Naama Hammel
Naama is a clinical research scientist in Google Health. In this role she focuses on developing and validating machine learning for medical applications across multiple fields including ophthalmology, dermatology, and more. Naama is an ophthalmologist with a subspecialty in glaucoma. She completed her medical and ophthalmology training at Tel-Aviv University; her glaucoma fellowship at the Shiley Eye Institute, UC San Diego; and her ophthalmic informatics fellowship at the UC Davis Eye Center.
Research Areas
Authored Publications
Google Publications
Other Publications
Sort By
Beyond Predictions: Explainability and Learning from Machine Learning
Chih-Ying Deng
Akinori Mitani
Christina Chen
Lily Peng
Digital Eye Care and Teleophthalmology, Springer (2023)
Preview abstract
The intense interest in developing machine learning (ML) models for applications in ophthalmology has produced many potentially useful tools for disease detection, grading, and prognostication. However, though many of these efforts have produced well-validated models, the inner workings of these methods may not be easily understood by many clinicians, patients, and even ML practitioners. In this chapter, we focus on ML model explainability, and begin by first highlighting the utility and importance of explainability before presenting a clinician-accessible explanation of the commonly used methods and the type of insights these methods provide. Next, we present several case studies of ML studies incorporating explainability and describe these studies’ strengths as well as limitations. Finally, we discuss the important work that lies ahead, and how explainability may eventually help push the frontiers of scientific knowledge by enabling human experts to learn from what the machine has learned.
View details
Discovering novel systemic biomarkers in external eye photos
Ilana Traynis
Christina Chen
Akib Uddin
Jorge Cuadros
Lauren P. Daskivich
April Y. Maa
Ramasamy Kim
Eugene Yu-Chuan Kang
Lily Peng
Avinash Varadarajan
The Lancet Digital Health (2023)
Preview abstract
Background
Photographs of the external eye were recently shown to reveal signs of diabetic retinal disease and elevated glycated haemoglobin. This study aimed to test the hypothesis that external eye photographs contain information about additional systemic medical conditions.
Methods
We developed a deep learning system (DLS) that takes external eye photographs as input and predicts systemic parameters, such as those related to the liver (albumin, aspartate aminotransferase [AST]); kidney (estimated glomerular filtration rate [eGFR], urine albumin-to-creatinine ratio [ACR]); bone or mineral (calcium); thyroid (thyroid stimulating hormone); and blood (haemoglobin, white blood cells [WBC], platelets). This DLS was trained using 123 130 images from 38 398 patients with diabetes undergoing diabetic eye screening in 11 sites across Los Angeles county, CA, USA. Evaluation focused on nine prespecified systemic parameters and leveraged three validation sets (A, B, C) spanning 25 510 patients with and without diabetes undergoing eye screening in three independent sites in Los Angeles county, CA, and the greater Atlanta area, GA, USA. We compared performance against baseline models incorporating available clinicodemographic variables (eg, age, sex, race and ethnicity, years with diabetes).
Findings
Relative to the baseline, the DLS achieved statistically significant superior performance at detecting AST >36.0 U/L, calcium <8.6 mg/dL, eGFR <60.0 mL/min/1.73 m2, haemoglobin <11.0 g/dL, platelets <150.0 × 103/μL, ACR ≥300 mg/g, and WBC <4.0 × 103/μL on validation set A (a population resembling the development datasets), with the area under the receiver operating characteristic curve (AUC) of the DLS exceeding that of the baseline by 5.3–19.9% (absolute differences in AUC). On validation sets B and C, with substantial patient population differences compared with the development datasets, the DLS outperformed the baseline for ACR ≥300.0 mg/g and haemoglobin <11.0 g/dL by 7.3–13.2%.
Interpretation
We found further evidence that external eye photographs contain biomarkers spanning multiple organ systems. Such biomarkers could enable accessible and non-invasive screening of disease. Further work is needed to understand the translational implications.
View details
Lessons learned from translating AI from development to deployment in healthcare
Sunny Virmani
Jay Nayar
Elin Rønby Pedersen
Divleen Jeji
Lily Peng
Nature Medicine (2023)
Preview abstract
The application of an artificial intelligence (AI)-based screening tool for retinal disease in India and Thailand highlighted the myths and reality of introducing medical AI, which may form a framework for subsequent tools.
