Predicting conversion to wet age-related macular degeneration using deep learning

[Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extensionDiretrizes para protocolos de ensaios clínicos com intervenções que utilizam inteligência artificial: a extensão SPIRIT-AI]

The SPIRIT 2013 statement aims to improve the completeness of clinical trial protocol reporting by providing evidence-based recommendations for the minimum set of items to be addressed. This guidance has been instrumental in promoting transparent evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate their impact on health outcomes. The SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence) extension is a new reporting guideline for clinical trial protocols evaluating interventions with an AI component. It was developed in parallel with its companion statement for trial reports: CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 26 candidate items, which were consulted upon by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The SPIRIT-AI extension includes 15 new items that were considered sufficiently important for clinical trial protocols of AI interventions. These new items should be routinely reported in addition to the core SPIRIT 2013 items. SPIRIT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention will be integrated, considerations for the handling of input and output data, the human-AI interaction and analysis of error cases. SPIRIT-AI will help promote transparency and completeness for clinical trial protocols for AI interventions. Its use will assist editors and peer reviewers, as well as the general readership, to understand, interpret and critically appraise the design and risk of bias for a planned clinical trial.

Deep Learning in Optical Coherence Tomography Angiography: Current Progress, Challenges, and Future Directions

Optical coherence tomography angiography (OCT-A) provides depth-resolved visualization of the retinal microvasculature without intravenous dye injection. It facilitates investigations of various retinal vascular diseases and glaucoma by assessment of qualitative and quantitative microvascular changes in the different retinal layers and radial peripapillary layer non-invasively, individually, and efficiently. Deep learning (DL), a subset of artificial intelligence (AI) based on deep neural networks, has been applied in OCT-A image analysis in recent years and achieved good performance for different tasks, such as image quality control, segmentation, and classification. DL technologies have further facilitated the potential implementation of OCT-A in eye clinics in an automated and efficient manner and enhanced its clinical values for detecting and evaluating various vascular retinopathies. Nevertheless, the deployment of this combination in real-world clinics is still in the "proof-of-concept" stage due to several limitations, such as small training sample size, lack of standardized data preprocessing, insufficient testing in external datasets, and absence of standardized results interpretation. In this review, we introduce the existing applications of DL in OCT-A, summarize the potential challenges of the clinical deployment, and discuss future research directions.

iERM: An Interpretable Deep Learning System to Classify Epiretinal Membrane for Different Optical Coherence Tomography Devices: A Multi-Center Analysis

Background: Epiretinal membranes (ERM) have been found to be common among individuals >50 years old. However, the severity grading assessment for ERM based on optical coherence tomography (OCT) images has remained a challenge due to lacking reliable and interpretable analysis methods. Thus, this study aimed to develop a two-stage deep learning (DL) system named iERM to provide accurate automatic grading of ERM for clinical practice. Methods: The iERM was trained based on human segmentation of key features to improve classification performance and simultaneously provide interpretability to the classification results. We developed and tested iERM using a total of 4547 OCT B-Scans of four different commercial OCT devices that were collected from nine international medical centers. Results: As per the results, the integrated network effectively improved the grading performance by 1−5.9% compared with the traditional classification DL model and achieved high accuracy scores of 82.9%, 87.0%, and 79.4% in the internal test dataset and two external test datasets, respectively. This is comparable to retinal specialists whose average accuracy scores are 87.8% and 79.4% in two external test datasets. Conclusion: This study proved to be a benchmark method to improve the performance and enhance the interpretability of the traditional DL model with the implementation of segmentation based on prior human knowledge. It may have the potential to provide precise guidance for ERM diagnosis and treatment.

Machine learning-based mortality prediction models using national liver transplantation registries are feasible but have limited utility across countries

Many countries curate national registries of liver transplant (LT) data. These registries are often used to generate predictive models; however, potential performance and transferability of these models remain unclear. We used data from 3 national registries and developed machine learning algorithm (MLA)-based models to predict 90-day post-LT mortality within and across countries. Predictive performance and external validity of each model were assessed. Prospectively collected data of adult patients (aged ≥18 years) who underwent primary LTs between January 2008 and December 2018 from the Canadian Organ Replacement Registry (Canada), National Health Service Blood and Transplantation (United Kingdom), and United Network for Organ Sharing (United States) were used to develop MLA models to predict 90-day post-LT mortality. Models were developed using each registry individually (based on variables inherent to the individual databases) and using all 3 registries combined (variables in common between the registries [harmonized]). The model performance was evaluated using area under the receiver operating characteristic (AUROC) curve. The number of patients included was as follows: Canada, n = 1214; the United Kingdom, n = 5287; and the United States, n = 59,558. The best performing MLA-based model was ridge regression across both individual registries and harmonized data sets. Model performance diminished from individualized to the harmonized registries, especially in Canada (individualized ridge: AUROC, 0.74; range, 0.73-0.74; harmonized: AUROC, 0.68; range, 0.50-0.73) and US (individualized ridge: AUROC, 0.71; range, 0.70-0.71; harmonized: AUROC, 0.66; range, 0.66-0.66) data sets. External model performance across countries was poor overall. MLA-based models yield a fair discriminatory potential when used within individual databases. However, the external validity of these models is poor when applied across countries. Standardization of registry-based variables could facilitate the added value of MLA-based models in informing decision making in future LTs.

Wayfinding artificial intelligence to detect clinically meaningful spots of retinal diseases: Artificial intelligence to help retina specialists in real world practice

AIM/BACKGROUND

To aim of this study is to develop an artificial intelligence (AI) that aids in the thought process by providing retinal clinicians with clinically meaningful or abnormal findings rather than just a final diagnosis, i.e., a "wayfinding AI."

METHODS

Spectral domain optical coherence tomography B-scan images were classified into 189 normal and 111 diseased eyes. These were automatically segmented using a deep-learning based boundary-layer detection model. During segmentation, the AI model calculates the probability of the boundary surface of the layer for each A-scan. If this probability distribution is not biased toward a single point, layer detection is defined as ambiguous. This ambiguity was calculated using entropy, and a value referred to as the ambiguity index was calculated for each OCT image. The ability of the ambiguity index to classify normal and diseased images and the presence or absence of abnormalities in each layer of the retina were evaluated based on the area under the curve (AUC). A heatmap, i.e., an ambiguity-map, of each layer, that changes the color according to the ambiguity index value, was also created.

