Publications by authors named "Michael F Chiang"

208 Publications

Single-Examination Risk Prediction of Severe Retinopathy of Prematurity.

Pediatrics 2021 Nov 23. Epub 2021 Nov 23.

Departments of Ophthalmology.

Background And Objectives: Retinopathy of prematurity (ROP) is a leading cause of childhood blindness. Screening and treatment reduces this risk, but requires multiple examinations of infants, most of whom will not develop severe disease. Previous work has suggested that artificial intelligence may be able to detect incident severe disease (treatment-requiring retinopathy of prematurity [TR-ROP]) before clinical diagnosis. We aimed to build a risk model that combined artificial intelligence with clinical demographics to reduce the number of examinations without missing cases of TR-ROP.

Methods: Infants undergoing routine ROP screening examinations (1579 total eyes, 190 with TR-ROP) were recruited from 8 North American study centers. A vascular severity score (VSS) was derived from retinal fundus images obtained at 32 to 33 weeks' postmenstrual age. Seven ElasticNet logistic regression models were trained on all combinations of birth weight, gestational age, and VSS. The area under the precision-recall curve was used to identify the highest-performing model.

Results: The gestational age + VSS model had the highest performance (mean ± SD area under the precision-recall curve: 0.35 ± 0.11). On 2 different test data sets (n = 444 and n = 132), sensitivity was 100% (positive predictive value: 28.1% and 22.6%) and specificity was 48.9% and 80.8% (negative predictive value: 100.0%).

Conclusions: Using a single examination, this model identified all infants who developed TR-ROP, on average, >1 month before diagnosis with moderate to high specificity. This approach could lead to earlier identification of incident severe ROP, reducing late diagnosis and treatment while simultaneously reducing the number of ROP examinations and unnecessary physiologic stress for low-risk infants.
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http://dx.doi.org/10.1542/peds.2021-051772DOI Listing
November 2021

Assessment and management of retinopathy of prematurity in the era of anti-vascular endothelial growth factor (VEGF).

Prog Retin Eye Res 2021 Nov 9:101018. Epub 2021 Nov 9.

Department of Ophthalmology, Chang Gung Memorial Hospital, Linkou, Taiwan; College of Medicine, Chang Gung University, Taoyuan, Taiwan. Electronic address:

The incidence of retinopathy of prematurity (ROP) continues to rise due to the improved survival of very low birth weight infants in developed countries. This epidemic is also fueled by increased survival of preterm babies with variable use of oxygen and a lack of ROP awareness and screening services in resource-limited regions. Improvements in technology and a basic understanding of the disease pathophysiology have changed the way we screen and manage ROP, educate providers and patients, and improve ROP awareness. Advancements in imaging techniques, expansion of telemedicine services, and the potential for artificial intelligence-assisted ROP screening programs have created opportunities to improve ROP care in areas with a shortage of ophthalmologists trained in ROP. To address the gap in provider knowledge regarding ROP, the Global Education Network for Retinopathy of Prematurity (GEN-ROP) created a web-based tele-education training module that can be used to educate all providers involved in ROP, including non-physician ROP screeners. Over the past 50 years, the treatment of severe ROP has evolved from limited treatment modalities to cryotherapy and laser photocoagulation. More recently, there has been growing evidence to support the use of anti-vascular endothelial growth factor (VEGF) agents for the treatment of severe ROP. However, VEGF is known to be important in organogenesis and microvascular maintenance, and given that intravitreal anti-VEGF treatment can result in systemic VEGF suppression over a period of at least 1-12 weeks, there are concerns regarding adverse effects and long-term ocular and systemic developmental consequences of anti-VEGF therapy. Future research in ophthalmology to address the growing burden of ROP should focus on cost-effective fundus imaging devices, implementation of artificial intelligence platforms, updated treatment algorithms with optimal use of anti-VEGF and careful investigation of its long-term effects, and surgical options in advanced ROP. Addressing these unmet needs will aid the global effort against the ROP epidemic and optimize our understanding and treatment of this blinding disease.
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http://dx.doi.org/10.1016/j.preteyeres.2021.101018DOI Listing
November 2021

The 2021 National Eye Institute Strategic Plan: Recruiting and Training a Diverse New Generation.

Authors:
Michael F Chiang

Am J Ophthalmol 2021 Nov 1. Epub 2021 Nov 1.

