery for uncontrolled glaucoma within specific time horizons. Predictive performance decreases as the time horizon for forecasting surgery increases. Implementing prediction models in a clinical setting may help identify patients that should be referred to a glaucoma specialist for surgical evaluation.

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.

Predicting Glaucoma Progression to Surgery with Artificial Intelligence Survival Models

Purpose

Prior artificial intelligence (AI) models for predicting glaucoma progression have used traditional classifiers that do not consider the longitudinal nature of patients' follow-up. In this study, we developed survival-based AI models for predicting glaucoma patients' progression to surgery, comparing performance of regression-, tree-, and deep learning-based approaches.

Design

Retrospective observational study.

Subjects

Patients with glaucoma seen at a single academic center from 2008 to 2020 identified from electronic health records (EHRs).

Methods

From the EHRs, we identified 361 baseline features, including demographics, eye examinations, diagnoses, and medications. We trained AI survival models to predict patients' progression to glaucoma surgery using the following: (1) a penalized Cox proportional hazards (CPH) model with principal component analysis (PCA); (2) random survival forests (RSFs); (3) gradient-boosting survival (GBS); and (4) a deep learning model (DeepSurv). The concordance index (C-index) and mean cumulative/dynamic area under the curve (mean AUC) were used to evaluate model performance on a held-out test set. Explainability was investigated using Shapley values for feature importance and visualization of model-predicted cumulative hazard curves for patients with different treatment trajectories.

Main Outcome Measures

Progression to glaucoma surgery.

Results

Of the 4512 patients with glaucoma, 748 underwent glaucoma surgery, with a median follow-up of 1038 days. The DeepSurv model performed best overall (C-index, 0.775; mean AUC, 0.802) among the models studied in this article (CPH with PCA: C-index, 0.745; mean AUC, 0.780; RSF: C-index, 0.766; mean AUC, 0.804; GBS: C-index, 0.764; mean AUC, 0.791). Predicted cumulative hazard curves demonstrate how models could distinguish between patient who underwent early surgery and patients who underwent surgery after > 3000 days of follow-up or no surgery.

Conclusions

Artificial intelligence survival models can predict progression to glaucoma surgery using structured data from EHRs. Tree-based and deep learning-based models performed better at predicting glaucoma progression to surgery than the CPH regression model, potentially because of their better suitability for high-dimensional data sets. Future work predicting ophthalmic outcomes should consider using tree-based and deep learning-based survival AI models. Additional research is needed to develop and evaluate more sophisticated deep learning survival models that can incorporate clinical notes or imaging.

Financial Disclosures

Proprietary or commercial disclosure may be found after the references.

A machine learning approach to predict the glaucoma filtration surgery outcome

This study aimed at predicting the filtration surgery (FS) outcome using a machine learning (ML) approach. 102 glaucomatous patients undergoing FS were enrolled and underwent ocular surface clinical tests (OSCTs), determination of surgical site-related biometric parameters (SSPs) and conjunctival vascularization. Break-up-time, Schirmer test I, corneal fluorescein staining, Meibomian gland expressibility; conjunctival hyperemia, upper bulbar conjunctiva area of exposure, limbus to superior eyelid distance; and conjunctival epithelial and stromal (CET, CST) thickness and reflectivity (ECR, SCR) at AS-OCT were considered. Successful FS required a 30% baseline intraocular pressure reduction, with values ≤ 18 mmHg with or without medications. The classification tree (CT) was the ML algorithm used to analyze data. At the twelfth month, FS was successful in 60.8% of cases, whereas failed in 39.2%. At the variable importance ranking, CST and SCR were the predictors with the greater relative importance to the CART tree construction, followed by age. CET and ECR showed less relative importance, whereas OSCTs and SSPs were not important features. Within the CT, CST turned out the most important variable for discriminating success from failure, followed by SCR and age, with cut-off values of 75 µm, 169 on gray scale, and 62 years, respectively. The ROC curve for the classifier showed an AUC of 0.784 (0.692-0.860). In this ML approach, CT analysis found that conjunctival stroma thickness and reflectivity, along with age, can predict the FS outcome with good accuracy. A pre-operative thick and hyper-reflective stroma, and a younger age increase the risk of FS failure.

