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Oliver Davidson,1,2 Anand E Rajesh,1,2 Marian Blazes,1,2 Ashley Batchelor,1,2 Aaron Y Lee,1,2 Cecilia S Lee,1,2,&ast; Laura C Huang1,3,&ast; On behalf of the IRIS® Registry Analytic Center Consortium1Department of Ophthalmology, University of Washington, Seattle, WA, USA; 2Roger and Angie Karalis Johnson Retina Center, Seattle, WA, USA; 3Division of Ophthalmology, Seattle Children’s Hospital, Seattle, WA, USA&ast;These authors contributed equally to this workCorrespondence: Laura C Huang, Department of Ophthalmology, Seattle Children’s Hospital and University of Washington, OA.9.220, 4800 Sand Point Way NE, Seattle, WA, USA, Tel +1 206-987-2177, Email lchuang1@uw.eduPurpose: Understanding sociodemographic factors associated with poor visual outcomes in children with juvenile idiopathic arthritis-associated uveitis may help inform practice patterns.Patients and Methods: Retrospective cohort study on patients < 18 years old who were diagnosed with both juvenile idiopathic arthritis and uveitis based on International Classification of Diseases tenth edition codes in the Intelligent Research in Sight Registry through December 2020. Surgical history was extracted using current procedural terminology codes. The primary outcome was incidence of blindness (20/200 or worse) in at least one eye in association with sociodemographic factors. Secondary outcomes included cataract and glaucoma surgery following uveitis diagnosis. Hazard ratios were calculated using multivariable-adjusted Cox proportional hazards models.Results: Median age of juvenile idiopathic arthritis-associated uveitis diagnosis was 11 (Interquartile Range: 8 to 15). In the Cox models adjusting for sociodemographic and insurance factors, the hazard ratios of best corrected visual acuity 20/200 or worse were higher in males compared to females (HR 2.15; 95% CI: 1.45– 3.18), in Black or African American patients compared to White patients (2.54; 1.44– 4.48), and in Medicaid-insured patients compared to commercially-insured patients (2.23; 1.48– 3.37).Conclusion: Sociodemographic factors and insurance coverage were associated with varying levels of risk for poor visual outcomes in children with juvenile idiopathic arthritis-associated uveitis.Keywords: rheumatology, ophthalmology, uveitis, health inequity, social determinants of health

Purpose: Both retinal changes and age-related macular degeneration (AMD) have been shown to be associated with Alzheimer's disease and related dementias (ADRD). In AMD, the outer retina is impacted significantly and early, but little is known about its association with cognition or changes in brain morphometry. This study investigates the relationship between retinal and brain morphometry in older adults with early and intermediate age-related macular degeneration (AMD)., Design: Cross-sectional study., Methods: Adults ≥ 70 years with normal, early, and intermediate AMD were recruited from Callahan Eye Hospital Clinics at the University of Alabama at Birmingham. Participants underwent cognitive testing, optical coherence tomography (OCT), and magnetic resonance imaging (MRI). Associations of retinal layer thickness with brain volume and thickness of specific brain regions were evaluated utilizing multivariable linear regression. The relevance of retinal thickness variables in brain volumetrics was quantified using least absolute shrinkage and selection operator (LASSO) regression models. Correlations between demographic variables, cognitive scores, and brain morphometry were evaluated., Results: Participants with thinner outer retina had significantly smaller hippocampus (β = 0.019, p = 0.022), lower occipital cortex regions of interest (occipital ROIs) thickness (β = 5.68, p = 0.020), and lower cortical thickness in ADRD-related brain regions (β = 7.72, p = 0.006). People with thinner total retina had significantly lower occipital ROIs (β = 3.19, p = 0.009) and ADRD-related brain region (β = 3.94, p = 0.005) thickness. Outer retinal thickness in the outer Early Treatment of Diabetic Retinopathy Study (ETDRS) ring was the most frequently reported retinal variable associated with brain morphometry on LASSO regression. Total gray matter volume showed positive correlations with education (Pearson's r = 0.30, p = 0.022)., Conclusion: In older adults with normal retinal aging and early and intermediate AMD, thinner outer retina had specific associations with brain regions primarily involved in vision and cognition, such as lower hippocampal volume and lower thickness of the occipital ROIs and brain regions known to show early structural changes in dementia., Competing Interests: Declaration of competing interest C.O.: Johnson & Johnson Vision; L.G.: BioEQ, Formycon; CAC: consults for Genentech/ Hoffman LaRoche, Apellis, Astellas, Boehringer Ingelheim, Character Biosciences, Osanni, Annexon, Mobius, and Ripple (outside this project)., (Copyright © 2024. Published by Elsevier Inc.)

Purpose: To apply methods for quantifying uncertainty of deep learning segmentation of geographic atrophy (GA)., Design: Retrospective analysis of OCT images and model comparison., Participants: One hundred twenty-six eyes from 87 participants with GA in the SWAGGER cohort of the Nonexudative Age-Related Macular Degeneration Imaged with Swept-Source OCT (SS-OCT) study., Methods: The manual segmentations of GA lesions were conducted on structural subretinal pigment epithelium en face images from the SS-OCT images. Models were developed for 2 approximate Bayesian deep learning techniques, Monte Carlo dropout and ensemble, to assess the uncertainty of GA semantic segmentation and compared to a traditional deep learning model., Main Outcome Measures: Model performance (Dice score) was compared. Uncertainty was calculated using the formula for Shannon Entropy., Results: The output of both Bayesian technique models showed a greater number of pixels with high entropy than the standard model. Dice scores for the Monte Carlo dropout method (0.90, 95% confidence interval 0.87-0.93) and the ensemble method (0.88, 95% confidence interval 0.85-0.91) were significantly higher ( P  < 0.001) than for the traditional model (0.82, 95% confidence interval 0.78-0.86)., Conclusions: Quantifying the uncertainty in a prediction of GA may improve trustworthiness of the models and aid clinicians in decision-making. The Bayesian deep learning techniques generated pixel-wise estimates of model uncertainty for segmentation, while also improving model performance compared with traditionally trained deep learning models., Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article., (© 2024 by the American Academy of Ophthalmology.)