RESULTS

The ambiguity index of the overall retina of the normal and disease-affected images (mean ± SD) were 1.76 ± 0.10 and 2.06 ± 0.22, respectively, with a significant difference (p < 0.05). The AUC used to distinguish normal and disease-affected images using the ambiguity index was 0.93, and was 0.588 for the internal limiting membrane boundary, 0.902 for the nerve fiber layer/ganglion cell layer boundary, 0.920 for the inner plexiform layer/inner nuclear layer boundary, 0.882 for the outer plexiform layer/outer nuclear layer boundary, 0.926 for the ellipsoid zone line, and 0.866 for the retinal pigment epithelium/Bruch's membrane boundary. Three representative cases reveal the usefulness of an ambiguity map.

CONCLUSIONS

The present AI algorithm can pinpoint abnormal retinal lesions in OCT images, and its localization is known at a glance when using an ambiguity map. This will help diagnose the processes of clinicians as a wayfinding tool.

The Need for Artificial Intelligence Based Risk Factor Analysis for Age-Related Macular Degeneration: A Review

In epidemiology, a risk factor is a variable associated with increased disease risk. Understanding the role of risk factors is significant for developing a strategy to improve global health. There is strong evidence that risk factors like smoking, alcohol consumption, previous cataract surgery, age, high-density lipoprotein (HDL) cholesterol, BMI, female gender, and focal hyper-pigmentation are independently associated with age-related macular degeneration (AMD). Currently, in the literature, statistical techniques like logistic regression, multivariable logistic regression, etc., are being used to identify AMD risk factors by employing numerical/categorical data. However, artificial intelligence (AI) techniques have not been used so far in the literature for identifying risk factors for AMD. On the other hand, artificial intelligence (AI) based tools can anticipate when a person is at risk of developing chronic diseases like cancer, dementia, asthma, etc., in providing personalized care. AI-based techniques can employ numerical/categorical and/or image data thus resulting in multimodal data analysis, which provides the need for AI-based tools to be used for risk factor analysis in ophthalmology. This review summarizes the statistical techniques used to identify various risk factors and the higher benefits that AI techniques provide for AMD-related disease prediction. Additional studies are required to review different techniques for risk factor identification for other ophthalmic diseases like glaucoma, diabetic macular edema, retinopathy of prematurity, cataract, and diabetic retinopathy.

Advances in artificial intelligence applications for ocular surface diseases diagnosis

In recent years, with the rapid development of computer technology, continual optimization of various learning algorithms and architectures, and establishment of numerous large databases, artificial intelligence (AI) has been unprecedentedly developed and applied in the field of ophthalmology. In the past, ophthalmological AI research mainly focused on posterior segment diseases, such as diabetic retinopathy, retinopathy of prematurity, age-related macular degeneration, retinal vein occlusion, and glaucoma optic neuropathy. Meanwhile, an increasing number of studies have employed AI to diagnose ocular surface diseases. In this review, we summarize the research progress of AI in the diagnosis of several ocular surface diseases, namely keratitis, keratoconus, dry eye, and pterygium. We discuss the limitations and challenges of AI in the diagnosis of ocular surface diseases, as well as prospects for the future.

Efficacy of a Deep Learning System for Screening Myopic Maculopathy Based on Color Fundus Photographs

INTRODUCTION

The maculopathy in highly myopic eyes is complex. Its clinical diagnosis is a huge workload and subjective. To simply and quickly classify pathologic myopia (PM), a deep learning algorithm was developed and assessed to screen myopic maculopathy lesions based on color fundus photographs.

METHODS

This study included 10,347 ocular fundus photographs from 7606 participants. Of these photographs, 8210 were used for training and validation, and 2137 for external testing. A deep learning algorithm was trained, validated, and externally tested to screen myopic maculopathy which was classified into four categories: normal or mild tessellated fundus, severe tessellated fundus, early-stage PM, and advanced-stage PM. The area under the precision-recall curve, the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, and Cohen's kappa were calculated and compared with those of retina specialists.

RESULTS

In the validation data set, the model detected normal or mild tessellated fundus, severe tessellated fundus, early-stage PM, and advanced-stage PM with AUCs of 0.98, 0.95, 0.99, and 1.00, respectively; while in the external-testing data set of 2137 photographs, the model had AUCs of 0.99, 0.96, 0.98, and 1.00, respectively.

CONCLUSIONS

We developed a deep learning model for detection and classification of myopic maculopathy based on fundus photographs. Our model achieved high sensitivities, specificities, and reliable Cohen's kappa, compared with those of attending ophthalmologists.

Predicting the future development of diabetic retinopathy using a deep learning algorithm for the analysis of non-invasive retinal imaging

Aims

Diabetic retinopathy (DR) is the most common cause of vision loss in the working age. This research aimed to develop an artificial intelligence (AI) machine learning model which can predict the development of referable DR from fundus imagery of otherwise healthy eyes.

Methods

Our researchers trained a machine learning algorithm on the EyePACS data set, consisting of 156 363 fundus images. Referrable DR was defined as any level above mild on the International Clinical Diabetic Retinopathy scale.

Results

The algorithm achieved 0.81 area under receiver operating curve (AUC) when averaging scores from multiple images on the task of predicting development of referrable DR, and 0.76 AUC when using a single image.

Conclusion

Our results suggest that risk of DR may be predicted from fundus photography alone. Prediction of personalised risk of DR may become key in treatment and contribute to patient compliance across the board, particularly when supported by further prospective research.

Radiomics-Based Assessment of OCT Angiography Images for Diabetic Retinopathy Diagnosis

Purpose

To evaluate the diagnostic accuracy of machine learning (ML) techniques applied to radiomic features extracted from OCT and OCT angiography (OCTA) images for diabetes mellitus (DM), diabetic retinopathy (DR), and referable DR (R-DR) diagnosis.

Design

Cross-sectional analysis of a retinal image dataset from a previous prospective OCTA study (ClinicalTrials.govNCT03422965).

Participants

Patients with type 1 DM and controls included in the progenitor study.

Methods

Radiomic features were extracted from fundus retinographies, OCT, and OCTA images in each study eye. Logistic regression, linear discriminant analysis, support vector classifier (SVC)-linear, SVC-radial basis function, and random forest models were created to evaluate their diagnostic accuracy for DM, DR, and R-DR diagnosis in all image types.