Steganography for medical record image

Medical record images in EHR system are users' privacy and an asset, and there is an urgent need to protect this data. Image steganography can offer a potential solution. A steganographic model for medical record images is therefore developed based on StegaStamp. In contrast to natural images, medical record images are document images, which can be very vulnerable to image cropping attacks. Therefore, we use text region segmentation and watermark region localization to combat the image cropping attack. The distortion network has been designed to take into account the distortion that can occur during the transmission of medical record images, making the model robust against communication induced distortions. In addition, based on StegaStamp, we innovatively introduced FISM as part of the loss function to reduce the ripple texture in the steganographic image. The experimental results show that the designed distortion network and the FISM loss function term can be well suited for the steganographic task of medical record images from the perspective of decoding accuracy and image quality.

Predicting near-term glaucoma progression: An artificial intelligence approach using clinical free-text notes and data from electronic health records

Purpose

The purpose of this study was to develop a model to predict whether or not glaucoma will progress to the point of requiring surgery within the following year, using data from electronic health records (EHRs), including both structured data and free-text progress notes.

Methods

A cohort of adult glaucoma patients was identified from the EHR at Stanford University between 2008 and 2020, with data including free-text clinical notes, demographics, diagnosis codes, prior surgeries, and clinical information, including intraocular pressure, visual acuity, and central corneal thickness. Words from patients' notes were mapped to ophthalmology domain-specific neural word embeddings. Word embeddings and structured clinical data were combined as inputs to deep learning models to predict whether a patient would undergo glaucoma surgery in the following 12 months using the previous 4-12 months of clinical data. We also evaluated models using only structured data inputs (regression-, tree-, and deep-learning-based models) and models using only text inputs.

Results

Of the 3,469 glaucoma patients included in our cohort, 26% underwent surgery. The baseline penalized logistic regression model achieved an area under the receiver operating curve (AUC) of 0.873 and F1 score of 0.750, compared with the best tree-based model (random forest, AUC 0.876; F1 0.746), the deep learning structured features model (AUC 0.885; F1 0.757), the deep learning clinical free-text features model (AUC 0.767; F1 0.536), and the deep learning model with both the structured clinical features and free-text features (AUC 0.899; F1 0.745).

Discussion

Fusion models combining text and EHR structured data successfully and accurately predicted glaucoma progression to surgery. Future research incorporating imaging data could further optimize this predictive approach and be translated into clinical decision support tools.

Use of artificial intelligence in forecasting glaucoma progression

Artificial intelligence (AI) has been widely used in ophthalmology for disease detection and monitoring progression. For glaucoma research, AI has been used to understand progression patterns and forecast disease trajectory based on analysis of clinical and imaging data. Techniques such as machine learning, natural language processing, and deep learning have been employed for this purpose. The results from studies using AI for forecasting glaucoma progression however vary considerably due to dataset constraints, lack of a standard progression definition and differences in methodology and approach. While glaucoma detection and screening have been the focus of most research that has been published in the last few years, in this narrative review we focus on studies that specifically address glaucoma progression. We also summarize the current evidence, highlight studies that have translational potential, and provide suggestions on how future research that addresses glaucoma progression can be improved.