The aim of this perspective is to promote the theory of salutogenesis as a novel approach to addressing ophthalmologic inflammatory conditions, illustrating several concepts in which it is based upon and how they can be applied to medical practice. This theory can better contextualize why patients with similar demographics and exposures are not uniform in their clinical presentations. Stressors in daily life can contribute to a state of ill-health and there are various factors that help alleviate their negative impact. These alleviating factors are significantly impaired in people with poor vision, one of the most common presentations of ophthalmologic conditions. Salutogenic principles can guide the treatment of eye conditions to be more respectful of patient autonomy amidst shifting expectations of the doctor-patient relationship. Being able to take ownership of their health and feeling that their cultural beliefs were considered improves compliance and subsequently gives more optimal outcomes. Population-level policy interventions could also utilize salutogenic principles to identify previously overlooked domains that can be addressed. We identified several papers about salutogenesis in an ophthalmological context and acknowledged the relatively few studies on this topic at present and offer directions in which we can explore further in subsequent studies.

Data is the cornerstone of using AI models, because their performance directly depends on the diversity, quantity, and quality of the data used for training. Using AI presents unique potential, particularly in medical applications that involve rich data such as ophthalmology, encompassing a variety of imaging methods, medical records, and eye-tracking data. However, sharing medical data comes with challenges because of regulatory issues and privacy concerns. This review explores traditional and nontraditional data sharing methods in medicine, focusing on previous works in ophthalmology. Traditional methods involve direct data transfer, whereas newer approaches prioritize security and privacy by sharing derived datasets, creating secure research environments, or using model-to-data strategies. We examine each method's mechanisms, variations, recent applications in ophthalmology, and their respective advantages and disadvantages. By empowering medical researchers with insights into data sharing methods and considerations, this review aims to assist informed decision-making while upholding ethical standards and patient privacy in medical AI development.

Purpose: Understanding sociodemographic factors associated with poor visual outcomes in children with juvenile idiopathic arthritis-associated uveitis may help inform practice patterns., Patients and Methods: Retrospective cohort study on patients <18 years old who were diagnosed with both juvenile idiopathic arthritis and uveitis based on International Classification of Diseases tenth edition codes in the Intelligent Research in Sight Registry through December 2020. Surgical history was extracted using current procedural terminology codes. The primary outcome was incidence of blindness (20/200 or worse) in at least one eye in association with sociodemographic factors. Secondary outcomes included cataract and glaucoma surgery following uveitis diagnosis. Hazard ratios were calculated using multivariable-adjusted Cox proportional hazards models., Results: Median age of juvenile idiopathic arthritis-associated uveitis diagnosis was 11 (Interquartile Range: 8 to 15). In the Cox models adjusting for sociodemographic and insurance factors, the hazard ratios of best corrected visual acuity 20/200 or worse were higher in males compared to females (HR 2.15; 95% CI: 1.45-3.18), in Black or African American patients compared to White patients (2.54; 1.44-4.48), and in Medicaid-insured patients compared to commercially-insured patients (2.23; 1.48-3.37)., Conclusion: Sociodemographic factors and insurance coverage were associated with varying levels of risk for poor visual outcomes in children with juvenile idiopathic arthritis-associated uveitis., Competing Interests: Dr. A. Lee reports support from the US Food and Drug Administration, grants from Amazon, Carl Zeiss Meditec, iCareWorld, Meta, Microsoft, Novartis, NVIDIA, Regeneron, Santen Pharmaceutical, and Topcon, personal fees from Alcon, Boehringer Ingelheim, Genentech/Roche, Gyroscope, Janssen, Johnson & Johnson, and Verana Health outside of the submitted work, and nonfinancial support from Microsoft, Optomed, and Heidelberg, outside of the submitted work. Dr. C. Lee reports personal fees from Boehringer Ingelheim. This article does not reflect the opinions of the Food and Drug Administration. The authors report no other conflicts of interest in this work., (© 2024 Davidson et al.)

Importance: Diagnostic information from administrative claims and electronic health record (EHR) data may serve as an important resource for surveillance of vision and eye health, but the accuracy and validity of these sources are unknown., Objective: To estimate the accuracy of diagnosis codes in administrative claims and EHRs compared to retrospective medical record review., Design, Setting, and Participants: This cross-sectional study compared the presence and prevalence of eye disorders based on diagnostic codes in EHR and claims records vs clinical medical record review at University of Washington-affiliated ophthalmology or optometry clinics from May 2018 to April 2020. Patients 16 years and older with an eye examination in the previous 2 years were included, oversampled for diagnosed major eye diseases and visual acuity loss., Exposures: Patients were assigned to vision and eye health condition categories based on diagnosis codes present in their billing claims history and EHR using the diagnostic case definitions of the US Centers for Disease Control and Prevention Vision and Eye Health Surveillance System (VEHSS) as well as clinical assessment based on retrospective medical record review., Main Outcome and Measures: Accuracy was measured as area under the receiver operating characteristic curve (AUC) of claims and EHR-based diagnostic coding vs retrospective review of clinical assessments and treatment plans., Results: Among 669 participants (mean [range] age, 66.1 [16-99] years; 357 [53.4%] female), identification of diseases in billing claims and EHR data using VEHSS case definitions was accurate for diabetic retinopathy (claims AUC, 0.94; 95% CI, 0.91-0.98; EHR AUC, 0.97; 95% CI, 0.95-0.99), glaucoma (claims AUC, 0.90; 95% CI, 0.88-0.93; EHR AUC, 0.93; 95% CI, 0.90-0.95), age-related macular degeneration (claims AUC, 0.87; 95% CI, 0.83-0.92; EHR AUC, 0.96; 95% CI, 0.94-0.98), and cataracts (claims AUC, 0.82; 95% CI, 0.79-0.86; EHR AUC, 0.91; 95% CI, 0.89-0.93). However, several condition categories showed low validity with AUCs below 0.7, including diagnosed disorders of refraction and accommodation (claims AUC, 0.54; 95% CI, 0.49-0.60; EHR AUC, 0.61; 95% CI, 0.56-0.67), diagnosed blindness and low vision (claims AUC, 0.56; 95% CI, 0.53-0.58; EHR AUC, 0.57; 95% CI, 0.54-0.59), and orbital and external diseases (claims AUC, 0.63; 95% CI, 0.57-0.69; EHR AUC, 0.65; 95% CI, 0.59-0.70)., Conclusion and Relevance: In this cross-sectional study of current and recent ophthalmology patients with high rates of eye disorders and vision loss, identification of major vision-threatening eye disorders based on diagnosis codes in claims and EHR records was accurate. However, vision loss, refractive error, and other broadly defined or lower-risk disorder categories were less accurately identified by diagnosis codes in claims and EHR data.