View details
Retinal fundus photographs capture hemoglobin loss after blood donation
Akinori Mitani
Ilana Traynis
Lily Hao Yi Peng
Avinash Vaidyanathan Varadarajan
medRxiv (2022)
Preview abstract
Recently it was shown that blood hemoglobin concentration could be predicted from retinal fundus photographs by deep learning models. However, it is unclear whether the models were quantifying current blood hemoglobin level, or estimating based on subjects' pretest probability of having anemia. Here, we conducted an observational study with 14 volunteers who donated blood at an on site blood drive held by the local blood center (ie, at which time approximately 10% of their blood was removed). When the deep learning model was applied to retinal fundus photographs taken before and after blood donation, it detected a decrease in blood hemoglobin concentration within each subject at 2-3 days after donation, suggesting that the model was quantifying subacute hemoglobin changes instead of predicting subjects' risk. Additional randomized or controlled studies can further validate this finding.
View details
Using generative AI to investigate medical imagery models and datasets
Charles Lau
Chloe Nichols
Doron Yaya-Stupp
Heather Cole-Lewis
Ilana Traynis
https://arxiv.org/ (2022)
Preview abstract
AI models have shown promise in performing many medical imaging tasks. However, our ability to explain what signals these models learn from the training data is severely lacking. Explanations are needed in order to increase the trust of doctors in AI-based models, especially in domains where AI prediction capabilities surpass those of humans. Moreover, such explanations could enable novel scientific discovery by uncovering signals in the data that aren’t yet known to experts. In this paper, we present a method for automatic visual explanations that can help achieve these goals by generating hypotheses of what visual signals in the images are correlated with the task. We propose the following 4 steps: (i) Train a classifier to perform a given task to assess whether the imagery indeed contains signals relevant to the task; (ii) Train a StyleGAN-based image generator with an architecture that enables guidance by the classifier (“StylEx”); (iii) Automatically detect and extract the top visual attributes that the classifier is sensitive to. Each of these attributes can then be independently modified for a set of images to generate counterfactual visualizations of those attributes (i.e. what that image would look like with the attribute increased or decreased); (iv) Present the discovered attributes and corresponding counterfactual visualizations to a multidisciplinary panel of experts to formulate hypotheses for the underlying mechanisms with consideration to social and structural determinants of health (e.g. whether the attributes correspond to known patho-physiological or socio-cultural phenomena, or could be novel discoveries) and stimulate future research. To demonstrate the broad applicability of our approach, we demonstrate results on eight prediction tasks across three medical imaging modalities – retinal fundus photographs, external eye photographs, and chest radiographs. We showcase examples where many of the automatically-learned attributes clearly capture clinically known features (e.g., types of cataract, enlarged heart), and demonstrate automatically-learned confounders that arise from factors beyond physiological mechanisms (e.g., chest X-ray underexposure is correlated with the classifier predicting abnormality, and eye makeup is correlated with the classifier predicting low hemoglobin levels). We further show that our method reveals a number of physiologically plausible novel attributes for future investigation (e.g., differences in the fundus associated with self-reported sex, which were previously unknown). While our approach is not able to discern causal pathways, the ability to generate hypotheses from the attribute visualizations has the potential to enable researchers to better understand, improve their assessment, and extract new knowledge from AI-based models. Importantly, we highlight that attributes generated by our framework can capture phenomena beyond physiology or pathophysiology, reflecting the real world nature of healthcare delivery and socio-cultural factors, and hence multidisciplinary perspectives are critical in these investigations. Finally, we release code to enable researchers to train their own StylEx models and analyze their predictive tasks of interest, and use the methodology presented in this paper for responsible interpretation of the revealed attributes.
View details
Deep learning to detect optical coherence tomography-derived diabetic macular edema from retinal photographs: a multicenter validation study
Xinle Sheila Liu
Tayyeba Ali
Ami Shah
Scott Mayer McKinney
Paisan Ruamviboonsuk
Angus W. Turner
Pearse A. Keane
Peranut Chotcomwongse
Variya Nganthavee
Mark Chia
Josef Huemer
Jorge Cuadros
Rajiv Raman
Lily Hao Yi Peng
Avinash Vaidyanathan Varadarajan
Reena Chopra
Ophthalmology Retina (2022)
Preview abstract
Purpose
To validate the generalizability of a deep learning system (DLS) that detects diabetic macular edema (DME) from two-dimensional color fundus photography (CFP), where the reference standard for retinal
thickness and fluid presence is derived from three-dimensional optical coherence tomography (OCT).