Assessing Usability of Smartwatch Digital Health Devices for Home Blood Pressure Monitoring among Glaucoma Patients

Glaucoma is a leading cause of blindness worldwide. Blood pressure (BP) dysregulation is a known risk factor, and home-based BP monitoring is increasingly used, but the usability of digital health devices to measure BP among glaucoma patients is not well studied. There may be particular usability challenges among this group, given that glaucoma disproportionately affects the elderly and can cause visual impairment. Therefore, the goal of this mixed-methods study was to assess the usability of a smart watch digital health device for home BP monitoring among glaucoma patients. Adult participants were recruited and given a smartwatch blood pressure monitor for at-home use. The eHEALS questionnaire was used to determine baseline digital health literacy. After a week of use, participants assessed the usability of the BP monitor and related mobile app using the Post-study System Usability Questionnaire (PSSUQ) and the System Usability Scale (SUS), standardized instruments to measure usability in health information technology interventions. Variations in scores were evaluated using ANOVA and open-ended responses about participants' experience were analyzed thematically. Overall, usability scores corresponded to the 80th-84th percentile, although older patients endorsed significantly worse usability based on quantitative scores and additionally provided qualitative feedback describing some difficulty using the device. Usability for older patients should be considered in the design of digital health devices for glaucoma given their disproportionate burden of disease and challenges in navigating digital health technologies, although the overall high usability scores for the device demonstrates promise for future clinical applications in glaucoma risk stratification.

Predictive Modeling of Long-Term Glaucoma Progression Based on Initial Ophthalmic Data and Optic Nerve Head Characteristics

Purpose

The purpose of this study was to develop a model, based on initial optic nerve head (ONH) characteristics, predictive of long-term rapid retinal nerve fiber layer (RNFL) thinning in patients with open-angle glaucoma (OAG).

Methods

This study evaluated 712 eyes with OAG that had been followed up for >5 years with annual evaluation of RNFL thickness. Baseline ophthalmic features were incorporated into the machine learning models for prediction of faster RNFL thinning. The model was trained and tested using a random forest (RF) method, and was interpreted using Shapley additive explanations. Factors associated with faster rate of RNFL thinning were statistically evaluated using a decision tree.

Results

The RF model showed that greater lamina cribrosa (LC) curvature, higher intraocular pressure (IOP), visual field mean deviation converging towards −5 dB, and thinner peripapillary choroid at baseline were the four most significant features predicting faster RNFL thinning. Partial interaction between the features showed that larger LC curvature was a strong factor for faster RNFL thinning when it exceeded approximately 12.0. When the LC curvature was ≤12, higher initial IOP and thinner peripapillary choroid played a role in the rapid RNFL thinning. Based on the decision tree, higher IOP (>26.5 mm Hg), greater laminar curvature (>13.95), and thinner peripapillary choroid (≤117.5 µm) were the 3 most important determinants affecting the rate of RNFL thinning.

Conclusions

Baseline ophthalmic data and ONH characteristics of patients with OAG were predictive of eyes at risk of faster progression. Combinations of important characteristics, such as IOP, LC curvature, and choroidal thickness, could stratify eyes into groups with different rates of RNFL thinning.

Translational Relevance

This work lays the foundations for developing prediction models to estimate glaucoma prognosis based on initial ONH characteristics.

Deep Learning Approaches for Predicting Glaucoma Progression Using Electronic Health Records and Natural Language Processing

Purpose

Advances in artificial intelligence have produced a few predictive models in glaucoma, including a logistic regression model predicting glaucoma progression to surgery. However, uncertainty exists regarding how to integrate the wealth of information in free-text clinical notes. The purpose of this study was to predict glaucoma progression requiring surgery using deep learning (DL) approaches on data from electronic health records (EHRs), including features from structured clinical data and from natural language processing of clinical free-text notes.

Design

Development of DL predictive model in an observational cohort.

Participants

Adult patients with glaucoma at a single center treated from 2008 through 2020.

Methods

Ophthalmology clinical notes of patients with glaucoma were identified from EHRs. Available structured data included patient demographic information, diagnosis codes, prior surgeries, and clinical information including intraocular pressure, visual acuity, and central corneal thickness. In addition, words from patients’ first 120 days of notes were mapped to ophthalmology domain-specific neural word embeddings trained on PubMed ophthalmology abstracts. Word embeddings and structured clinical data were used as inputs to DL models to predict subsequent glaucoma surgery.