Purpose: To investigate whether associations between diabetic retinopathy (DR) and dementia and Alzheimer's disease (AD) remain significant after controlling for several measures of diabetes severity., Design: Retrospective cohort study., Methods: Adult Changes in Thought (ACT) is a prospective cohort study of adults aged ≥65 years, randomly selected and recruited from the membership rolls of Kaiser Permanente Washington, who are dementia free at enrollment and followed biennially until incident dementia. The ACT participants were included in this study if they had type 2 diabetes mellitus at enrollment or developed it during follow-up, and data were collected through September, 2018 (3516 person-years of follow-up). Diabetes was defined by ≥ 2 diabetes medication fills in 1 year. Diagnosis of DR was based on International Classification of Diseases Ninth and Tenth Revision codes. Estimates of microalbuminuria, long-term glycemia, and renal function from longitudinal laboratory records were used as indicators of diabetes severity. Alzheimer's disease and dementia were diagnosed using research criteria at expert consensus meetings., Results: A total of 536 participants (median baseline age 75 [interquartile range 71-80], 54% women) met inclusion criteria. Significant associations between DR >5 years duration with dementia (hazard ratio 1.81 [95% CI 1.23, 2.65]) and AD (1.80 [1.15, 2.82]) were not altered by adjustment for estimates of microalbuminuria, long-term glycemia, and renal function (dementia: 1.69 [1.14, 2.50]; AD: 1.73 [1.10, 2.74])., Conclusions: Among people with type 2 diabetes, DR itself appears to be an important biomarker of dementia risk in addition to glycemia and renal complications., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Background: Self-reported questions on blindness and vision problems are collected in many national surveys. Recently released surveillance estimates on the prevalence of vision loss used self-reported data to predict variation in the prevalence of objectively measured acuity loss among population groups for whom examination data are not available. However, the validity of self-reported measures to predict prevalence and disparities in visual acuity has not been established., Objective: This study aimed to estimate the diagnostic accuracy of self-reported vision loss measures compared to best-corrected visual acuity (BCVA), inform the design and selection of questions for future data collection, and identify the concordance between self-reported vision and measured acuity at the population level to support ongoing surveillance efforts., Methods: We calculated accuracy and correlation between self-reported visual function versus BCVA at the individual and population level among patients from the University of Washington ophthalmology or optometry clinics with a prior eye examination, randomly oversampled for visual acuity loss or diagnosed eye diseases. Self-reported visual function was collected via telephone survey. BCVA was determined based on retrospective chart review. Diagnostic accuracy of questions at the person level was measured based on the area under the receiver operator curve (AUC), whereas population-level accuracy was determined based on correlation., Results: The survey question, "Are you blind or do you have serious difficulty seeing, even when wearing glasses?" had the highest accuracy for identifying patients with blindness (BCVA ≤20/200; AUC=0.797). The highest accuracy for detecting any vision loss (BCVA <20/40) was achieved by responses of "fair," "poor," or "very poor" to the question, "At the present time, would you say your eyesight, with glasses or contact lenses if you wear them, is excellent, good, fair, poor, or very poor" (AUC=0.716). At the population level, the relative relationship between prevalence based on survey questions and BCVA remained stable for most demographic groups, with the only exceptions being groups with small sample sizes, and these differences were generally not significant., Conclusions: Although survey questions are not considered to be sufficiently accurate to be used as a diagnostic test at the individual level, we did find relatively high levels of accuracy for some questions. At the population level, we found that the relative prevalence of the 2 most accurate survey questions were highly correlated with the prevalence of measured visual acuity loss among nearly all demographic groups. The results of this study suggest that self-reported vision questions fielded in national surveys are likely to yield an accurate and stable signal of vision loss across different population groups, although the actual measure of prevalence from these questions is not directly analogous to that of BCVA., (©John Wittenborn, Aaron Lee, Elizabeth A Lundeen, Phoebe Lamuda, Jinan Saaddine, Grace L Su, Randy Lu, Aashka Damani, Jonathan S Zawadzki, Colin P Froines, Jolie Z Shen, Timothy-Paul H Kung, Ryan T Yanagihara, Morgan Maring, Melissa M Takahashi, Marian Blazes, David B Rein. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 07.03.2023.)

Purpose: To create an unsupervised cross-domain segmentation algorithm for segmenting intraretinal fluid and retinal layers on normal and pathologic macular OCT images from different manufacturers and camera devices., Design: We sought to use generative adversarial networks (GANs) to generalize a segmentation model trained on one OCT device to segment B-scans obtained from a different OCT device manufacturer in a fully unsupervised approach without labeled data from the latter manufacturer., Participants: A total of 732 OCT B-scans from 4 different OCT devices (Heidelberg Spectralis, Topcon 1000, Maestro2, and Zeiss Plex Elite 9000)., Methods: We developed an unsupervised GAN model, GANSeg, to segment 7 retinal layers and intraretinal fluid in Topcon 1000 OCT images (domain B) that had access only to labeled data on Heidelberg Spectralis images (domain A). GANSeg was unsupervised because it had access only to 110 Heidelberg labeled OCTs and 556 raw and unlabeled Topcon 1000 OCTs. To validate GANSeg segmentations, 3 masked graders manually segmented 60 OCTs from an external Topcon 1000 test dataset independently. To test the limits of GANSeg, graders also manually segmented 3 OCTs from Zeiss Plex Elite 9000 and Topcon Maestro2. A U-Net was trained on the same labeled Heidelberg images as baseline. The GANSeg repository with labeled annotations is at https://github.com/uw-biomedical-ml/ganseg., Main Outcome Measures: Dice scores comparing segmentation results from GANSeg and the U-Net model with the manual segmented images., Results: Although GANSeg and U-Net achieved comparable Dice scores performance as human experts on the labeled Heidelberg test dataset, only GANSeg achieved comparable Dice scores with the best performance for the ganglion cell layer plus inner plexiform layer (90%; 95% confidence interval [CI], 68%-96%) and the worst performance for intraretinal fluid (58%; 95% CI, 18%-89%), which was statistically similar to human graders (79%; 95% CI, 43%-94%). GANSeg significantly outperformed the U-Net model. Moreover, GANSeg generalized to both Zeiss and Topcon Maestro2 swept-source OCT domains, which it had never encountered before., Conclusions: GANSeg enables the transfer of supervised deep learning algorithms across OCT devices without labeled data, thereby greatly expanding the applicability of deep learning algorithms., (Copyright © 2022 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.)