Main Outcome Measures

Area under the receiver operating characteristic curve (AUC) mean and standard deviation for each ML model and each individual and combined image types.

Results

A dataset of 726 eyes (439 individuals) were included. For DM diagnosis, the greatest AUC was observed for OCT (0.82, 0.03). For DR detection, the greatest AUC was observed for OCTA (0.77, 0.03), especially in the 3 × 3 mm superficial capillary plexus OCTA scan (0.76, 0.04). For R-DR diagnosis, the greatest AUC was observed for OCTA (0.87, 0.12) and the deep capillary plexus OCTA scan (0.86, 0.08). The addition of clinical variables (age, sex, etc.) improved most models AUC for DM, DR and R-DR diagnosis. The performance of the models was similar in unilateral and bilateral eyes image datasets.

Conclusions

Radiomics extracted from OCT and OCTA images allow identification of patients with DM, DR, and R-DR using standard ML classifiers. OCT was the best test for DM diagnosis, OCTA for DR and R-DR diagnosis and the addition of clinical variables improved most models. This pioneer study demonstrates that radiomics-based ML techniques applied to OCT and OCTA images may be an option for DR screening in patients with type 1 DM.

Financial Disclosures

Proprietary or commercial disclosure may be found after the references.

Randomised controlled trials in medical AI: ethical considerations

In recent years, there has been a surge of high-profile publications on applications of artificial intelligence (AI) systems for medical diagnosis and prognosis. While AI provides various opportunities for medical practice, there is an emerging consensus that the existing studies show considerable deficits and are unable to establish the clinical benefit of AI systems. Hence, the view that the clinical benefit of AI systems needs to be studied in clinical trials-particularly randomised controlled trials (RCTs)-is gaining ground. However, an issue that has been overlooked so far in the debate is that, compared with drug RCTs, AI RCTs require methodological adjustments, which entail ethical challenges. This paper sets out to develop a systematic account of the ethics of AI RCTs by focusing on the moral principles of clinical equipoise, informed consent and fairness. This way, the objective is to animate further debate on the (research) ethics of medical AI.

Artificial intelligence and deep learning in ophthalmology: Current status and future perspectives

Background

The ophthalmology field was among the first to adopt artificial intelligence (AI) in medicine. The availability of digitized ocular images and substantial data have made deep learning (DL) a popular topic.

Main text

At the moment, AI in ophthalmology is mostly used to improve disease diagnosis and assist decision-making aiming at ophthalmic diseases like diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD), cataract and other anterior segment diseases. However, most of the AI systems developed to date are still in the experimental stages, with only a few having achieved clinical applications. There are a number of reasons for this phenomenon, including security, privacy, poor pervasiveness, trust and explainability concerns.

Conclusions

This review summarizes AI applications in ophthalmology, highlighting significant clinical considerations for adopting AI techniques and discussing the potential challenges and future directions.

An overview of artificial intelligence in diabetic retinopathy and other ocular diseases

Artificial intelligence (AI), also known as machine intelligence, is a branch of science that empowers machines using human intelligence. AI refers to the technology of rendering human intelligence through computer programs. From healthcare to the precise prevention, diagnosis, and management of diseases, AI is progressing rapidly in various interdisciplinary fields, including ophthalmology. Ophthalmology is at the forefront of AI in medicine because the diagnosis of ocular diseases heavy reliance on imaging. Recently, deep learning-based AI screening and prediction models have been applied to the most common visual impairment and blindness diseases, including glaucoma, cataract, age-related macular degeneration (ARMD), and diabetic retinopathy (DR). The success of AI in medicine is primarily attributed to the development of deep learning algorithms, which are computational models composed of multiple layers of simulated neurons. These models can learn the representations of data at multiple levels of abstraction. The Inception-v3 algorithm and transfer learning concept have been applied in DR and ARMD to reuse fundus image features learned from natural images (non-medical images) to train an AI system with a fraction of the commonly used training data (<1%). The trained AI system achieved performance comparable to that of human experts in classifying ARMD and diabetic macular edema on optical coherence tomography images. In this study, we highlight the fundamental concepts of AI and its application in these four major ocular diseases and further discuss the current challenges, as well as the prospects in ophthalmology.

Machine learning-based 3D modeling and volumetry of human posterior vitreous cavity of optical coherence tomographic images

The structure of the human vitreous varies considerably because of age-related liquefactions of the vitreous gel. These changes are poorly studied in vivo mainly because their high transparency and mobility make it difficult to obtain reliable and repeatable images of the vitreous. Optical coherence tomography can detect the boundaries between the vitreous gel and vitreous fluid, but it is difficult to obtain high resolution images that can be used to convert the images to three-dimensional (3D) images. Thus, the purpose of this study was to determine the shape and characteristics of the vitreous fluid using machine learning-based 3D modeling in which manually labelled fluid areas were used to train deep convolutional neural network (DCNN). The trained DCNN labelled vitreous fluid automatically and allowed us to obtain 3D vitreous model and to quantify the vitreous fluidic cavities. The mean volume and surface area of posterior vitreous fluidic cavities are 19.6 ± 7.8 mm³ and 104.0 ± 18.9 mm² in eyes of 17 school children. The results suggested that vitreous fluidic cavities expanded as the cavities connects with each other, and this modeling system provided novel imaging markers for aging and eye diseases.

Optical Coherence Tomography Classification Systems for Diabetic Macular Edema and Their Associations With Visual Outcome and Treatment Responses - An Updated Review

ABSTRACT

Optical coherence tomography (OCT) is an invaluable imaging tool in detecting and assessing diabetic macular edema (DME). Over the past decade, there have been different proposed OCT-based classification systems for DME. In this review, we present an update of spectral-domain OCT (SDOCT)-based DME classifications over the past 5 years. In addition, we attempt to summarize the proposed OCT qualitative and quantitative parameters from different classification systems in relation to disease severity, risk of progression, and treatment outcome. Although some OCT-based measurements were found to have prognostic value on visual outcome, there has been a lack of consensus or guidelines on which parameters can be reliably used to predict treatment outcomes. We also summarize recent literatures on the prognostic value of these parameters including quantitative measures such as macular thickness or volume, central subfield thickness or foveal thickness, and qualitative features such as the morphology of the vitreoretinal interface, disorganization of retinal inner layers, ellipsoid zone disruption integrity, and hyperreflec-tive foci. In addition, we discuss that a framework to assess the validity of biomarkers for treatment outcome is essentially important in assessing the prognosis before deciding on treatment in DME. Finally, we echo with other experts on the demand for updating the current diabetic retinal disease classification.