Main Outcome Measures

Evaluation metrics included area under the receiver operating characteristic curve (AUC) and F1 score, the harmonic mean of positive predictive value, and sensitivity on a held-out test set.

Results

Seven hundred forty-eight of 4512 patients with glaucoma underwent surgery. The model that incorporated both structured clinical features as well as input features from clinical notes achieved an AUC of 73% and F1 of 40%, compared with only structured clinical features, (AUC, 66%; F1, 34%) and only clinical free-text features (AUC, 70%; F1, 42%). All models outperformed predictions from a glaucoma specialist’s review of clinical notes (F1, 29.5%).

Conclusions

We can successfully predict which patients with glaucoma will need surgery using DL models on EHRs unstructured text. Models incorporating free-text data outperformed those using only structured inputs. Future predictive models using EHRs should make use of information from within clinical free-text notes to improve predictive performance. Additional research is needed to investigate optimal methods of incorporating imaging data into future predictive models as well.

Integration of Artificial Intelligence and Blockchain Technology in Healthcare and Agriculture

Over the last decade, the healthcare sector has accelerated its digitization and electronic health records (EHRs). As information technology progresses, the notion of intelligent health also gathers popularity. By combining technologies such as the internet of things (IoT) and artificial intelligence (AI), innovative healthcare modifies and enhances traditional medical systems in terms of efficiency, service, and personalization. On the other side, intelligent healthcare systems are incredibly vulnerable to data breaches and other malicious assaults. Recently, blockchain technology has emerged as a potentially transformative option for enhancing data management, access control, and integrity inside healthcare systems. Integrating these advanced approaches in agriculture is critical for managing food supply chains, drug supply chains, quality maintenance, and intelligent prediction. This study reviews the literature, formulates a research topic, and analyzes the applicability of blockchain to the agriculture/food industry and healthcare, with a particular emphasis on AI and IoT. This article summarizes research on the newest blockchain solutions paired with AI technologies for strengthening and inventing new technological standards for the healthcare ecosystems and food industry.

Evaluating machine learning classifiers for glaucoma referral decision support in primary care settings

Several artificial intelligence algorithms have been proposed to help diagnose glaucoma by analyzing the functional and/or structural changes in the eye. These algorithms require carefully curated datasets with access to ocular images. In the current study, we have modeled and evaluated classifiers to predict self-reported glaucoma using a single, easily obtained ocular feature (intraocular pressure (IOP)) and non-ocular features (age, gender, race, body mass index, systolic and diastolic blood pressure, and comorbidities). The classifiers were trained on publicly available data of 3015 subjects without a glaucoma diagnosis at the time of enrollment. 337 subjects subsequently self-reported a glaucoma diagnosis in a span of 1–12 years after enrollment. The classifiers were evaluated on the ability to identify these subjects by only using their features recorded at the time of enrollment. Support vector machine, logistic regression, and adaptive boosting performed similarly on the dataset with F1 scores of 0.31, 0.30, and 0.28, respectively. Logistic regression had the highest sensitivity at 60% with a specificity of 69%. Predictive classifiers using primarily non-ocular features have the potential to be used for identifying suspected glaucoma in non-eye care settings, including primary care. Further research into finding additional features that improve the performance of predictive classifiers is warranted.

Development of the Integrated Glaucoma Risk Index

Various machine-learning schemes have been proposed to diagnose glaucoma. They can classify subjects into ‘normal’ or ‘glaucoma’-positive but cannot determine the severity of the latter. To complement this, researchers have proposed statistical indices for glaucoma risk. However, they are based on a single examination indicator and do not reflect the total severity of glaucoma progression. In this study, we propose an integrated glaucoma risk index (I-GRI) based on the visual field (VF) test, optical coherence tomography (OCT), and intraocular pressure (IOP) test. We extracted important features from the examination data using a machine learning scheme and integrated them into a single measure using a mathematical equation. The proposed index produces a value between 0 and 1; the higher the risk index value, the greater the risk/severity of glaucoma. In the sanity test using test cases, the I-GRI showed a balanced distribution in both glaucoma and normal cases. When we classified glaucoma and normal cases using the I-GRI, we obtained a misclassification rate of 0.07 (7%). The proposed index is useful for diagnosing glaucoma and for detecting its progression.