The National Eye Institute, National Institutes of Health, Bethesda, Maryland. Electronic address:

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http://dx.doi.org/10.1016/j.ajo.2021.10.022DOI Listing
November 2021

The 2021 National Eye Institute Strategic Plan: Driving Innovation in Eye and Vision Research.

Authors:
Michael F Chiang

Invest Ophthalmol Vis Sci 2021 11;62(14)

National Eye Institute, National Institutes of Health, Bethesda, Maryland, United States. E-mail: .

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http://dx.doi.org/10.1167/iovs.62.14.2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8572467PMC
November 2021

The 2021 National Eye Institute Strategic Plan-Relating Vision to Health and Quality of Life.

Authors:
Michael F Chiang

JAMA Ophthalmol 2021 Nov 1. Epub 2021 Nov 1.

National Eye Institute, National Institutes of Health, Bethesda, Maryland.

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http://dx.doi.org/10.1001/jamaophthalmol.2021.4774DOI Listing
November 2021

The 2021 National Eye Institute Strategic Plan: Fostering Collaboration in Vision Research and Clinical Care.

Authors:
Michael F Chiang

Optom Vis Sci 2021 Nov;98(11):1228-1230

National Eye Institute, National Institutes of Health, Bethesda, Maryland

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http://dx.doi.org/10.1097/OPX.0000000000001821DOI Listing
November 2021

Prospective evaluation of optical coherence tomography for disease detection in the Casey mobile eye clinic.

Exp Biol Med (Maywood) 2021 Oct 15;246(20):2214-2221. Epub 2021 Sep 15.

Casey Eye Institute, Oregon Health & Science University, OR 97239, USA.

This study was designed to evaluate iVue Spectral-domain optical coherence tomography (SD-OCT) effectiveness in screening for eye disease compared to clinical examination. Subjects were recruited from the Casey Eye Community Outreach Program Mobile Clinic during its routinely scheduled outreach clinics to indigent, underserved populations throughout Oregon. Macular optical coherence tomography interpretation and automated optical coherence tomography analysis were compared to the clinical examination, with specific attention to findings indicative of retinal abnormalities, risks for glaucoma, and narrow angles. As a result, a total of 114 subjects were included in this study. In diabetics, optical coherence tomography and clinical exam were in fair agreement (kappa = 0.39), with 22% of eyes having abnormal findings on macular optical coherence tomography and 26% of eyes having diabetic retinopathy or diabetic macular edema on fundus exam. In non-diabetics, optical coherence tomography and clinical exam were in fair agreement (kappa = 0.28), with 11% of eyes having abnormal findings on macular optical coherence tomography and 9% on fundus exam. Using optical coherence tomography ganglion cell complex and retinal nerve fiber layer analysis, 18% of eyes were found to be glaucoma suspects, whereas clinical exam of cup-to-disc ratio detected 8% and intraocular pressure 5%. Agreements between optical coherence tomography and other methods were poor (kappa < 0.11) for glaucoma suspect. Anterior segment optical coherence tomography of the angle found 8% of eyes to have occludable angles, whereas slit lamp and gonioscopy found 5% of eyes to have narrow angles, with moderate agreement (kappa = 0.57). In summary, optical coherence tomography detected additional retinal abnormalities, glaucoma suspects, and narrow angles compared to clinical exam alone and may serve as a useful adjunct to the clinical exam in screening for eye disease in a low-risk, medically underserved, ethnically diverse population.
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http://dx.doi.org/10.1177/15353702211037262DOI Listing
October 2021

Foundational Considerations for Artificial Intelligence Using Ophthalmic Images.

Ophthalmology 2021 Aug 31. Epub 2021 Aug 31.

Division of Pediatric Cardiac Anesthesia, Department of Anesthesiology, Stanford University School of Medicine, San Francisco, California; Center for Biomedical Ethics, Stanford University School of Medicine, San Francisco, California.

Importance: The development of artificial intelligence (AI) and other machine diagnostic systems, also known as software as a medical device, and its recent introduction into clinical practice requires a deeply rooted foundation in bioethics for consideration by regulatory agencies and other stakeholders around the globe.

Objectives: To initiate a dialogue on the issues to consider when developing a bioethically sound foundation for AI in medicine, based on images of eye structures, for discussion with all stakeholders.

Evidence Review: The scope of the issues and summaries of the discussions under consideration by the Foundational Principles of Ophthalmic Imaging and Algorithmic Interpretation Working Group, as first presented during the Collaborative Community on Ophthalmic Imaging inaugural meeting on September 7, 2020, and afterward in the working group.