Design
Retrospective validation of a DLS across international datasets.
Participants
Paired CFP and OCT of patients from diabetic retinopathy (DR) screening programs or retina clinics. The DLS was developed using datasets from Thailand, the United Kingdom (UK) and the United States and validated using 3,060 unique eyes from 1,582 patients across screening populations in Australia, India and Thailand. The DLS was separately validated in 698 eyes from 537 screened patients in the UK with mild DR and suspicion of DME based on CFP.
Methods
The DLS was trained using DME labels from OCT. Presence of DME was based on retinal thickening or intraretinal fluid. The DLS’s performance was compared to expert grades of maculopathy and to a previous proof-of-concept version of the DLS. We further simulated integration of the current DLS into an algorithm trained to detect DR from CFPs.
Main Outcome Measures
Superiority of specificity and non-inferiority of sensitivity of the DLS for the detection of center-involving DME, using device specific thresholds, compared to experts.
Results
Primary analysis in a combined dataset spanning Australia, India, and Thailand showed the DLS had 80% specificity and 81% sensitivity compared to expert graders who had 59% specificity and 70% sensitivity. Relative to human experts, the DLS had significantly higher specificity (p=0.008) and non-inferior sensitivity (p<0.001). In the UK dataset the DLS had a specificity of 80% (p<0.001 for specificity > 50%) and a sensitivity of 100% (p=0.02 for sensitivity > 90%).
Conclusions
The DLS can generalize to multiple international populations with an accuracy exceeding experts. The clinical value of this DLS to reduce false positive referrals, thus decreasing the burden on specialist eye care, warrants prospective evaluation.
View details
Detection of signs of disease in external photographs of the eyes via deep learning
Akinori Mitani
Ilana Traynis
Naho Kitade
April Maa
Jorge Cuadros
Lily Hao Yi Peng
Avinash Vaidyanathan Varadarajan
Nature Biomedical Engineering (2022)
Preview abstract
Retinal fundus photographs can be used to detect a range of retinal conditions. Here we show that deep-learning models trained instead on external photographs of the eyes can be used to detect diabetic retinopathy (DR), diabetic macular oedema and poor blood glucose control. We developed the models using eye photographs from 145,832 patients with diabetes from 301 DR screening sites and evaluated the models on four tasks and four validation datasets with a total of 48,644 patients from 198 additional screening sites. For all four tasks, the predictive performance of the deep-learning models was significantly higher than the performance of logistic regression models using self-reported demographic and medical history data, and the predictions generalized to patients with dilated pupils, to patients from a different DR screening programme and to a general eye care programme that included diabetics and non-diabetics. We also explored the use of the deep-learning models for the detection of elevated lipid levels. The utility of external eye photographs for the diagnosis and management of diseases should be further validated with images from different cameras and patient populations.
View details
Iterative quality control strategies for expert medical image labeling
Sonia Phene
Abigail Huang
Rebecca Ackermann
Olga Kanzheleva
Caitlin Taggart
Proceedings of the AAAI Conference on Human Computation and Crowdsourcing (2021)
Preview abstract
Data quality is a key concern for artificial intelligence (AI) efforts that rely upon crowdsourced data collection. In the domain of medicine in particular, labeled data must meet higher quality standards, or the resulting AI may lead to patient harm, and/or perpetuate biases. What are the challenges involved in expert medical labeling? What processes do such teams employ? In this study, we interviewed members of teams developing AI for medical imaging across 4 subdomains (ophthalmology, radiology, pathology, and dermatology). We identify a set of common practices for ensuring data quality. We describe one instance of low-quality labeling caught by post-launch monitoring. However, the more common pattern is to involve experts in an iterative process of defining, testing, and iterating tasks and instructions. Teams invest in these upstream efforts in order to mitigate downstream quality issues during large-scale labeling.