Review on Artificial Intelligence Based Ophthalmic Application

Artificial intelligence is the leading branch of technology and innovation. The utility of artificial intelligence in the field of medicine is also remarkable. From drug discovery and development till the introduction of products in the market, artificial intelligence can play its role. As people age, they are more prone to be affected by eye diseases around the globe. Early diagnosis and detection help in minimizing the risk of vision loss and providing a quality life. With the help of artificial intelligence, the workload of humans and manmade errors can be reduced to an extent. The need for artificial intelligence in the area of ophthalmic is also found to be significant. As people age, they are more prone to be affected by eye diseases around the globe. Early diagnosis and detection help in minimizing the risk of vision loss and providing a quality life. In this review, we elaborated on the use of artificial intelligence in the field of pharmaceutical product development mainly with its application in ophthalmic care. AI in the future has a high potential to increase the success rate in the drug discovery phase has already been established. The application of artificial intelligence for drug development, diagnosis, and treatment is also reported with the scientific evidence in this paper.

Leveraging clinical data across healthcare institutions for continual learning of predictive risk models

The inherent flexibility of machine learning-based clinical predictive models to learn from episodes of patient care at a new institution (site-specific training) comes at the cost of performance degradation when applied to external patient cohorts. To exploit the full potential of cross-institutional clinical big data, machine learning systems must gain the ability to transfer their knowledge across institutional boundaries and learn from new episodes of patient care without forgetting previously learned patterns. In this work, we developed a privacy-preserving learning algorithm named WUPERR (Weight Uncertainty Propagation and Episodic Representation Replay) and validated the algorithm in the context of early prediction of sepsis using data from over 104,000 patients across four distinct healthcare systems. We tested the hypothesis, that the proposed continual learning algorithm can maintain higher predictive performance than competing methods on previous cohorts once it has been trained on a new patient cohort. In the sepsis prediction task, after incremental training of a deep learning model across four hospital systems (namely hospitals H-A, H-B, H-C, and H-D), WUPERR maintained the highest positive predictive value across the first three hospitals compared to a baseline transfer learning approach (H-A: 39.27% vs. 31.27%, H-B: 25.34% vs. 22.34%, H-C: 30.33% vs. 28.33%). The proposed approach has the potential to construct more generalizable models that can learn from cross-institutional clinical big data in a privacy-preserving manner.

From Data to Deployment: The Collaborative Community on Ophthalmic Imaging Roadmap for Artificial Intelligence in Age-Related Macular Degeneration

OBJECTIVE

Health care systems worldwide are challenged to provide adequate care for the 200 million individuals with age-related macular degeneration (AMD). Artificial intelligence (AI) has the potential to make a significant, positive impact on the diagnosis and management of patients with AMD; however, the development of effective AI devices for clinical care faces numerous considerations and challenges, a fact evidenced by a current absence of Food and Drug Administration (FDA)-approved AI devices for AMD.

PURPOSE

To delineate the state of AI for AMD, including current data, standards, achievements, and challenges.

METHODS

Members of the Collaborative Community on Ophthalmic Imaging Working Group for AI in AMD attended an inaugural meeting on September 7, 2020, to discuss the topic. Subsequently, they undertook a comprehensive review of the medical literature relevant to the topic. Members engaged in meetings and discussion through December 2021 to synthesize the information and arrive at a consensus.

RESULTS

Existing infrastructure for robust AI development for AMD includes several large, labeled data sets of color fundus photography and OCT images; however, image data often do not contain the metadata necessary for the development of reliable, valid, and generalizable models. Data sharing for AMD model development is made difficult by restrictions on data privacy and security, although potential solutions are under investigation. Computing resources may be adequate for current applications, but knowledge of machine learning development may be scarce in many clinical ophthalmology settings. Despite these challenges, researchers have produced promising AI models for AMD for screening, diagnosis, prediction, and monitoring. Future goals include defining benchmarks to facilitate regulatory authorization and subsequent clinical setting generalization.

CONCLUSIONS

Delivering an FDA-authorized, AI-based device for clinical care in AMD involves numerous considerations, including the identification of an appropriate clinical application; acquisition and development of a large, high-quality data set; development of the AI architecture; training and validation of the model; and functional interactions between the model output and clinical end user. The research efforts undertaken to date represent starting points for the medical devices that eventually will benefit providers, health care systems, and patients.

Generating future fundus images for early age-related macular degeneration based on generative adversarial networks

BACKGROUND AND OBJECTIVE

Age-related macular degeneration (AMD) is one of the most common diseases that can lead to blindness worldwide. Recently, various fundus image analyzing studies are done using deep learning methods to classify fundus images to aid diagnosis and monitor AMD disease progression. But until now, to the best of our knowledge, no attempt was made to generate future synthesized fundus images that can predict AMD progression. In this paper, we developed a deep learning model using fundus images for AMD patients with different time elapses to generate synthetic future fundus images.

METHOD

We exploit generative adversarial networks (GANs) with additional drusen masks to maintain the pathological information. The dataset included 8196 fundus images from 1263 AMD patients. A proposed GAN-based model, called Multi-Modal GAN (MuMo-GAN), was trained to generate synthetic predicted-future fundus images.

RESULTS

The proposed deep learning model indicates that the additional drusen masks can help to learn the AMD progression. Our model can generate future fundus images with appropriate pathological features. The drusen development over time is depicted well. Both qualitative and quantitative experiments show that our model is more efficient to monitor the AMD disease as compared to other studies.

CONCLUSION

This study could help individualized risk prediction for AMD patients. Compared to existing methods, the experimental results show a significant improvement in terms of tracking the AMD stage in both image-level and pixel-level.

SD-OCT Biomarkers and the Current Status of Artificial Intelligence in Predicting Progression from Intermediate to Advanced AMD

Age-related macular degeneration (AMD) is one of the leading causes of blindness in the Western World. Optical coherence tomography (OCT) has revolutionized the diagnosis and follow-up of AMD patients. This review focuses on SD-OCT imaging biomarkers which were identified as predictors for progression in intermediate AMD to late AMD, either geographic atrophy (GA) or choroidal neovascularization (CNV). Structural OCT remains the most compelling modality to study AMD features related to the progression such as drusen characteristics, hyperreflective foci (HRF), reticular pseudo-drusen (RPD), sub-RPE hyper-reflective columns and their impact on retinal layers. Further on, we reviewed articles that attempted to integrate biomarkers that have already proven their involvement in intermediate AMD progression, in their models of artificial intelligence (AI). By combining structural biomarkers with genetic risk and lifestyle the predictive ability becomes more accurate.