COVID-19 mortality risk stratification tools could improve care, inform accurate and rapid triage decisions, and guide family discussions regarding goals of care. A minority of COVID-19 prognostic tools have been tested in external cohorts. Our objective was to compare machine learning algorithms and develop a tool for predicting subsequent clinical outcomes in COVID-19. We conducted a retrospective cohort study that included hospitalized patients with COVID-19 from March 2020 to March 2021. Seven Hundred Twelve consecutive patients from University of Washington and 345 patients from Tongji Hospital in China were included. We applied three different machine learning algorithms to clinical and laboratory data collected within the initial 24 h of hospital admission to determine the risk of in-hospital mortality, transfer to the intensive care unit, shock requiring vasopressors, and receipt of renal replacement therapy. Mortality risk models were derived, internally validated in UW and externally validated in Tongji Hospital. The risk models for ICU transfer, shock and RRT were derived and internally validated in the UW dataset but were unable to be externally validated due to a lack of data on these outcomes. Among the UW dataset, 122 patients died (17%) during hospitalization and the mean days to hospital mortality was 15.7 +/- 21.5 (mean +/- SD). Elastic net logistic regression resulted in a C-statistic for in-hospital mortality of 0.72 (95% CI, 0.64 to 0.81) in the internal validation and 0.85 (95% CI, 0.81 to 0.89) in the external validation set. Age, platelet count, and white blood cell count were the most important predictors of mortality. In the sub-group of patients > 50 years of age, the mortality prediction model continued to perform with a C-statistic of 0.82 (95% CI:0.76,0.87). Prediction models also performed well for shock and RRT in the UW dataset but functioned with lower accuracy for ICU transfer. We trained, internally and externally validated a prediction model using data collected within 24 h of hospital admission to predict in-hospital mortality on average two weeks prior to death. We also developed models to predict RRT and shock with high accuracy. These models could be used to improve triage decisions, resource allocation, and support clinical trial enrichment., (© 2022. The Author(s).)

In response to the COVID-19 global pandemic, recent research has proposed creating deep learning based models that use chest radiographs (CXRs) in a variety of clinical tasks to help manage the crisis. However, the size of existing datasets of CXRs from COVID-19+ patients are relatively small, and researchers often pool CXR data from multiple sources, for example, using different x-ray machines in various patient populations under different clinical scenarios. Deep learning models trained on such datasets have been shown to overfit to erroneous features instead of learning pulmonary characteristics in a phenomenon known as shortcut learning. We propose adding feature disentanglement to the training process. This technique forces the models to identify pulmonary features from the images and penalizes them for learning features that can discriminate between the original datasets that the images come from. We find that models trained in this way indeed have better generalization performance on unseen data; in the best case we found that it improved AUC by 0.13 on held out data. We further find that this outperforms masking out non-lung parts of the CXRs and performing histogram equalization, both of which are recently proposed methods for removing biases in CXR datasets., Competing Interests: The authors have read the journal’s policy and have the following competing interests to declare: AYL reports support from the US Food and Drug Administration, grants from Santen, Regeneron, Carl Zeiss Meditec, and Novartis, and personal fees from Genentech, Roche, Verana Health, and Johnson and Johnson, outside of the submitted work. This article does not reflect the opinions of the Food and Drug Administration. CR, AT, AO, RD, and JMLF are paid employees of the Microsoft Corporation. JMLF also receives personal fees from Singularity University as a speaker. JD and SG are paid employees of IRIS. In addition, SG is a consultant/advisor for Alcon Laboratories, Allergan, Inc., Andrews Institute, GENENTECH, Novartis, Alcon Pharmaceuticals, Regeneron Pharmaceuticals, Inc., Roche Diagnostics, and Spark Therapeutics, Inc. as well as an equity owner in Intelligent Retinal Imaging Systems (IRIS), Retina Specialty Institute, and USRetina. This does not alter the authors’ adherence to PLOS ONE policies on sharing data and materials. There are no patents, products in development or marketed products associated with this research to declare. MB, PKB, WCL, and JKC have no relevant financial or non-financial interests to disclose.

The objective grading of anterior chamber inflammation (ACI) has remained a challenge in the field of uveitis. While the grading criteria produced by the Standardization of Uveitis Nomenclature (SUN) International Workshop have been widely adopted, limitations exist including interobserver variability and grading confined to discrete categories rather than a continuous measurement. Since the earliest iterations of optical coherence tomography (OCT), ACI has been assessed using anterior segment OCT and shown to correlate with slit-lamp findings. However, widespread use of this approach has not been adopted. Barriers to standardization include variability in OCT devices across clinical settings, lack of standardization of image acquisition protocols, varying quantification methods, and the difficulty of distinguishing inflammatory cells from other cell types. Modern OCT devices and techniques in artificial intelligence show promise in expanding the clinical applicability of anterior segment OCT for the grading of ACI.

The rapid evolution of the novel coronavirus disease (COVID-19) pandemic has resulted in an urgent need for effective clinical tools to reduce transmission and manage severe illness. Numerous teams are quickly developing artificial intelligence approaches to these problems, including using deep learning to predict COVID-19 diagnosis and prognosis from chest computed tomography (CT) imaging data. In this work, we assess the value of aggregated chest CT data for COVID-19 prognosis compared to clinical metadata alone. We develop a novel patient-level algorithm to aggregate the chest CT volume into a 2D representation that can be easily integrated with clinical metadata to distinguish COVID-19 pneumonia from chest CT volumes from healthy participants and participants with other viral pneumonia. Furthermore, we present a multitask model for joint segmentation of different classes of pulmonary lesions present in COVID-19 infected lungs that can outperform individual segmentation models for each task. We directly compare this multitask segmentation approach to combining feature-agnostic volumetric CT classification feature maps with clinical metadata for predicting mortality. We show that the combination of features derived from the chest CT volumes improve the AUC performance to 0.80 from the 0.52 obtained by using patients' clinical data alone. These approaches enable the automated extraction of clinically relevant features from chest CT volumes for risk stratification of COVID-19 patients., (© 2022. The Author(s).)