View details
Preview abstract
The retina can be a source of subtle signs of disease. Yet visual inspection of microvasculature, nerves and connective-tissue structures in the retina has only led to a few hallmarks of disease — most notably, of lesions of diabetic retinopathy — that can be incorporated into clinical guidelines as criteria for screening and diagnosis1. In the past few years, the application of deep learning to the analysis of retinal fundus images has shown that retinal tissue can also reveal information about cardiovascular risk (through clinically relevant risk factors2), and that such trained neural networks can be used to predict retinal-vessel calibre3, coronary artery calcium scores4,5, low blood haemoglobin6, risk of chronic kidney disease7 and a host of systemic parameters, such as body mass index (BMI) and creatinine8. This suggests that deep learning could eventually be implemented clinically to examine a patient’s health and for the health screening of populations, conceivably improving affordability and accessibility. However, at present, the development of deep learning for health-screening purposes is at an early stage, and the vast majority of proof-of-concept work has not yet been clinically validated. Writing in Nature Biomedical Engineering, Kang Zhang, Ting Chen, Tao Xu, Guangyu Wang and colleagues now show that deep-learning models can be used to detect chronic kidney disease (CKD) and type 2 diabetes mellitus (T2DM) solely from retinal fundus photographs (collected using standard table-top fundus cameras) or in conjunction with patient metadata9. Crucially, the researchers validated their findings across multiple geographically distinct patient datasets from China, including a dataset prospectively collected under point-of-care (POC) settings using a custom smartphone-based system.
View details
Large-scale machine learning-based phenotyping significantly improves genomic discovery for optic nerve head morphology
Babak Alipanahi
Babak Behsaz
Zachary Ryan Mccaw
Emanuel Schorsch
Lizzie Dorfman
Sonia Phene
Andrew Walker Carroll
Anthony Khawaja
American Journal of Human Genetics (2021)
Preview abstract
Genome-wide association studies (GWAS) require accurate cohort phenotyping, but expert labeling can be costly, time-intensive, and variable. Here we develop a machine learning (ML) model to predict glaucomatous features from color fundus photographs. We used the model to predict vertical cup-to-disc ratio (VCDR), a diagnostic parameter and cardinal endophenotype for glaucoma, in 65,680 Europeans in the UK Biobank (UKB). A GWAS of ML-based VCDR identified 299 independent genome-wide significant (GWS; P≤5×10-8) hits in 156 loci. The ML-based GWAS replicated 62 of 65 GWS loci from a recent VCDR GWAS in the UKB for which two ophthalmologists manually labeled images for 67,040 Europeans. The ML-based GWAS also identified 93 novel loci, significantly expanding our understanding of the genetic etiologies of glaucoma and VCDR. Pathway analyses support the biological significance of the novel hits to VCDR, with select loci near genes involved in neuronal and synaptic biology or known to cause severe Mendelian ophthalmic disease. Finally, the ML-based GWAS results significantly improve polygenic prediction of VCDR in independent datasets.
View details
Redesigning Clinical Pathways for Immediate Diabetic Retinopathy Screening Results
Elin Rønby Pedersen
Jorge Cuadros
Mahbuba Khan
Sybille Fleischmann
Gregory Wolff
NEJM Catalyst, vol. 2 (2021)
Preview abstract
Regular diabetic retinopathy (DR) screening and early treatment can prevent DR-associated vision loss. However, some DR screening programs within primary care settings have found low rates of patient adherence to referral recommendations, even following a positive screen for vision-threatening DR. The authors hypothesized that adherence can be increased by providing screening results immediately and improving workflows by engaging patients and, when needed, scheduling a follow-up ophthalmology appointment immediately. A long-term goal of this project is to investigate the potential value of an immediate clinical image interpretation provided by artificial intelligence (AI); however, in this study, optometrists simulated AI by providing immediate reads of the fundus images. Immediate interpretation, which formed the basis for counseling and recommendations while the patient was in the office, resulted in significantly improved adherence among patients who received a recommendation to see a specialist within 1 month, from the historical baseline of 35% to 72% (P < .01 after controlling for cohort characteristics). This suggests that providing results and scheduling follow-up appointments immediately following a DR screening test can substantially improve patient adherence and reduce unnecessary vision loss. The changes were adopted widely within the hospital system and even scaled to include referrals to other specialties.