Predicting Glaucoma Progression Requiring Surgery Using Clinical Free-Text Notes and Transfer Learning With Transformers

Purpose

We evaluated the use of massive transformer-based language models to predict glaucoma progression requiring surgery using ophthalmology clinical notes from electronic health records (EHRs).

Methods

Ophthalmology clinical notes for 4512 glaucoma patients at a single center from 2008 to 2020 were identified from the EHRs. Four different pre-trained Bidirectional Encoder Representations from Transformers (BERT)-based models were fine-tuned on ophthalmology clinical notes from the patients' first 120 days of follow-up for the task of predicting which patients would require glaucoma surgery. Models were evaluated with standard metrics, including area under the receiver operating characteristic curve (AUROC) and F1 score.

Results

Of the patients, 748 progressed to require glaucoma surgery (16.6%). The original BERT model had the highest AUROC (73.4%; F1 = 45.0%) for identifying these patients, followed by RoBERTa, with an AUROC of 72.4% (F1 = 44.7%); DistilBERT, with an AUROC of 70.2% (F1 = 42.5%); and BioBERT, with an AUROC of 70.1% (F1 = 41.7%). All models had higher F1 scores than an ophthalmologist's review of clinical notes (F1 = 29.9%).

Conclusions

Using transfer learning with massively pre-trained BERT-based models is a natural language processing approach that can access the wealth of clinical information stored within ophthalmology clinical notes to predict the progression of glaucoma. Future work to improve model performance can focus on integrating structured or imaging data or further tailoring the BERT models to ophthalmology domain-specific text.

Translational Relevance

Predictive models can provide the basis for clinical decision support tools to aid clinicians in identifying high- or low-risk patients to maximally tailor glaucoma treatments.

Diagnosing post-traumatic stress disorder using electronic medical record data

This study proposes a predictive model that uses structured data and unstructured narrative notes from Electronic Medical Records to accurately identify patients diagnosed with Post-Traumatic Stress Disorder (PTSD). We utilize data from primary care clinicians participating in the Manitoba Primary Care Research Network (MaPCReN) representing 154,118 patients. A reference sample of 195 patients that had their PTSD diagnosis confirmed using a manual chart review of structured data and narrative notes, and PTSD negative patients is used as the gold standard data for model training, validation and testing. We assess structured and unstructured data from eight tables in the MaPCReN namely, patient demographics, disease case, examinations, medication, billing records, health condition, risk factors, and encounter notes. Feature engineering is applied to convert data into proper representation for predictive modeling. We explore serial and parallel mixed data models that are trained on both structured and unstructured data to identify PTSD. Model performances were calculated based on a highly skewed hold-out test dataset. The serial model that uses both structured and text data as input, yielded the highest values in sensitivity (0.77), F-measure (0.76), and AUC (0.88) and the parallel model that uses both structured and text data as the input obtained the highest positive predicted value (PPV) (0.75). Diseases such as PTSD are difficult to diagnose. Information recorded in the chart note over multiple visits of the patients with the primary care physicians has higher predictive power than structured data and combining these two data types can increase the predictive capabilities of machine learning models in diagnosing PTSD. While the deep-learning model outperformed the traditional ensemble model in processing text data, the ensemble classifier obtained better results in ingesting a combination of features obtained from both data types in the serial mixed model. The study demonstrated that unstructured encounter notes enhance a model's ability to identify patients diagnosed with PTSD. These findings can enhance quality improvement, research, and disease surveillance related to PTSD in primary care populations.

Predictive Analytics for Glaucoma Using Data From the All of Us Research Program

PURPOSE

To (1) use All of Us (AoU) data to validate a previously published single-center model predicting the need for surgery among individuals with glaucoma, (2) train new models using AoU data, and (3) share insights regarding this novel data source for ophthalmic research.