Findings: Artificial intelligence has the potential to improve health care access and patient outcome fundamentally while decreasing disparities, lowering cost, and enhancing the care team. Nevertheless, substantial concerns exist. Bioethicists, AI algorithm experts, as well as the Food and Drug Administration and other regulatory agencies, industry, patient advocacy groups, clinicians and their professional societies, other provider groups, and payors (i.e., stakeholders) working together in collaborative communities to resolve the fundamental ethical issues of nonmaleficence, autonomy, and equity are essential to attain this potential. Resolution impacts all levels of the design, validation, and implementation of AI in medicine. Design, validation, and implementation of AI warrant meticulous attention.

Conclusions And Relevance: The development of a bioethically sound foundation may be possible if it is based in the fundamental ethical principles of nonmaleficence, autonomy, and equity for considerations for the design, validation, and implementation for AI systems. Achieving such a foundation will be helpful for continuing successful introduction into medicine before consideration by regulatory agencies. Important improvements in accessibility and quality of health care, decrease in health disparities, and lower cost thereby can be achieved. These considerations should be discussed with all stakeholders and expanded on as a useful initiation of this dialogue.
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http://dx.doi.org/10.1016/j.ophtha.2021.08.023DOI Listing
August 2021

Description and management of retinopathy of prematurity reactivation after intravitreal antivascular endothelial growth factor therapy.

Curr Opin Ophthalmol 2021 Sep;32(5):468-474

Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, Illinois.

Purpose Of Review: To review the literature regarding reactivation of retinopathy of prematurity (ROP) after treatment with antivascular endothelial growth factor (anti-VEGF) agents.

Recent Findings: Reactivation can occur after anti-VEGF or laser. Risk factors for reactivation include patient and disease-related factors. Various studies are evaluating the use of different anti-VEGF agents and reactivation rates. However, the definition of reactivation varies between studies.

Summary: The literature has varied definitions of reactivation, which is often used interchangeably with recurrence. It is important to recognize features of reactivation of ROP to appropriately manage patients and conduct clinical trials. The International Classification of ROP 3rd edition has established a consensus guideline regarding terminology describing reactivation.
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http://dx.doi.org/10.1097/ICU.0000000000000786DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8514167PMC
September 2021

The NEI Audacious Goals Initiative: Advancing the Frontier of Regenerative Medicine.

Transl Vis Sci Technol 2021 08;10(10)

Office of the Director, National Eye Institute, Bethesda, Maryland, USA.

Eight years since the launch of the National Eye Institute Audacious Goals Initiative for Regenerative Medicine, real progress has been made in the effort to restore vision by replacing retinal neurons. Although challenges remain, the infrastructure, tools, and preclinical models to support clinical studies in humans are being prepared. Building on the pioneering trials that are replacing the retinal pigment epithelium, it is expected that by the end of this decade first-in-human trials for the replacement of retinal neurons will be initiated.
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http://dx.doi.org/10.1167/tvst.10.10.2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8362633PMC
August 2021

Electronic health record note review in an outpatient specialty clinic: who is looking?

JAMIA Open 2021 Jul 31;4(3):ooab044. Epub 2021 Jul 31.

National Eye Institute, National Institutes of Health, Bethesda, Maryland, USA.

Note entry and review in electronic health records (EHRs) are time-consuming. While some clinics have adopted team-based models of note entry, how these models have impacted note review is unknown in outpatient specialty clinics such as ophthalmology. We hypothesized that ophthalmologists and ancillary staff review very few notes. Using audit log data from 9775 follow-up office visits in an academic ophthalmology clinic, we found ophthalmologists reviewed a median of 1 note per visit (2.6 ± 5.3% of available notes), while ancillary staff reviewed a median of 2 notes per visit (4.1 ± 6.2% of available notes). While prior ophthalmic office visit notes were the most frequently reviewed note type, ophthalmologists and staff reviewed no such notes in 51% and 31% of visits, respectively. These results highlight the collaborative nature of note review and raise concerns about how cumbersome EHR designs affect efficient note review and the utility of prior notes in ophthalmic clinical care.
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http://dx.doi.org/10.1093/jamiaopen/ooab044DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8325486PMC
July 2021

Addressing the Third Epidemic of Retinopathy of Prematurity Through Telemedicine and Technology: A Systematic Review.

J Pediatr Ophthalmol Strabismus 2021 Jul-Aug;58(4):261-269. Epub 2021 Jul 1.