Purpose: To compare the rate of postoperative endophthalmitis after immediately sequential bilateral cataract surgery (ISBCS) versus delayed sequential bilateral cataract surgery (DSBCS) using the American Academy of Ophthalmology Intelligent Research in Sight (IRIS®) Registry database., Design: Retrospective cohort study., Participants: Patients in the IRIS Registry who underwent cataract surgery from 2013 through 2018., Methods: Patients who underwent cataract surgery were divided into 2 groups: (1) ISBCS and (2) DSBCS (second-eye surgery ≥1 day after the first-eye surgery) or unilateral surgery. Postoperative endophthalmitis was defined as endophthalmitis occurring within 4 weeks of surgery by International Classification of Diseases (ICD) code and ICD code with additional clinical criteria., Main Outcome Measures: Rate of postoperative endophthalmitis., Results: Of 5 573 639 IRIS Registry patients who underwent cataract extraction, 165 609 underwent ISBCS, and 5 408 030 underwent DSBCS or unilateral surgery (3 695 440 DSBCS, 1 712 590 unilateral surgery only). A total of 3102 participants (0.056%) met study criteria of postoperative endophthalmitis with supporting clinical findings. The rates of endophthalmitis in either surgery eye between the 2 surgery groups were similar (0.059% in the ISBCS group vs. 0.056% in the DSBCS or unilateral group; P = 0.53). Although the incidence of endophthalmitis was slightly higher in the ISBCS group compared with the DSBCS or unilateral group, the odds ratio did not reach statistical significance (1.08; 95% confidence interval, 0.87-1.31; P = 0.47) after adjusting for age, sex, race, insurance status, and comorbid eye disease. Seven cases of bilateral endophthalmitis with supporting clinical data in the DSBCS group and no cases in the ISBCS group were identified., Conclusions: Risk of postoperative endophthalmitis was not statistically significantly different between patients who underwent ISBCS and DSBCS or unilateral cataract surgery., (Copyright © 2021 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.)

People who have Alzheimer's disease neuropathologic change (ADNC) typically associated with dementia but not the associated cognitive decline can be considered to be "resilient" to the effects of ADNC. We have previously reported lower neocortical levels of hyperphosphorylated tau (pTau) and less limbic-predominant age-related TDP-43 encephalopathy neuropathologic change (LATE-NC) in the resilient participants compared to those with dementia and similar ADNC as determined by current NIA-AA recommendations using traditional semi-quantitative assessments of amyloid β and pathological tau burden. To better understand differences between AD-dementia and resilient participants, we developed and applied a deep learning approach to analyze the neuropathology of 14 brain donors from the Adult Changes in Thought study, including seven stringently defined resilient participants and seven age-matched AD-dementia controls. We created two novel, fully automated deep learning algorithms to quantify the level of phosphorylated TDP-43 (pTDP-43) and pTau in whole slide imaging. The models performed better than traditional techniques for quantifying pTDP-43 and pTau. The second model was able to segment lesions staining for pTau into neurofibrillary tangles (NFTs) and tau neurites (neuronal processes positive for pTau). Both groups had similar quantities of pTau localizing to neurites, but the pTau burden associated with NFTs in the resilient group was significantly lower compared to the group with dementia. These results validate use of deep learning approaches to quantify clinically relevant microscopic characteristics from neuropathology workups. These results also suggest that the burden of NFTs is more strongly associated with cognitive impairment than the more diffuse neuritic tau commonly seen with tangle pathology and suggest that additional factors may underlie resilience mechanisms defined by traditional means., (© 2021 The Authors. Brain Pathology published by John Wiley & Sons Ltd on behalf of International Society of Neuropathology.)

Purpose: To develop a deep learning semantic segmentation network to automate the assessment of 8 periorbital measurements DESIGN: Development and validation of an artificial intelligence (AI) segmentation algorithm METHODS: A total of 418 photographs of periorbital areas were used to train a deep learning semantic segmentation model to segment iris, aperture, and brow areas. These data were used to develop a post-processing algorithm that measured margin reflex distance (MRD) 1 and 2, medial canthal height (MCH), lateral canthal height (LCH), medial brow height (MBH), lateral brow height (LBH), medial intercanthal distance (MID), and lateral intercanthal distance (LID). The algorithm validity was evaluated on a prospective hold-out test set against 3 graders. The main outcome measures were dice coefficient, mean absolute difference, intraclass correlation coefficient, and Bland-Altman analysis. A smartphone video was also segmented and evaluated as proof of concept., Results: The AI algorithm performed in close agreement with all human graders, with a mean absolute difference of 0.5 mm for MRD1, MRD2, LCH, and MCH. The mean absolute difference between graders is approximately 1.5-2 mm for LBH and MBH and approximately 2-4 mm for MID and LID. The 95% confidence intervals for all graders overlapped in most cases, demonstrating that the algorithm performs similarly to human graders. The segmentation of a smartphone video demonstrated that MRD1 can be dynamically measured., Conclusions: We present, to our knowledge, the first open-sourced, artificial intelligence system capable of automating static and dynamic periorbital measurements. A fully automated tool stands to transform the delivery of clinical care and quantification of surgical outcomes., (Copyright © 2021. Published by Elsevier Inc.)

This work explores a student-teacher framework that leverages unlabeled images to train lightweight deep learning models with fewer parameters to perform fast automated detection of optical coherence tomography B-scans of interest. Twenty-seven lightweight models (LWMs) from four families of models were trained on expert-labeled B-scans (∼70 K) as either "abnormal" or "normal", which established a baseline performance for the models. Then the LWMs were trained from random initialization using a student-teacher framework to incorporate a large number of unlabeled B-scans (∼500 K). A pre-trained ResNet50 model served as the teacher network. The ResNet50 teacher model achieved 96.0% validation accuracy and the validation accuracy achieved by the LWMs ranged from 89.6% to 95.1%. The best performing LWMs were 2.53 to 4.13 times faster than ResNet50 (0.109s to 0.178s vs. 0.452s). All LWMs benefitted from increasing the training set by including unlabeled B-scans in the student-teacher framework, with several models achieving validation accuracy of 96.0% or higher. The three best-performing models achieved comparable sensitivity and specificity in two hold-out test sets to the teacher network. We demonstrated the effectiveness of a student-teacher framework for training fast LWMs for automated B-scan of interest detection leveraging unlabeled, routinely-available data., Competing Interests: Niranchana Manivannan: Carl Zeiss Meditec, Inc., Dublin, CA (E); Gary C. Lee: Carl Zeiss Meditec, Inc., Dublin, CA (E); Sophia Yu: Carl Zeiss Meditec, Inc., Dublin, CA (E); Mary K. Durbin: Carl Zeiss Meditec, Inc., Dublin, CA (E); Aditya Nair: Carl Zeiss Meditec, Inc., Dublin, CA (E); Rishi P. Singh: Regeneron(C), Genentech(C), Alcon(C), Novartis(C), Bausch and Lomb(C), Gyroscope (C), Apellis(F), Graybug(F), Aerie (F); Katherine Talcott: Zeiss (F), Roche/Genentech (R); Aaron Lee: Carl Zeiss Meditec(F), Novartis(F), Genentech(C), Verana Health(C), US FDA(E), Santen(F), Microsoft(F), NVIDIA(F), Topcon(R), (© 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.)