Screening of Common Retinal Diseases Using Six-Category Models Based on EfficientNet

Purpose

A six-category model of common retinal diseases is proposed to help primary medical institutions in the preliminary screening of the five common retinal diseases.

Methods

A total of 2,400 fundus images of normal and five common retinal diseases were provided by a cooperative hospital. Two six-category deep learning models of common retinal diseases based on the EfficientNet-B4 and ResNet50 models were trained. The results from the six-category models in this study and the results from a five-category model in our previous study based on ResNet50 were compared. A total of 1,315 fundus images were used to test the models, the clinical diagnosis results and the diagnosis results of the two six-category models were compared. The main evaluation indicators were sensitivity, specificity, F1-score, area under the curve (AUC), 95% confidence interval, kappa and accuracy, and the receiver operator characteristic curves of the two six-category models were compared in the study.

Results

The diagnostic accuracy rate of EfficientNet-B4 model was 95.59%, the kappa value was 94.61%, and there was high diagnostic consistency. The AUC of the normal diagnosis and the five retinal diseases were all above 0.95. The sensitivity, specificity, and F1-score for the diagnosis of normal fundus images were 100, 99.9, and 99.83%, respectively. The specificity and F1-score for RVO diagnosis were 95.68, 98.61, and 93.09%, respectively. The sensitivity, specificity, and F1-score for high myopia diagnosis were 96.1, 99.6, and 97.37%, respectively. The sensitivity, specificity, and F1-score for glaucoma diagnosis were 97.62, 99.07, and 94.62%, respectively. The sensitivity, specificity, and F1-score for DR diagnosis were 90.76, 99.16, and 93.3%, respectively. The sensitivity, specificity, and F1-score for MD diagnosis were 92.27, 98.5, and 91.51%, respectively.

Conclusion

The EfficientNet-B4 model was used to design a six-category model of common retinal diseases. It can be used to diagnose the normal fundus and five common retinal diseases based on fundus images. It can help primary doctors in the screening for common retinal diseases, and give suitable suggestions and recommendations. Timely referral can improve the efficiency of diagnosis of eye diseases in rural areas and avoid delaying treatment.

AMD Genomics: Non-Coding RNAs as Biomarkers and Therapeutic Targets

Age-related macular degeneration (AMD) is a progressive neurodegenerative disease that is the world’s leading cause of blindness in the aging population. Although the clinical stages and forms of AMD have been elucidated, more specific prognostic tools are required to determine when patients with early and intermediate AMD will progress into the advanced stages of AMD. Another challenge in the field has been the appropriate development of therapies for intermediate AMD and advanced atrophic AMD. After numerous negative clinical trials, an anti-C5 agent and anti-C3 agent have recently shown promising results in phase 3 clinical trials, in terms of slowing the growth of geographic atrophy, an advanced form of AMD. Interestingly, both drugs appear to be associated with an increased incidence of wet AMD, another advanced form of the disease, and will require frequent intravitreal injections. Certainly, there remains a need for other therapeutic agents with the potential to prevent progression to advanced stages of the disease. Investigation of the role and clinical utility of non-coding RNAs (ncRNAs) is a major advancement in biology that has only been minimally applied to AMD. In the following review, we discuss the clinical relevance of ncRNAs in AMD as both biomarkers and therapeutic targets.

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.

Identifying the Retinal Layers Linked to Human Contrast Sensitivity Via Deep Learning

Purpose

Luminance contrast is the fundamental building block of human spatial vision. Therefore contrast sensitivity, the reciprocal of contrast threshold required for target detection, has been a barometer of human visual function. Although retinal ganglion cells (RGCs) are known to be involved in contrast coding, it still remains unknown whether the retinal layers containing RGCs are linked to a person's contrast sensitivity (e.g., Pelli-Robson contrast sensitivity) and, if so, to what extent the retinal layers are related to behavioral contrast sensitivity. Thus the current study aims to identify the retinal layers and features critical for predicting a person's contrast sensitivity via deep learning.

Methods

Data were collected from 225 subjects including individuals with either glaucoma, age-related macular degeneration, or normal vision. A deep convolutional neural network trained to predict a person's Pelli-Robson contrast sensitivity from structural retinal images measured with optical coherence tomography was used. Then, activation maps that represent the critical features learned by the network for the output prediction were computed.

Results

The thickness of both ganglion cell and inner plexiform layers, reflecting RGC counts, were found to be significantly correlated with contrast sensitivity (r = 0.26 ∼ 0.58, Ps < 0.001 for different eccentricities). Importantly, the results showed that retinal layers containing RGCs were the critical features the network uses to predict a person's contrast sensitivity (an average R2 = 0.36 ± 0.10).

Conclusions

The findings confirmed the structure and function relationship for contrast sensitivity while highlighting the role of RGC density for human contrast sensitivity.

A clinical perspective on the expanding role of artificial intelligence in age-related macular degeneration

In recent years, there has been intense development of artificial intelligence (AI) techniques, which have the potential to improve the clinical management of age-related macular degeneration (AMD) and facilitate the prevention of irreversible vision loss from this condition. Such AI techniques could be used as clinical decision support tools to: (i) improve the detection of AMD by community eye health practitioners, (ii) enhance risk stratification to enable personalised monitoring strategies for those with the early stages of AMD, and (iii) enable early detection of signs indicative of possible choroidal neovascularisation allowing triaging of patients requiring urgent review. This review discusses the latest developments in AI techniques that show promise for these tasks, as well as how they may help in the management of patients being treated for choroidal neovascularisation and in accelerating the discovery of new treatments in AMD.

On Algorithmic Fairness in Medical Practice

The application of machine-learning technologies to medical practice promises to enhance the capabilities of healthcare professionals in the assessment, diagnosis, and treatment, of medical conditions. However, there is growing concern that algorithmic bias may perpetuate or exacerbate existing health inequalities. Hence, it matters that we make precise the different respects in which algorithmic bias can arise in medicine, and also make clear the normative relevance of these different kinds of algorithmic bias for broader questions about justice and fairness in healthcare. In this paper, we provide the building blocks for an account of algorithmic bias and its normative relevance in medicine.