DESIGN

Development and evaluation of machine learning models.

METHODS

Electronic health record data were extracted from AoU for 1,231 adults diagnosed with primary open-angle glaucoma. The single-center model was applied to AoU data for external validation. AoU data were then used to train new models for predicting the need for glaucoma surgery using multivariable logistic regression, artificial neural networks, and random forests. Five-fold cross-validation was performed. Model performance was evaluated based on area under the receiver operating characteristic curve (AUC), accuracy, precision, and recall.

RESULTS

The mean (standard deviation) age of the AoU cohort was 69.1 (10.5) years, with 57.3% women and 33.5% black, significantly exceeding representation in the single-center cohort (P = .04 and P < .001, respectively). Of 1,231 participants, 286 (23.2%) needed glaucoma surgery. When applying the single-center model to AoU data, accuracy was 0.69 and AUC was only 0.49. Using AoU data to train new models resulted in superior performance: AUCs ranged from 0.80 (logistic regression) to 0.99 (random forests).

CONCLUSIONS

Models trained with national AoU data achieved superior performance compared with using single-center data. Although AoU does not currently include ophthalmic imaging, it offers several strengths over similar big-data sources such as claims data. AoU is a promising new data source for ophthalmic research.

Development and evaluation of novel ophthalmology domain-specific neural word embeddings to predict visual prognosis

OBJECTIVE

To develop and evaluate novel word embeddings (WEs) specific to ophthalmology, using text corpora from published literature and electronic health records (EHR).

MATERIALS AND METHODS

We trained ophthalmology-specific WEs using 121,740 PubMed abstracts and 89,282 EHR notes using word2vec continuous bag-of-words architecture. PubMed and EHR WEs were compared to general domain GloVe WEs and general biomedical domain BioWordVec embeddings using a novel ophthalmology-domain-specific 200-question analogy test and prediction of prognosis in 5547 low vision patients using EHR notes as inputs to a deep learning model.

RESULTS

We found that many words representing important ophthalmic concepts in the EHR were missing from the general domain GloVe vocabulary, but covered in the ophthalmology abstract corpus. On ophthalmology analogy testing, PubMed WEs scored 95.0 %, outperforming EHR (86.0 %) and GloVe (91.0 %) but less than BioWordVec (99.5 %). On predicting low vision prognosis, PubMed and EHR WEs resulted in similar AUROC (0.830; 0.826), outperforming GloVe (0.778) and BioWordVec (0.784).

CONCLUSION

We found that using ophthalmology domain-specific WEs improved performance in ophthalmology-related clinical prediction compared to general WEs. Deep learning models using clinical notes as inputs can predict the prognosis of visually impaired patients. This work provides a framework to improve predictive models using domain-specific WEs.

A deep learning approach to identify blepharoptosis by convolutional neural networks

PURPOSE

Blepharoptosis is a known cause of reversible vision loss. Accurate assessment can be difficult, especially amongst non-specialists. Existing automated techniques disrupt clinical workflow by requiring user input, or placement of reference markers. Neural networks are known to be effective in image classification tasks. We aim to develop an algorithm that can accurately identify blepharoptosis from a clinical photo.

METHODS

A total of 500 clinical photographs from patients with and without blepharoptosis were sourced from a tertiary ophthalmic center in Taiwan. Images were labeled by two oculoplastic surgeons, with an independent third oculoplastic surgeon to adjudicate disagreements. These images were used to train a series of convolutional neural networks (CNNs) to ascertain the best CNN architecture for this particular task.

RESULTS

Of the models that trained on the dataset, most were able to identify ptosis images with reasonable accuracy. We found the best performing model to use the DenseNet121 architecture without pre-training which achieved a sensitivity of 90.1 % with a specificity of 82.4 %, compared to the worst performing model which was used a Resnet34 architecture with pre-training, achieving a sensitivity of 74.1 %, and specificity of 63.6 %. Models with and without pre-training performed similarly (mean accuracy 82.6 % vs. 85.8 % respectively, p = 0.06), though models with pre-training took less time to train (1-minute vs. 16 min, p < 0.01).