Importance: Big data studies may allow for the aggregation of patients with rare diseases such as uveitis to answer important clinical questions. Standardization of uveitis-related variables will be necessary, including the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) codes used to identify patients of interest. There are currently limited data on the uniformity of diagnosis mapping to ICD-10 codes for uveitis diagnoses among different health systems., Objective: To assess the degree of uniformity in mapping of uveitis clinical concepts to ICD-10 codes across health care systems using the same electronic health record (EHR) system., Design, Setting, and Participants: This multicenter survey study was conducted between September 14 and October 9, 2020, at 5 academic health care systems that use the Epic EHR. Researchers from the University of Washington, Harvard University, Stanford University, Yale University, and the University of California, San Francisco queried 54 uveitis-related diagnostic terms and recorded the associated ICD-10 codes., Main Outcomes and Measures: The degree of uniformity for uveitis clinical concepts and associated ICD-10 codes., Results: Fifty-four uveitis-related diagnostic terms were queried within the Epic EHR at 5 different health care systems. There was perfect agreement among all 5 centers for 52 of the 54 diagnostic terms. Two diagnostic terms had differences in ICD-10 coding: juvenile idiopathic arthritis associated chronic uveitis and intermediate uveitis. Intermediate uveitis was associated with codes H20.1x (ICD-10 description: chronic iridocyclitis) or H20.9 (ICD-10 description: unspecified iridocyclitis) in 3 centers while being associated with code H30.2x (ICD-10 description: posterior cyclitis) at the 2 remaining centers. The discrepancies appear to be related to a recent update in diagnostic mapping in the Epic EHR., Conclusions and Relevance: This study suggests that ICD-10 code mapping to uveitis diagnostic terminology appears to be highly uniform at different centers with the Epic EHR. However, temporal changes in diagnosis mapping to ICD-10 codes and a lack of 1-to-1 mapping of diagnosis to ICD-10 code add additional sources of complexity to the interpretation of big data studies in uveitis.

Importance: Approximately 2 million cataract operations are performed annually in the US, and patterns of cataract surgery delivery are changing to meet the increasing demand. Therefore, a comparative analysis of visual acuity outcomes after immediate sequential bilateral cataract surgery (ISBCS) vs delayed sequential bilateral cataract surgery (DSBCS) is important for informing future best practices., Objective: To compare refractive outcomes of patients who underwent ISBCS, short-interval (1-14 days between operations) DSBCS (DSBCS-14), and long-interval (15-90 days) DSBCS (DSBCS-90) procedures., Design, Setting, and Participants: This retrospective cohort study used population-based data from the American Academy of Ophthalmology Intelligent Research in Sight (IRIS) Registry. A total of 1 824 196 IRIS Registry participants with bilateral visual acuity measurements who underwent bilateral cataract surgery were assessed., Exposures: Participants were divided into 3 groups (DSBCS-90, DSBCS-14, and ISBCS groups) based on the timing of the second eye surgery. Univariable and multivariable linear regression models were used to analyze the refractive outcomes of the first and second surgery eye., Main Outcomes and Measures: Mean postoperative uncorrected visual acuity (UCVA) and best-corrected visual acuity (BCVA) after cataract surgery., Results: This study analyzed data from 1 824 196 patients undergoing bilateral cataract surgery (mean [SD] age for those <87 years, 70.03 [7.77]; 684 916 [37.5%] male). Compared with the DSBCS-90 group, after age, self-reported race, insurance status, history of age-related macular degeneration, diabetic retinopathy, and glaucoma were controlled for, the UCVA of the first surgical eye was higher by 0.41 (95% CI, 0.36-0.45; P < .001) letters, and the BCVA was higher by 0.89 (95% CI, 0.86-0.92; P < .001) letters in the DSBCS-14 group, whereas in the ISBCS group, the UCVA was lower by 2.79 (95% CI, -2.95 to -2.63; P < .001) letters and the BCVA by 1.64 (95% CI, -1.74 to -1.53; P < .001) letters. Similarly, compared with the DSBCS-90 group for the second eye, in the DSBCS-14 group, the UCVA was higher by 0.79 (95% CI, 0.74-0.83; P < .001) letters and the BCVA by 0.48 (95% CI, 0.45-0.51; P < .001) letters, whereas in the ISBCS group, the UCVA was lower by -1.67 (95% CI, -1.83 to -1.51; P < .001) letters and the BCVA by -1.88 (95% CI, -1.98 to -1.78; P < .001) letters., Conclusions and Relevance: The results of this cohort study of patients in the IRIS Registry suggest that compared with DSBCS-14 or DSBCS-90, ISBCS is associated with worse visual outcomes, which may or may not be clinically relevant, depending on patients' additional risk factors. Nonrandom surgery group assignment, confounding factors, and large sample size could account for the small but statistically significant differences noted. Further studies are warranted to determine whether these factors should be considered clinically relevant when counseling patients before cataract surgery.