Artificial intelligence applied to ophthalmology and optometry: A citation network analysis

PURPOSE

The objective of this study is to analyse co-authorship and co-citation networks of publications in the field of artificial intelligence in ophthalmology and optometry. As well as, identify the different areas of research and the most cited publication.

METHOD

A search of publications was performed in the Web of Science database for the period from 1977 to December 2021, using the term "Artificial Intelligence AND (Ophthalmol* OR optometry)". The analysis of the publication was carried out using the Citation Network Explorer, VOSviewer and CiteSpace software.

RESULTS

1086 publications and 2348 citation networks were found. 2020 was the year with the highest number of publications, a total of 351 publications and 115 citation networks. The most cited publication was "Clinically applicable deep learning for diagnosis and referral in retinal disease" published by De Fauw et al. in 2018, with a citation index of 723. Through the clustering function, three groups were found that cover the main research areas in this field: retinal pathology, anterior segment and glaucoma.

CONCLUSIONS

The citation network analysis offers an in-depth analysis of scientific publications and the adoption of new topics and fields of research. The results of an exhaustive analysis of citation networks in artificial intelligence in the field of ophthalmology and optometry are presented since the publication of the first article in 1977.

Non-invasive testing for early detection of neovascular macular degeneration in unaffected second eyes of older adults: EDNA diagnostic accuracy study

BACKGROUND

Neovascular age-related macular degeneration is a leading cause of sight loss, and early detection and treatment is important. For patients with neovascular age-related macular degeneration in one eye, it is usual practice to monitor the unaffected eye. The test used to diagnose neovascular age-related macular degeneration, fundus fluorescein angiography, is an invasive test. Non-invasive tests are available, but their diagnostic accuracy is unclear.

OBJECTIVES

The primary objective was to determine the diagnostic monitoring performance of tests for neovascular age-related macular degeneration in the second eye of patients with unilateral neovascular age-related macular degeneration. The secondary objectives were the cost-effectiveness of tests and to identify predictive factors of developing neovascular age-related macular degeneration.

DESIGN

This was a multicentre, prospective, cohort, comparative diagnostic accuracy study in a monitoring setting for up to 3 years. A Cox regression risk prediction model and a Markov microsimulation model comparing cost-effectiveness of the index tests over 25 years were used.

SETTING

This took place in hospital eye services.

PARTICIPANTS

Participants were adults (aged 50-95 years) with newly diagnosed (within the previous 6 weeks) neovascular age-related macular degeneration in one eye and an unaffected second (study) eye who were attending for treatment injections in the first eye and who had a study eye baseline visual acuity of ≥ 68 Early Treatment Diabetic Retinopathy Study letters.

INTERVENTIONS

The index tests were Amsler chart (completed by participants), fundus clinical examination, optical coherence tomography, self-reported vision assessment (completed by participants) and visual acuity. The reference standard was fundus fluorescein angiography.

MAIN OUTCOME MEASURES

The main outcome measures were sensitivity and specificity; the performance of the risk predictor model; and costs and quality-adjusted life-years.

RESULTS

In total, 552 out of 578 patients who consented from 24 NHS hospitals (n = 16 ineligible; n = 10 withdrew consent) took part. The mean age of the patients was 77.4 years (standard deviation 7.7 years) and 57.2% were female. For the primary analysis, 464 patients underwent follow-up fundus fluorescein angiography and 120 developed neovascular age-related macular degeneration on fundus fluorescein angiography. The diagnostic accuracy [sensitivity (%) (95% confidence interval); specificity (%) (95% confidence interval)] was as follows: optical coherence tomography 91.7 (85.2 to 95.6); 87.8 (83.8 to 90.9)], fundus clinical examination [53.8 (44.8 to 62.5); 97.6 (95.3 to 98.9)], Amsler [33.7 (25.1 to 43.5); 81.4 (76.4 to 85.5)], visual acuity [30.0 (22.5 to 38.7); 66.3 (61.0 to 71.1)] and self-reported vision [4.2 (1.6 to 9.8); 97.0 (94.6 to 98.5)]. Optical coherence tomography had the highest sensitivity across all secondary analyses. The final prediction model for neovascular age-related macular degeneration in the non-affected eye included smoking status, family history of neovascular age-related macular degeneration, the presence of nodular drusen with or without reticular pseudodrusen, and the presence of pigmentary abnormalities [c-statistic 0.66 (95% confidence interval 0.62 to 0.71)]. Optical coherence tomography monitoring generated the greatest quality-adjusted life-years gained per patient (optical coherence tomography, 5.830; fundus clinical examination, 5.787; Amsler chart, 5.736, self-reported vision, 5.630; and visual acuity, 5.600) for the lowest health-care and social care costs (optical coherence tomography, £19,406; fundus clinical examination, £19,649; Amsler chart, £19,751; self-reported vision, £20,198; and visual acuity, £20,444) over the lifetime of the simulated cohort. Optical coherence tomography dominated the other tests or had an incremental cost-effectiveness ratio below the accepted cost-effectiveness thresholds (£20,000) across the scenarios explored.

LIMITATIONS

The diagnostic performance may be different in an unselected population without any history of neovascular age-related macular degeneration; the prediction model did not include genetic profile data, which might have improved the discriminatory performance.

CONCLUSIONS

Optical coherence tomography was the most accurate in diagnosing conversion to neovascular age-related macular degeneration in the fellow eye of patients with unilateral neovascular age-related macular degeneration. Economic modelling suggests that optical coherence tomography monitoring is cost-effective and leads to earlier diagnosis of and treatment for neovascular age-related macular degeneration in the second eye of patients being treated for neovascular age-related macular degeneration in their first eye.

FUTURE WORK

Future works should investigate the role of home monitoring, improved risk prediction models and impact on long-term visual outcomes.

STUDY REGISTRATION

This study was registered as ISRCTN48855678.

FUNDING

This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 26, No. 8. See the NIHR Journals Library website for further project information.