The rising prevalence of retinopathy of prematurity (ROP) in low- and middle-income countries has increased the need for screening at-risk infants. The purpose of this article was to review the impact of tele-medicine and technology on ROP screening programs. Adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a systematic review was performed using PubMed, Pro-Quest, and Google Scholar bibliographic search engine. Terms searched included retinopathy of prematurity, telemedicine, and tele-ophthalmology. Data regarding internet access and gross domestic product per capita were obtained from the World Bank. Information was also obtained about internet access, speeds, and costs in low-income countries. There has been increasing integration of telemedicine and technology for ROP screening and management. Low-income countries are using available internet options and information and communications technology for ROP screening, which can aid in addressing the unique challenges faced by low-income countries. This provides a promising solution to the third epidemic of ROP by expanding and improving screening and management. Although telemedicine systems may serve as a cost-effective approach to facilitate delivery of health care, programs (especially in lowand middle-income countries) require national support to maintain its infrastructure. .
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http://dx.doi.org/10.3928/01913913-20210223-01DOI Listing
November 2021

Length and Redundancy of Outpatient Progress Notes Across a Decade at an Academic Medical Center.

JAMA Netw Open 2021 Jul 1;4(7):e2115334. Epub 2021 Jul 1.

Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland.

Importance: There is widespread concern that clinical notes have grown longer and less informative over the past decade. Addressing these concerns requires a better understanding of the magnitude, scope, and potential causes of increased note length and redundancy.

Objective: To measure changes between 2009 and 2018 in the length and redundancy of outpatient progress notes across multiple medical specialties and investigate how these measures associate with author experience and method of note entry.

Design, Setting, And Participants: This cross-sectional study was conducted at Oregon Health & Science University, a large academic medical center. Participants included clinicians and staff who wrote outpatient progress notes between 2009 and 2018 for a random sample of 200 000 patients. Statistical analysis was performed from March to August 2020.

Exposures: Use of a comprehensive electronic health record to document patient care.

Main Outcomes And Measures: Note length, note redundancy (ie, the proportion of text identical to the patient's last note), and percentage of templated, copied, or directly typed note text.

Results: A total of 2 704 800 notes written by 6228 primary authors across 46 specialties were included in this study. Median note length increased 60.1% (99% CI, 46.7%-75.2%) from a median of 401 words (interquartile range [IQR], 225-660 words) in 2009 to 642 words (IQR, 399-1007 words) in 2018. Median note redundancy increased 10.9 percentage points (99% CI, 7.5-14.3 percentage points) from 47.9% in 2009 to 58.8% in 2018. Notes written in 2018 had a mean value of just 29.4% (99% CI, 28.2%-30.7%) directly typed text with the remaining 70.6% of text being templated or copied. Mixed-effect linear models found that notes with higher proportions of templated or copied text were significantly longer and more redundant (eg, in the 2-year model, each 1% increase in the proportion of copied or templated note text was associated with 1.5% [95% CI, 1.5%-1.5%] and 1.6% [95% CI, 1.6%-1.6%] increases in note length, respectively). Residents and fellows also wrote significantly (26.3% [95% CI, 25.8%-26.7%]) longer notes than more senior authors, as did more recent hires (1.8% for each year later [95% CI, 1.3%-2.4%]).

Conclusions And Relevance: In this study, outpatient progress notes grew longer and more redundant over time, potentially limiting their use in patient care. Interventions aimed at reducing outpatient progress note length and redundancy may need to simultaneously address multiple factors such as note template design and training for both new and established clinicians.
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http://dx.doi.org/10.1001/jamanetworkopen.2021.15334DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8290305PMC
July 2021

International Classification of Retinopathy of Prematurity, Third Edition.

Ophthalmology 2021 10 8;128(10):e51-e68. Epub 2021 Jul 8.

Department of Pediatric Retina, Narayana Nethralaya Eye Institute, Bangalore, Karnataka, India.

Purpose: The International Classification of Retinopathy of Prematurity is a consensus statement that creates a standard nomenclature for classification of retinopathy of prematurity (ROP). It was initially published in 1984, expanded in 1987, and revisited in 2005. This article presents a third revision, the International Classification of Retinopathy of Prematurity, Third Edition (ICROP3), which is now required because of challenges such as: (1) concerns about subjectivity in critical elements of disease classification; (2) innovations in ophthalmic imaging; (3) novel pharmacologic therapies (e.g., anti-vascular endothelial growth factor agents) with unique regression and reactivation features after treatment compared with ablative therapies; and (4) recognition that patterns of ROP in some regions of the world do not fit neatly into the current classification system.