View details
Predicting the risk of developing diabetic retinopathy using deep learning
Ashish Bora
Siva Balasubramanian
Sunny Virmani
Akinori Mitani
Guilherme De Oliveira Marinho
Jorge Cuadros
Dr. Paisan Raumviboonsuk
Lily Hao Yi Peng
Avinash Vaidyanathan Varadarajan
Lancet Digital Health (2020)
Preview abstract
Background: Diabetic retinopathy screening is instrumental to preventing blindness, but scaling up screening is challenging because of the increasing number of patients with all forms of diabetes. We aimed to create a deep-learning system to predict the risk of patients with diabetes developing diabetic retinopathy within 2 years.
Methods: We created and validated two versions of a deep-learning system to predict the development of diabetic retinopathy in patients with diabetes who had had teleretinal diabetic retinopathy screening in a primary care setting. The input for the two versions was either a set of three-field or one-field colour fundus photographs. Of the 575 431 eyes in the development set 28 899 had known outcomes, with the remaining 546 532 eyes used to augment the training process via multitask learning. Validation was done on one eye (selected at random) per patient from two datasets: an internal validation (from EyePACS, a teleretinal screening service in the USA) set of 3678 eyes with known outcomes and an external validation (from Thailand) set of 2345 eyes with known outcomes.
Findings: The three-field deep-learning system had an area under the receiver operating characteristic curve (AUC) of 0·79 (95% CI 0·77–0·81) in the internal validation set. Assessment of the external validation set—which contained only one-field colour fundus photographs—with the one-field deep-learning system gave an AUC of 0·70 (0·67–0·74). In the internal validation set, the AUC of available risk factors was 0·72 (0·68–0·76), which improved to 0·81 (0·77–0·84) after combining the deep-learning system with these risk factors (p<0·0001). In the external validation set, the corresponding AUC improved from 0·62 (0·58–0·66) to 0·71 (0·68–0·75; p<0·0001) following the addition of the deep-learning system to available risk factors.
Interpretation: The deep-learning systems predicted diabetic retinopathy development using colour fundus photographs, and the systems were independent of and more informative than available risk factors. Such a risk stratification tool might help to optimise screening intervals to reduce costs while improving vision-related outcomes.
View details
Preview abstract
Artificial intelligence (AI) methods have become a focus of intense interest within the eye care community. This parallels a wider interest in AI as it has started impacting many facets of society. However, understanding across the community has not kept pace with technical developments. What is AI? How does it relate to other terms like machine learning (ML) or deep learning (DL)? How is AI currently used within eye care, and how might it be used in the future? This review paper provides an overview of these concepts for eye care specialists. We explain core concepts in AI, describe how these methods have been applied in ophthalmology, and consider future directions and challenges. We walk through the steps needed to develop an AI system for eye disease, and discuss the challenges in validating and deploying such technology. We argue that among medical fields, ophthalmology may be uniquely positioned to benefit from the thoughtful deployment of AI to improve patient care.
View details
Remote Tool-based Adjudication for Grading Diabetic Retinopathy
Tayyeba Ali
Brian Basham
Will Chen
Xiang Ji
Lily Peng
Edith Law
Translational Vision Science & Technology (TVST) (2019)
Preview abstract
Purpose: To present and evaluate a remote, tool-based system and structured grading rubric for adjudicating image-based diabetic retinopathy (DR) grades.
Methods: We compared three different procedures for adjudicating DR severity assessments among retina specialist panels, including (1) in-person adjudication based on a previously described procedure (Baseline), (2) remote, tool-based adjudication for assessing DR severity alone (TA), and (3) remote, tool-based adjudication using a feature-based rubric (TA-F). We developed a system allowing graders to review images remotely and asynchronously. For both TA and TA-F approaches, images with disagreement were reviewed by all graders in a round-robin fashion until disagreements were resolved. Five panels of three retina specialists each adjudicated a set of 499 retinal fundus images (1 panel using Baseline, 2 using TA, and 2 using TA-F adjudication). Reliability was measured as grade agreement among the panels using Cohen's quadratically weighted kappa. Efficiency was measured as the number of rounds needed to reach a consensus for tool-based adjudication.