Machine Learning Algorithms to Detect Subclinical Keratoconus: Systematic Review

BACKGROUND

Keratoconus is a disorder characterized by progressive thinning and distortion of the cornea. If detected at an early stage, corneal collagen cross-linking can prevent disease progression and further visual loss. Although advanced forms are easily detected, reliable identification of subclinical disease can be problematic. Several different machine learning algorithms have been used to improve the detection of subclinical keratoconus based on the analysis of multiple types of clinical measures, such as corneal imaging, aberrometry, or biomechanical measurements.

OBJECTIVE

The aim of this study is to survey and critically evaluate the literature on the algorithmic detection of subclinical keratoconus and equivalent definitions.

METHODS

For this systematic review, we performed a structured search of the following databases: MEDLINE, Embase, and Web of Science and Cochrane Library from January 1, 2010, to October 31, 2020. We included all full-text studies that have used algorithms for the detection of subclinical keratoconus and excluded studies that did not perform validation. This systematic review followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) recommendations.

RESULTS

We compared the measured parameters and the design of the machine learning algorithms reported in 26 papers that met the inclusion criteria. All salient information required for detailed comparison, including diagnostic criteria, demographic data, sample size, acquisition system, validation details, parameter inputs, machine learning algorithm, and key results are reported in this study.

CONCLUSIONS

Machine learning has the potential to improve the detection of subclinical keratoconus or early keratoconus in routine ophthalmic practice. Currently, there is no consensus regarding the corneal parameters that should be included for assessment and the optimal design for the machine learning algorithm. We have identified avenues for further research to improve early detection and stratification of patients for early treatment to prevent disease progression.

Precision reimbursement for precision medicine: the need for patient-level decisions between payers, providers and pharmaceutical companies

Healthcare costs have been dramatically rising in developed economies worldwide. A key driver of cost increases has been high-cost drugs. The current model of reimbursement is not configured for drugs with uncertain outcomes. Future reimbursement will require better allocation of available healthcare system funds. Technological advancements have opened the door to a new type of outcomes-based reimbursement, enabling value exchange between payers and pharmaceutical companies, which we term precision reimbursement. Precision reimbursement extends beyond value-based contracts, with decisions at individual rather than aggregate level. For precision reimbursement to be adopted, there are data, computation and infrastructure requirements. All stakeholders benefit in moving to precision reimbursement for optimal resource allocation, risk sharing and, ultimately, improved outcomes. There are implementation challenges including cost, change management, information governance and development of surrogate markers. The overarching trend in medicine is toward personalised interventions, with precision reimbursement as the logical consequence.

Trustworthy AI: Closing the gap between development and integration of AI systems in ophthalmic practice

An increasing number of artificial intelligence (AI) systems are being proposed in ophthalmology, motivated by the variety and amount of clinical and imaging data, as well as their potential benefits at the different stages of patient care. Despite achieving close or even superior performance to that of experts, there is a critical gap between development and integration of AI systems in ophthalmic practice. This work focuses on the importance of trustworthy AI to close that gap. We identify the main aspects or challenges that need to be considered along the AI design pipeline so as to generate systems that meet the requirements to be deemed trustworthy, including those concerning accuracy, resiliency, reliability, safety, and accountability. We elaborate on mechanisms and considerations to address those aspects or challenges, and define the roles and responsibilities of the different stakeholders involved in AI for ophthalmic care, i.e., AI developers, reading centers, healthcare providers, healthcare institutions, ophthalmological societies and working groups or committees, patients, regulatory bodies, and payers. Generating trustworthy AI is not a responsibility of a sole stakeholder. There is an impending necessity for a collaborative approach where the different stakeholders are represented along the AI design pipeline, from the definition of the intended use to post-market surveillance after regulatory approval. This work contributes to establish such multi-stakeholder interaction and the main action points to be taken so that the potential benefits of AI reach real-world ophthalmic settings.

Intraprocedural Artificial Intelligence for Colorectal Cancer Detection and Characterisation in Endoscopy and Laparoscopy

In this article, we provide an evidence-based primer of current tools and evolving concepts in the area of intraprocedural artificial intelligence (AI) methods in colonoscopy and laparoscopy as a 'procedure companion', with specific focus on colorectal cancer recognition and characterisation. These interventions are both likely beneficiaries from an impending rapid phase in technical and technological evolution. The domains where AI is most likely to impact are explored as well as the methodological pitfalls pertaining to AI methods. Such issues include the need for large volumes of data to train AI systems, questions surrounding false positive rates, explainability and interpretability as well as recent concerns surrounding instabilities in current deep learning (DL) models. The area of biophysics-inspired models, a potential remedy to some of these pitfalls, is explored as it could allow our understanding of the fundamental physiological differences between tissue types to be exploited in real time with the help of computer-assisted interpretation. Right now, such models can include data collected from dynamic fluorescence imaging in surgery to characterise lesions by their biology reducing the number of cases needed to build a reliable and interpretable classification system. Furthermore, instead of focussing on image-by-image analysis, such systems could analyse in a continuous fashion, more akin to how we view procedures in real life and make decisions in a manner more comparable to human decision-making. Synergistical approaches can ensure AI methods usefully embed within practice thus safeguarding against collapse of this exciting field of investigation as another 'boom and bust' cycle of AI endeavour.

LamNet: A Lesion Attention Maps-Guided Network for the Prediction of Choroidal Neovascularization Volume in SD-OCT Images

Choroidal neovascularization (CNV) volume prediction has an important clinical significance to predict the therapeutic effect and schedule the follow-up. In this paper, we propose a Lesion Attention Maps-Guided Network (LamNet) to automatically predict the CNV volume of next follow-up visit after therapy based on 3-dimentional spectral-domain optical coherence tomography (SD-OCT) images. In particular, the backbone of LamNet is a 3D convolutional neural network (3D-CNN). In order to guide the network to focus on the local CNV lesion regions, we use CNV attention maps generated by an attention map generator to produce the multi-scale local context features. Then, the multi-scale of both local and global feature maps are fused to achieve the high-precision CNV volume prediction. In addition, we also design a synergistic multi-task predictor, in which a trend-consistent loss ensures that the change trend of the predicted CNV volume is consistent with the real change trend of the CNV volume. The experiments include a total of 541 SD-OCT cubes from 68 patients with two types of CNV captured by two different SD-OCT devices. The results demonstrate that LamNet can provide the reliable and accurate CNV volume prediction, which would further assist the clinical diagnosis and design the treatment options.