Design: Review of evidence-based literature, along with expert consensus opinion.

Participants: International ROP expert committee assembled in March 2019 representing 17 countries and comprising 14 pediatric ophthalmologists and 20 retinal specialists, as well as 12 women and 22 men.

Methods: The committee was initially divided into 3 subcommittees-acute phase, regression or reactivation, and imaging-each of which used iterative videoconferences and an online message board to identify key challenges and approaches. Subsequently, the entire committee used iterative videoconferences, 2 in-person multiday meetings, and an online message board to develop consensus on classification.

Main Outcome Measures: Consensus statement.

Results: The ICROP3 retains current definitions such as zone (location of disease), stage (appearance of disease at the avascular-vascular junction), and circumferential extent of disease. Major updates in the ICROP3 include refined classification metrics (e.g., posterior zone II, notch, subcategorization of stage 5, and recognition that a continuous spectrum of vascular abnormality exists from normal to plus disease). Updates also include the definition of aggressive ROP to replace aggressive-posterior ROP because of increasing recognition that aggressive disease may occur in larger preterm infants and beyond the posterior retina, particularly in regions of the world with limited resources. ROP regression and reactivation are described in detail, with additional description of long-term sequelae.

Conclusions: These principles may improve the quality and standardization of ROP care worldwide and may provide a foundation to improve research and clinical care.
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http://dx.doi.org/10.1016/j.ophtha.2021.05.031DOI Listing
October 2021

High-speed and widefield handheld swept-source OCT angiography with a VCSEL light source.

Biomed Opt Express 2021 Jun 20;12(6):3553-3570. Epub 2021 May 20.

Casey Eye Institute, Oregon Health and Science University, Portland, OR 97239, USA.

Optical coherence tomography (OCT) and OCT angiography (OCTA) enable noninvasive structural and angiographic imaging of the eye. Portable handheld OCT/OCTA systems are required for imaging patients in the supine position. Examples include infants in the neonatal intensive care unit (NICU) and operating room (OR). The speed of image acquisition plays a pivotal role in acquiring high-quality OCT/OCTA images, particularly with the handheld system, since both the operator hand tremor and subject motion can cause significant motion artifacts. In addition, having a large field of view and the ability of real-time data visualization are critical elements in rapid disease screening, reducing imaging time, and detecting peripheral retinal pathologies. The arrangement of optical components is less flexible in the handheld system due to the limitation of size and weight. In this paper, we introduce a 400-kHz, 55-degree field of view handheld OCT/OCTA system that has overcome many technical challenges as a portable OCT system as well as a high-speed OCTA system. We demonstrate imaging premature infants with retinopathy of prematurity (ROP) in the NICU, a patient with incontinentia pigmenti (IP), and a patient with X-linked retinoschisis (XLRS) in the OR using our handheld OCT system. Our design may have the potential for improving the diagnosis of retinal diseases and help provide a practical guideline for designing a flexible and portable OCT system.
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http://dx.doi.org/10.1364/BOE.425411DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8221946PMC
June 2021

Impact of Artificial Intelligence on Medical Education in Ophthalmology.

Transl Vis Sci Technol 2021 06;10(7):14

Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, IL, USA.

Clinical care in ophthalmology is rapidly evolving as artificial intelligence (AI) algorithms are being developed. The medical community and national and federal regulatory bodies are recognizing the importance of adapting to AI. However, there is a gap in physicians' understanding of AI and its implications regarding its potential use in clinical care, and there are limited resources and established programs focused on AI and medical education in ophthalmology. Physicians are essential in the application of AI in a clinical context. An AI curriculum in ophthalmology can help provide physicians with a fund of knowledge and skills to integrate AI into their practice. In this paper, we provide general recommendations for an AI curriculum for medical students, residents, and fellows in ophthalmology.
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http://dx.doi.org/10.1167/tvst.10.7.14DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8212436PMC
June 2021

Evaluation of pediatric ophthalmologists' perspectives of artificial intelligence in ophthalmology.

J AAPOS 2021 06 1;25(3):164.e1-164.e5. Epub 2021 Jun 1.

Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, Illinois. Electronic address:

Purpose: To survey pediatric ophthalmologists on their perspectives of artificial intelligence (AI) in ophthalmology.