Results: The grades from remote, tool-based adjudication showed high agreement with the Baseline procedure, with Cohen's kappa scores of 0.948 and 0.943 for the two TA panels, and 0.921 and 0.963 for the two TA-F panels. Cases adjudicated using TA-F were resolved in fewer rounds compared with TA (P < 0.001; standard permutation test).
Conclusions: Remote, tool-based adjudication presents a flexible and reliable alternative to in-person adjudication for DR diagnosis. Feature-based rubrics can help accelerate consensus for tool-based adjudication of DR without compromising label quality.
Translational Relevance: This approach can generate reference standards to validate automated methods, and resolve ambiguous diagnoses by integrating into existing telemedical workflows.
View details
Using a deep learning algorithm and integrated gradient explanation to assist grading for diabetic retinopathy
Ankur Taly
Anthony Joseph
Arjun Sood
Arun Narayanaswamy
Derek Wu
Ehsan Rahimy
Jesse Smith
Katy Blumer
Lily Peng
Michael Shumski
Scott Barb
Zahra Rastegar
Ophthalmology (2019)
Preview abstract
Background Deep learning methods have recently produced algorithms that can detect disease such as diabetic retinopathy (DR) with doctor-level accuracy. We sought to understand the impact of these models on physician graders in assisted-read settings.
Methods We surfaced model predictions and explanation maps ("masks") to 9 ophthalmologists with varying levels of experience to read 1,804 images each for DR severity based on the International Clinical Diabetic Retinopathy (ICDR) disease severity scale. The image sample was representative of the diabetic screening population, and was adjudicated by 3 retina specialists for a reference standard. Doctors read each image in one of 3 conditions: Unassisted, Grades Only, or Grades+Masks.
Findings Readers graded DR more accurately with model assistance than without (p < 0.001, logistic regression). Compared to the adjudicated reference standard, for cases with disease, 5-class accuracy was 57.5% for the model. For graders, 5-class accuracy for cases with disease was 47.5 ± 5.6% unassisted, 56.9 ± 5.5% with Grades Only, and 61.5 ± 5.5% with Grades+Mask. Reader performance improved with assistance across all levels of DR, including for severe and proliferative DR. Model assistance increased the accuracy of retina fellows and trainees above that of the unassisted grader or model alone. Doctors’ grading confidence scores and read times both increased overall with assistance. For most cases, Grades + Masks was as only effective as Grades Only, though masks provided additional benefit over grades alone in cases with: some DR and low model certainty; low image quality; and proliferative diabetic retinopathy (PDR) with features that were frequently missed, such as panretinal photocoagulation (PRP) scars.
Interpretation Taken together, these results show that deep learning models can improve the accuracy of, and confidence in, DR diagnosis in an assisted read setting.
View details
Deep Learning and Glaucoma Specialists: The Relative Importance of Optic Disc Features to Predict Glaucoma Referral in Fundus Photographs
Sonia Phene
Carter Dunn
Naho Kitade
Derek Wu
Ashish Bora
Anita Misra
Abigail Huang
Arielle Spitze
Felipe Medeiros
April Maa
Monica Gandhi
Lily Peng
Ophthalmology (2019)
Preview abstract
Purpose
To develop and validate a deep learning (DL) algorithm that predicts referable glaucomatous optic neuropathy (GON) and optic nerve head (ONH) features from color fundus images, to determine the relative importance of these features in referral decisions by glaucoma specialists (GSs) and the algorithm, and to compare the performance of the algorithm with eye care providers.
Design
Development and validation of an algorithm.
Participants
Fundus images from screening programs, studies, and a glaucoma clinic.
Methods
A DL algorithm was trained using a retrospective dataset of 86 618 images, assessed for glaucomatous ONH features and referable GON (defined as ONH appearance worrisome enough to justify referral for comprehensive examination) by 43 graders. The algorithm was validated using 3 datasets: dataset A (1205 images, 1 image/patient; 18.1% referable), images adjudicated by panels of GSs; dataset B (9642 images, 1 image/patient; 9.2% referable), images from a diabetic teleretinal screening program; and dataset C (346 images, 1 image/patient; 81.7% referable), images from a glaucoma clinic.