Implementation and Application of an Intelligent Pterygium Diagnosis System Based on Deep Learning

Objective: This study aims to implement and investigate the application of a special intelligent diagnostic system based on deep learning in the diagnosis of pterygium using anterior segment photographs.

Methods: A total of 1,220 anterior segment photographs of normal eyes and pterygium patients were collected for training (using 750 images) and testing (using 470 images) to develop an intelligent pterygium diagnostic model. The images were classified into three categories by the experts and the intelligent pterygium diagnosis system: (i) the normal group, (ii) the observation group of pterygium, and (iii) the operation group of pterygium. The intelligent diagnostic results were compared with those of the expert diagnosis. Indicators including accuracy, sensitivity, specificity, kappa value, the area under the receiver operating characteristic curve (AUC), as well as 95% confidence interval (CI) and F1-score were evaluated.

Results: The accuracy rate of the intelligent diagnosis system on the 470 testing photographs was 94.68%; the diagnostic consistency was high; the kappa values of the three groups were all above 85%. Additionally, the AUC values approached 100% in group 1 and 95% in the other two groups. The best results generated from the proposed system for sensitivity, specificity, and F1-scores were 100, 99.64, and 99.74% in group 1; 90.06, 97.32, and 92.49% in group 2; and 92.73, 95.56, and 89.47% in group 3, respectively.

Conclusion: The intelligent pterygium diagnosis system based on deep learning can not only judge the presence of pterygium but also classify the severity of pterygium. This study is expected to provide a new screening tool for pterygium and benefit patients from areas lacking medical resources.

Updates in deep learning research in ophthalmology

Ophthalmology has been one of the early adopters of artificial intelligence (AI) within the medical field. Deep learning (DL), in particular, has garnered significant attention due to the availability of large amounts of data and digitized ocular images. Currently, AI in Ophthalmology is mainly focused on improving disease classification and supporting decision-making when treating ophthalmic diseases such as diabetic retinopathy, age-related macular degeneration (AMD), glaucoma and retinopathy of prematurity (ROP). However, most of the DL systems (DLSs) developed thus far remain in the research stage and only a handful are able to achieve clinical translation. This phenomenon is due to a combination of factors including concerns over security and privacy, poor generalizability, trust and explainability issues, unfavorable end-user perceptions and uncertain economic value. Overcoming this challenge would require a combination approach. Firstly, emerging techniques such as federated learning (FL), generative adversarial networks (GANs), autonomous AI and blockchain will be playing an increasingly critical role to enhance privacy, collaboration and DLS performance. Next, compliance to reporting and regulatory guidelines, such as CONSORT-AI and STARD-AI, will be required to in order to improve transparency, minimize abuse and ensure reproducibility. Thirdly, frameworks will be required to obtain patient consent, perform ethical assessment and evaluate end-user perception. Lastly, proper health economic assessment (HEA) must be performed to provide financial visibility during the early phases of DLS development. This is necessary to manage resources prudently and guide the development of DLS.

Five-Year Reactivation After Ranibizumab or Aflibercept Treatment for Neovascular Age-Related Macular Degeneration and Polypoidal Choroidal Vasculopathy

Purpose: To evaluate 5-year reactivation after ranibizumab or aflibercept treatment for neovascular age-related macular degeneration (AMD) and polypoidal choroidal vasculopathy (PCV). Methods: This retrospective study included 192 patients (192 eyes) who had been diagnosed with neovascular AMD or PCV and treated with ranibizumab or aflibercept. The incidence and timing of lesion reactivation during the 5-year follow-up period were evaluated, and the factors associated with reactivation were also investigated. Results: During the follow-up period, lesion reactivation was noted in 156 patients (81.3%) at a mean of 9.5 ± 10.5 months after the third antivascular endothelial growth factor injection. The incidence of reactivation was 59.9% during the first 12 months, 33.7% during ≥12 and <24 months, 11.8% during >24 and ≤36 months, 15.5% during >36 and ≤48 months, and 5.3% during >48 and ≤60 months. There was a significant difference in the incidence among the 5 periods (P < 0.001). The proportion of PCV was significantly higher in patients experiencing reactivation (51.9%) than in those who did not (30.6%) (P = 0.021). Conclusions: During the 5-year follow-up, lesion reactivation was noted in approximately four-fifths of the patients. The incidence of lesion reactivation was highest during the first 12 months and decreased thereafter. The incidence was higher in patients with PCV than in those with neovascular AMD, especially after 12 months.

Predicting post-operative right ventricular failure using video-based deep learning

Despite progressive improvements over the decades, the rich temporally resolved data in an echocardiogram remain underutilized. Human assessments reduce the complex patterns of cardiac wall motion, to a small list of measurements of heart function. All modern echocardiography artificial intelligence (AI) systems are similarly limited by design - automating measurements of the same reductionist metrics rather than utilizing the embedded wealth of data. This underutilization is most evident where clinical decision making is guided by subjective assessments of disease acuity. Predicting the likelihood of developing post-operative right ventricular failure (RV failure) in the setting of mechanical circulatory support is one such example. Here we describe a video AI system trained to predict post-operative RV failure using the full spatiotemporal density of information in pre-operative echocardiography. We achieve an AUC of 0.729, and show that this ML system significantly outperforms a team of human experts at the same task on independent evaluation.

Imaging and artificial intelligence for progression of age-related macular degeneration

Age-related macular degeneration (AMD) is a leading cause of severe vision loss. With our aging population, it may affect 288 million people globally by the year 2040. AMD progresses from an early and intermediate dry form to an advanced one, which manifests as choroidal neovascularization and geographic atrophy. Conversion to AMD-related exudation is known as progression to neovascular AMD, and presence of geographic atrophy is known as progression to advanced dry AMD. AMD progression predictions could enable timely monitoring, earlier detection and treatment, improving vision outcomes. Machine learning approaches, a subset of artificial intelligence applications, applied on imaging data are showing promising results in predicting progression. Extracted biomarkers, specifically from optical coherence tomography scans, are informative in predicting progression events. The purpose of this mini review is to provide an overview about current machine learning applications in artificial intelligence for predicting AMD progression, and describe the various methods, data-input types, and imaging modalities used to identify high-risk patients. With advances in computational capabilities, artificial intelligence applications are likely to transform patient care and management in AMD. External validation studies that improve generalizability to populations and devices, as well as evaluating systems in real-world clinical settings are needed to improve the clinical translations of artificial intelligence AMD applications.