Purpose: To investigate emerging trends and increasing costs in the National Residency Matching Program (NRMP) and San Francisco Residency and Fellowship Match Services (SF Match) associated with the current applicant/program Gale-Shapley-type matching algorithms., Design: A longitudinal observational study of behavioral trends in national residency matching systems with modeling of match results with alternative parameters., Methods: We analyzed publicly available data from the SF Match and NRMP websites from 1985 to 2020 for trends in the total number of applicants and available positions, as well the average number of applications and interviews per applicant for multiple specialties. To understand these trends and the algorithms' effect on the residency programs and applicants, we analyzed anonymized rank list and match data for ophthalmology from the SF Match between 2011 to 2019. Match results using current match parameters, as well as under conditions in which applicant and/or program rank lists were truncated, were analyzed., Results: Both the number of applications and length of programs' rank lists have increased steadily throughout residency programs, particularly those with competitive specialities. Capping student rank lists at 7 programs, or less than 80% of the average 8.9 programs currently ranked, results in a 0.71% decrease in the total number of positions filled. Similarly, capping program rank lists at 7 applicants per spot, or less than 60% of the average 11.5 applicants ranked per spot, results in a 5% decrease in the total number of positions filled., Conclusion: While the number of ophthalmology positions in the US has increased only modestly, the number of applications under consideration has increased substantially over the past two decades. The current study suggests that both programs and applicants rank more choices than are required for a nearly-complete and stable match, creating excess cost and work for both applicants and programs. "Stable-marriage"-type algorithms induce applicants and programs to rank as many counter-parties as possible to maximize individual chances of optimizing the match.

Purpose: Optical coherence tomography (OCT) is widely used in the management of retinal pathologies, including age-related macular degeneration (AMD), diabetic macular edema (DME), and primary open-angle glaucoma (POAG). We used machine learning techniques to understand diagnostic performance gains from expanding macular OCT B-scans compared with foveal-only OCT B-scans for these conditions., Methods: Electronic medical records were extracted to obtain 61 B-scans per eye from patients with AMD, diabetic retinopathy, or POAG. We constructed deep neural networks and random forest ensembles and generated area under the receiver operating characteristic (AUROC) and area under the precision recall (AUPR) curves., Results: After extracting 630,000 OCT images, we achieved improved AUROC and AUPR curves when comparing the central image (one B-scan) to all images (61 B-scans). The AUROC and AUPR points of diminishing return for diagnostic accuracy for macular OCT coverage were found to be within 2.75 to 4.00 mm (14-19 B-scans), 4.25 to 4.50 mm (20-21 B-scans), and 4.50 to 6.25 mm (21-28 B-scans) for AMD, DME, and POAG, respectively. All models with >0.25 mm of coverage had statistically significantly improved AUROC/AUPR curves for all diseases (P < 0.05)., Conclusions: Systematically expanded macular coverage models demonstrated significant differences in total macular coverage required for improved diagnostic accuracy, with the largest macular area being relevant in POAG followed by DME and then AMD. These findings support our hypothesis that the extent of macular coverage by OCT imaging in the clinical setting, for any of the three major disorders, has a measurable impact on the functionality of artificial intelligence decision support., Translational Relevance: We used machine learning techniques to improve OCT imaging standards for common retinal disease diagnoses.

Objective: With rising global prevalence of diabetic retinopathy (DR), automated DR screening is needed for primary care settings. Two automated artificial intelligence (AI)-based DR screening algorithms have U.S. Food and Drug Administration (FDA) approval. Several others are under consideration while in clinical use in other countries, but their real-world performance has not been evaluated systematically. We compared the performance of seven automated AI-based DR screening algorithms (including one FDA-approved algorithm) against human graders when analyzing real-world retinal imaging data., Research Design and Methods: This was a multicenter, noninterventional device validation study evaluating a total of 311,604 retinal images from 23,724 veterans who presented for teleretinal DR screening at the Veterans Affairs (VA) Puget Sound Health Care System (HCS) or Atlanta VA HCS from 2006 to 2018. Five companies provided seven algorithms, including one with FDA approval, that independently analyzed all scans, regardless of image quality. The sensitivity/specificity of each algorithm when classifying images as referable DR or not were compared with original VA teleretinal grades and a regraded arbitrated data set. Value per encounter was estimated., Results: Although high negative predictive values (82.72-93.69%) were observed, sensitivities varied widely (50.98-85.90%). Most algorithms performed no better than humans against the arbitrated data set, but two achieved higher sensitivities, and one yielded comparable sensitivity (80.47%, P = 0.441) and specificity (81.28%, P = 0.195). Notably, one had lower sensitivity (74.42%) for proliferative DR ( P = 9.77 × 10 -4 ) than the VA teleretinal graders. Value per encounter varied at $15.14-$18.06 for ophthalmologists and $7.74-$9.24 for optometrists., Conclusions: The DR screening algorithms showed significant performance differences. These results argue for rigorous testing of all such algorithms on real-world data before clinical implementation., (© 2021 by the American Diabetes Association.)

Background: Immunomodulatory therapy (IMT) is often considered for systemic treatment of non-infectious uveitis (NIU). During the evolving coronavirus disease-2019 (COVID-19) pandemic, given the concerns related to IMT and the increased risk of infections, an urgent need for guidance on the management of IMT in patients with uveitis has emerged., Methods: A cross-sectional survey of international uveitis experts was conducted. An expert steering committee identified clinical questions on the use of IMT in patients with NIU during the COVID-19 pandemic. Using an interactive online questionnaire, guided by background experience and knowledge, 139 global uveitis experts generated consensus statements for IMT. In total, 216 statements were developed around when to initiate, continue, decrease and stop systemic and local corticosteroids, conventional immunosuppressive agents and biologics in patients with NIU. Thirty-one additional questions were added, related to general recommendations, including the use of non-steroidal anti-inflammatory drugs (NSAIDs) and hydroxychloroquine., Results: Highest consensus was achieved for not initiating IMT in patients who have suspected or confirmed COVID-19, and for using local over systemic corticosteroid therapy in patients who are at high-risk and very high-risk for severe or fatal COVID-19. While there was a consensus in starting or initiating NSAIDs for the treatment of scleritis in healthy patients, there was no consensus in starting hydroxychloroquine in any risk groups., Conclusion: Consensus guidelines were proposed based on global expert opinion and practical experience to bridge the gap between clinical needs and the absence of medical evidence, to guide the treatment of patients with NIU during the COVID-19 pandemic., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Vascular disease is a risk factor for Alzheimer's disease (AD) and related dementia in older adults. Retinal artery/vein occlusion (RAVO) is an ophthalmic complication of systemic vascular pathology. Whether there are associations between RAVO and dementia risk is unknown., Objective: To determine whether RAVOs are associated with an increased risk of developing vascular dementia or AD., Methods: Data from Adult Changes in Thought (ACT) study participants were analyzed. This prospective, population-based cohort study followed older adults (age ≥65 years) who were dementia-free at enrollment for development of vascular dementia or AD based on research criteria. RAVO diagnoses were extracted from electronic medical records. Cox-regression survival analyses were stratified by APOEɛ4 genotype and adjusted for demographic and clinical factors., Results: On review of 41,216 person-years (4,743 participants), 266 (5.6%) experienced RAVO. APOEɛ4 carriers who developed RAVO had greater than four-fold higher risk for developing vascular dementia (Hazard Ratio [HR] 4.54, 95% Confidence Interval [CI] 1.86, 11.10, p = 0.001). When including other cerebrovascular disease (history of carotid endarterectomy or transient ischemic attack) in the model, the risk was three-fold higher (HR 3.06, 95% CI 1.23, 7.62). No other conditions evaluated in the secondary analyses were found to confound this relationship. There was no effect in non-APOEɛ4 carriers (HR 1.03, 95% CI 0.37, 2.80). There were no significant associations between RAVO and AD in either APOE group., Conclusion: Older dementia-free patients who present with RAVO and carry the APOEɛ4 allele appear to be at higher risk for vascular dementia.