CONCLUSIONS

We report the use of AI to accurately diagnose blepharoptosis from a clinical photograph with no external reference markers or user input requirement. Most current-generation CNN architectures performed reasonably on this task, with the DenseNet121, and Resnet18 architectures without pre-training performing best in our dataset.

Systematic literature review of machine learning methods used in the analysis of real-world data for patient-provider decision making

BACKGROUND

Machine learning is a broad term encompassing a number of methods that allow the investigator to learn from the data. These methods may permit large real-world databases to be more rapidly translated to applications to inform patient-provider decision making.

METHODS

This systematic literature review was conducted to identify published observational research of employed machine learning to inform decision making at the patient-provider level. The search strategy was implemented and studies meeting eligibility criteria were evaluated by two independent reviewers. Relevant data related to study design, statistical methods and strengths and limitations were identified; study quality was assessed using a modified version of the Luo checklist.

RESULTS

A total of 34 publications from January 2014 to September 2020 were identified and evaluated for this review. There were diverse methods, statistical packages and approaches used across identified studies. The most common methods included decision tree and random forest approaches. Most studies applied internal validation but only two conducted external validation. Most studies utilized one algorithm, and only eight studies applied multiple machine learning algorithms to the data. Seven items on the Luo checklist failed to be met by more than 50% of published studies.

CONCLUSIONS

A wide variety of approaches, algorithms, statistical software, and validation strategies were employed in the application of machine learning methods to inform patient-provider decision making. There is a need to ensure that multiple machine learning approaches are used, the model selection strategy is clearly defined, and both internal and external validation are necessary to be sure that decisions for patient care are being made with the highest quality evidence. Future work should routinely employ ensemble methods incorporating multiple machine learning algorithms.

Special Commentary: Using Clinical Decision Support Systems to Bring Predictive Models to the Glaucoma Clinic

Advances in the field of predictive modeling using artificial intelligence and machine learning have the potential to improve clinical care and outcomes, but only if the results of these models are presented appropriately to clinicians at the time they make decisions for individual patients. Clinical decision support (CDS) systems could be used to accomplish this. Modern CDS systems are computer-based tools designed to improve clinician decision making for individual patients. However, not all CDS systems are effective. Four principles that have been shown in other medical fields to be important for successful CDS system implementation are (1) integration into clinician workflow, (2) user-centered interface design, (3) evaluation of CDS systems and rules, and (4) standards-based development so the tools can be deployed across health systems.

Applications of deep learning in detection of glaucoma: A systematic review

Glaucoma is the leading cause of irreversible blindness and disability worldwide. Nevertheless, the majority of patients do not know they have the disease and detection of glaucoma progression using standard technology remains a challenge in clinical practice. Artificial intelligence (AI) is an expanding field that offers the potential to improve diagnosis and screening for glaucoma with minimal reliance on human input. Deep learning (DL) algorithms have risen to the forefront of AI by providing nearly human-level performance, at times exceeding the performance of humans for detection of glaucoma on structural and functional tests. A succinct summary of present studies and challenges to be addressed in this field is needed. Following PRISMA guidelines, we conducted a systematic review of studies that applied DL methods for detection of glaucoma using color fundus photographs, optical coherence tomography (OCT), or standard automated perimetry (SAP). In this review article we describe recent advances in DL as applied to the diagnosis of glaucoma and glaucoma progression for application in screening and clinical settings, as well as the challenges that remain when applying this novel technique in glaucoma.

Balancing treatments for patients with systemic hypertension and glaucoma

INTRODUCTION

Treatment of glaucoma and systemic hypertension requires careful balancing of ophthalmic and systemic medications. This report offers background on various common therapies and considerations to minimize undesirable systemic and ophthalmic outcomes.