Main Outcome Measures
The algorithm was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity for referable GON and glaucomatous ONH features.
Results
The algorithm’s AUC for referable GON was 0.945 (95% confidence interval [CI], 0.929–0.960) in dataset A, 0.855 (95% CI, 0.841–0.870) in dataset B, and 0.881 (95% CI, 0.838–0.918) in dataset C. Algorithm AUCs ranged between 0.661 and 0.973 for glaucomatous ONH features. The algorithm showed significantly higher sensitivity than 7 of 10 graders not involved in determining the reference standard, including 2 of 3 GSs, and showed higher specificity than 3 graders (including 1 GS), while remaining comparable to others. For both GSs and the algorithm, the most crucial features related to referable GON were: presence of vertical cup-to-disc ratio of 0.7 or more, neuroretinal rim notching, retinal nerve fiber layer defect, and bared circumlinear vessels.
Conclusions
A DL algorithm trained on fundus images alone can detect referable GON with higher sensitivity than and comparable specificity to eye care providers. The algorithm maintained good performance on an independent dataset with diagnoses based on a full glaucoma workup.
View details
Predicting Progression of Age-related Macular Degeneration from Fundus Images using Deep Learning
Siva Balasubramanian
Katy Blumer
Lily Peng
Avinash Vaidyanathan Varadarajan
arXiv (2019)
Preview abstract
Background: Patients with neovascular age-related macular degeneration (AMD) can avoid vision loss via certain therapy. However, methods to predict the progression to neovascular age-related macular degeneration (nvAMD) are lacking. Purpose: To develop and validate a deep learning (DL) algorithm to predict 1-year progression of eyes with no, early, or intermediate AMD to nvAMD, using color fundus photographs (CFP). Design: Development and validation of a DL algorithm. Methods: We trained a DL algorithm to predict 1-year progression to nvAMD, and used 10-fold cross-validation to evaluate this approach on two groups of eyes in the Age-Related Eye Disease Study (AREDS): none/early/intermediate AMD, and intermediate AMD (iAMD) only. We compared the DL algorithm to the manually graded 4-category and 9-step scales in the AREDS dataset. Main outcome measures: Performance of the DL algorithm was evaluated using the sensitivity at 80% specificity for progression to nvAMD. Results: The DL algorithm's sensitivity for predicting progression to nvAMD from none/early/iAMD (78+/-6%) was higher than manual grades from the 9-step scale (67+/-8%) or the 4-category scale (48+/-3%). For predicting progression specifically from iAMD, the DL algorithm's sensitivity (57+/-6%) was also higher compared to the 9-step grades (36+/-8%) and the 4-category grades (20+/-0%). Conclusions: Our DL algorithm performed better in predicting progression to nvAMD than manual grades. Future investigations are required to test the application of this DL algorithm in a real-world clinical setting.
View details
Predicting Anemia from Fundus Images
Akinori Mitani
Abigail Huang
Lily Peng
Avinash Vaidyanathan Varadarajan
Nature Biomedical Engineering (2019)
Preview abstract
Owing to the invasiveness of diagnostic tests for anaemia and the costs associated with screening for it, the condition is often undetected. Here, we show that anaemia can be detected via machine-learning algorithms trained using retinal fundus images, study participant metadata (including race or ethnicity, age, sex and blood pressure) or the combination of both data types (images and study participant metadata). In a validation dataset of 11,388 study participants from the UK Biobank, the fundusimage-only, metadata-only and combined models predicted haemoglobin concentration (in g dl–1) with mean absolute error values of 0.73 (95% confidence interval: 0.72–0.74), 0.67 (0.66–0.68) and 0.63 (0.62–0.64), respectively, and with areas under the receiver operating characteristic curve (AUC) values of 0.74 (0.71–0.76), 0.87 (0.85–0.89) and 0.88 (0.86–0.89), respectively. For 539 study participants with self-reported diabetes, the combined model predicted haemoglobin concentration with a mean absolute error of 0.73 (0.68–0.78) and anaemia an AUC of 0.89 (0.85–0.93). Automated anaemia screening on the basis of fundus images could particularly aid patients with diabetes undergoing regular retinal imaging and for whom anaemia can increase morbidity and mortality risks.
View details
No Results Found