The eye and brain share common mechanisms of aging and disease, thus the retina is an essential source of accessible information about neurodegenerative processes occurring in the brain. Advances in retinal imaging have led to the discovery of many potential biomarkers of Alzheimer's disease, although further research is needed to validate these associations. Understanding the mechanisms of retinal disease in the context of aging will extend our knowledge of AD and may enable advancements in diagnosis, monitoring, and treatment., Competing Interests: CONFLICTS OF INTEREST The authors declare that they have no conflicts of interest.

Competing Interests: Disclosure: E. Tom, None; P.A. Keane, DeepMind Technologies (C), Roche (C), Novartis (C), Apellis (C), Bayer (F), Allergan (F), Topcon (F), Heidelberg Engineering (F); M. Blazes, None; L.R. Pasquale, Verily (C), Eyenovia (C), Nicox (C), Bausch + Lomb (C), Emerald Bioscience (C); M.F. Chiang, Novartis (C), InTeleretina, LLC (I); A.Y. Lee, U.S. Food and Drug Administration (E), Genentech (C), Topcon (C), Verana Health (C), Santen (F), Novartis (F), Carl Zeiss Meditec (F); C.S. Lee, None; M.D. Abramoff, IDx (I, F, E, P, S), Alimera (F); J.P. Campbell, Genentech (F); R. Singh, Genentech (C), Novartis (C), Apellis (F), Bayer (C), Carl Zeiss Meditec (C), Aerie (C), Graybug (F), Regeneron (C); D. Ting, EyRIS (I, P), Novartis (C), Ocutrx (I, C), Optomed (C)

The two most common types of genetic alterations yet identified in uterine endometrioid carcinoma (UEC) are PTEN mutations and microsatellite instability (MI). Furthermore, MI-positive UECs (defined as tumors with detectable alterations at two or more different microsatellite loci) are significantly more likely to contain PTEN mutations than are MI-negative UECs. To determine whether PTEN inactivation is a relatively early event in endometrial tumorigenesis, we evaluated complex atypical hyperplasia (CAH), the direct precursor to UEC, for the presence of PTEN mutations. Mutations were present in 3 of 11 (27%) CAHs with synchronous UEC and in 4 of 18 (22%) CAHs that were not associated with invasive carcinoma. One case with synchronous CAH and UEC contained a germ-line PTEN mutation. In addition, we evaluated the same series of CAHs for MI. We identified four MI-positive CAHs with synchronous UEC but did not detect the MI phenotype in any CAHs without associated invasive carcinoma. A PTEN-mutant (germ-line mutation) MI-negative CAH was synchronous with a PTEN-mutant MI-positive UEC. These results suggest that mutation of PTEN can be an early event in the pathogenesis of UEC and may precede the development of the MI phenotype in a subset of cases.

Loss of heterozygosity of chromosome 10q has been reported in approximately 40% of endometrial carcinomas. PTEN, a candidate tumor suppressor gene located at chromosome 10q23.3, was recently identified and found to be homozygously deleted or mutated in several different types of human tumors. To determine if PTEN is a target of 10q loss of heterozygosity in carcinomas of the endometrium, we examined 32 primary endometrial carcinomas for mutations in PTEN. The tumors included the two major histopathological types of endometrial carcinoma: endometrioid (n = 26; 14 microsatellite instability (MI)-positive and 12 MI-negative) and serous (n = 6). Overall, mutations were detected in 50% of the endometrial carcinomas we analyzed. Mutations were present in 12 of 14 (86%) MI-positive and 4 of 12 (33%) MI-negative endometrioid tumors. Furthermore, mutations were found in all three histological grades of MI-positive endometrioid carcinoma. All six serous endometrial carcinomas lacked detectable mutations. To evaluate the role of PTEN in other common malignancies of the female genital tract, 12 serous ovarian carcinomas and 10 squamous cervical carcinomas were analyzed and were negative for mutations. Our results support PTEN as a tumor suppressor gene and suggest that mutations in PTEN play a significant role in the pathogenesis of the endometrioid type of endometrial carcinoma.

Endometrial carcinoma is the second most common tumor type in women with hereditary nonpolyposis colorectal carcinoma. Microsatellite instability (MI) has been observed in the inherited (hereditary nonpolyposis colorectal carcinoma-associated) form of endometrial carcinoma as well as in approximately 20% of presumably sporadic cases. Recent studies suggest that MI in many cell lines or xenografts derived from sporadic colorectal carcinomas is not attributable to mutations in four known human DNA mismatch repair (MMR) genes (hMSH2, hMLH1, hPMS1, and hPMS2). Mutational analyses of these four MMR genes in endometrial carcinomas have not been previously reported. We analyzed nine sporadic MI-positive primary endometrial carcinomas for mutations in the above four MMR genes. Mutations were detected in two tumors (in hMSH2), and both of the mutations were acquired somatically. Immunohistochemical staining revealed a lack of expression of hMSH2 protein in the two tumors containing hMSH2 mutations. Our data suggest that mutations in these four known DNA MMR genes are not responsible for MI in the majority of sporadic endometrial carcinomas displaying this phenotype.