AREAS COVERED

Key considerations relating to medical treatments for systemic hypertension and glaucoma chosen from a review of the literature are included. The historic safety of ophthalmic beta blockers and sub-types as well as combination agents are described. The potential role of ocular perfusion pressure in the pathogenesis of glaucoma is reviewed with a discussion of landmark studies, including the Barbados Eye Study and the Early Manifest Glaucoma Trial, with implications for medication choices for hypertension and glaucoma. Systemic antihypertensives are considered with regards to their potential association with glaucoma and its progression, especially in patients with low blood pressure and normal tension glaucoma. Opinions are offered with regard to therapeutic choices and considerations.

EXPERT OPINION

The medical treatment of systemic hypertension and glaucoma carries the potential for drug interactions and significant adverse effects requiring customization for each patient's particular issues. The evolution of new therapies and treatment paradigms, including laser as a first-line treatment, has expanded safe options and minimized potential dangers for high-risk patients.

Personalising surgical treatments for glaucoma patients

Surgical treatments for glaucoma have relied for decades on traditional filtering surgery such as trabeculectomy and, in more challenging cases, tubes. Antifibrotics were introduced to improve surgical success in patients at increased risk of failure but have been shown to be linked to a greater incidence of complications, some being potentially vision-threatening. As our understanding of glaucoma and its early diagnosis have improved, a more individualised management has been suggested. Recently the term "precision medicine" has emerged as a new concept of an individualised approach to disease management incorporating a wide range of individual data in the choice of therapeutic modalities. For glaucoma surgery, this involves evaluation of the right timing, individual risk factors, targeting the correct anatomical and functional outflow pathways and appropriate prevention of scarring. As a consequence, there is an obvious need for better knowledge of anatomical and functional pathways and for more individualised surgical approaches with new, less invasive and safer techniques allowing for earlier intervention. With the recent advent of minimally invasive glaucoma surgery (MIGS) a large number of novel devices have been introduced targeting potential new sites of the outflow pathway for lowering intraocular pressure (IOP). Their popularity is growing in view of the relative surgical simplicity and apparent lack of serious side effects. However, these new surgical techniques are still in an era of early experiences, short follow-up and lack of evidence of their superiority in safety and cost-effectiveness over the traditional methods. Each year several new devices are introduced while others are withdrawn from the market. Glaucoma continues to be the primary cause of irreversible blindness worldwide and access to safe and efficacious treatment is a serious problem, particularly in the emerging world where the burden of glaucoma-related blindness is important and concerning. Early diagnosis, individualised treatment and, very importantly, safe surgical management should be the hallmarks of glaucoma treatment. However, there is still need for a better understanding of the disease, its onset and progression, the functional and structural elements of the outflow pathways in relation to the new devices as well as their long-term IOP-lowering efficacy and safety. This review discusses current knowledge and the future need for personalised glaucoma surgery.

Applications of Artificial Intelligence to Electronic Health Record Data in Ophthalmology

Widespread adoption of electronic health records (EHRs) has resulted in the collection of massive amounts of clinical data. In ophthalmology in particular, the volume range of data captured in EHR systems has been growing rapidly. Yet making effective secondary use of this EHR data for improving patient care and facilitating clinical decision-making has remained challenging due to the complexity and heterogeneity of these data. Artificial intelligence (AI) techniques present a promising way to analyze these multimodal data sets. While AI techniques have been extensively applied to imaging data, there are a limited number of studies employing AI techniques with clinical data from the EHR. The objective of this review is to provide an overview of different AI methods applied to EHR data in the field of ophthalmology. This literature review highlights that the secondary use of EHR data has focused on glaucoma, diabetic retinopathy, age-related macular degeneration, and cataracts with the use of AI techniques. These techniques have been used to improve ocular disease diagnosis, risk assessment, and progression prediction. Techniques such as supervised machine learning, deep learning, and natural language processing were most commonly used in the articles reviewed.