Methods: This is a subgroup analysis of a study previously reported. In March 2019, members of the American Association for Pediatric Ophthalmology and Strabismus (AAPOS) were recruited via the online AAPOS discussion board to voluntarily complete a Web-based survey consisting of 15 items. Survey items assessed the extent participants "agreed" or "disagreed" with statements on the perceived benefits and concerns of AI in ophthalmology. Responses were analyzed using descriptive statistics.

Results: A total of 80 pediatric ophthalmologists who are members of AAPOS completed the survey. The mean number of years since graduating residency was 21 years (range, 0-46). Overall, 91% (73/80) reported understanding the concept of AI, 70% (56/80) believed AI will improve the practice of ophthalmology, 68% (54/80) reported willingness to incorporate AI into their clinical practice, 65% (52/80) did not believe AI will replace physicians, and 71% (57/80) believed AI should be incorporated into medical school and residency curricula. However, 15% (12/80) were concerned that AI will replace physicians, 26% (21/80) believed AI will harm the patient-physician relationship, and 46% (37/80) reported concern over the diagnostic accuracy of AI.

Conclusions: Most pediatric ophthalmologists in this survey viewed the role of AI in ophthalmology positively.
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http://dx.doi.org/10.1016/j.jaapos.2021.01.011DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8328946PMC
June 2021

Methods for Large-Scale Quantitative Analysis of Scribe Impacts on Clinical Documentation.

AMIA Annu Symp Proc 2020 25;2020:573-582. Epub 2021 Jan 25.

Department of Medical Informatics and Clinical Epidemiology.

Many medical providers employ scribes to manage electronic health record (EHR) documentation. Prior studies have shown the benefits of scribes, but no large-scale study has quantitively assessed scribe impact on documentation workflows. We propose methods that leverage EHR data for identifying scribe presence during an office visit, measuring provider documentation time, and determining how notes are edited and composed. In a case study, we found scribe use was associated with less provider documentation time overall (averaging 2.4 minutes or 39% less time, p < 0.001), fewer note edits by providers (8.4% less added and 4.2% less deleted text, p < 0.001), but significantly more documentation time after the visit for four out of seven providers (p < 0.001) and no change in the amount of copied and imported note text. Our methods could validate prior study results, identify variability for determining best practices, and determine that scribes do not improve all aspects of documentation.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8075531PMC
June 2021

Diagnosability of Synthetic Retinal Fundus Images for Plus Disease Detection in Retinopathy of Prematurity.

AMIA Annu Symp Proc 2020 25;2020:329-337. Epub 2021 Jan 25.

Medical Informatics & Clinical Epidemiology.

Advances in generative adversarial networks have allowed for engineering of highly-realistic images. Many studies have applied these techniques to medical images. However, evaluation of generated medical images often relies upon image quality and reconstruction metrics, and subjective evaluation by laypersons. This is acceptable for generation of images depicting everyday objects, but not for medical images, where there may be subtle features experts rely upon for diagnosis. We implemented the pix2pix generative adversarial network for retinal fundus image generation, and evaluated the ability of experts to identify generated images as such and to form accurate diagnoses of plus disease in retinopathy of prematurity. We found that, while experts could discern between real and generated images, the diagnoses between image sets were similar. By directly evaluating and confirming physicians' abilities to diagnose generated retinal fundus images, this work supports conclusions that generated images may be viable for dataset augmentation and physician training.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8075515PMC
June 2021

Application of Machine Learning to Predict Patient No-Shows in an Academic Pediatric Ophthalmology Clinic.

AMIA Annu Symp Proc 2020 25;2020:293-302. Epub 2021 Jan 25.

Department of Ophthalmology, Casey Eye Institute, and.

Patient "no-shows" are missed appointments resulting in clinical inefficiencies, revenue loss, and discontinuity of care. Using secondary electronic health record (EHR) data, we used machine learning to predict patient no-shows in follow-up and new patient visits in pediatric ophthalmology and to evaluate features for importance. The best model, XGBoost, had an area under the receiver operating characteristics curve (AUC) score of 0.90 for predicting no-shows in follow-up visits. The key findings from this study are: (1) secondary use of EHR data can be used to build datasets for predictive modeling and successfully predict patient no-shows in pediatric ophthalmology, (2) models predicting no-shows for follow-up visits are more accurate than those for new patient visits, and (3) the performance of predictive models is more robust in predicting no-shows compared to individual important features. We hope these models will be used for more effective interventions to mitigate the impact ofpatient no-shows.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8075453PMC
June 2021

Neurodevelopmental outcomes in preterm infants with retinopathy of prematurity.

Surv Ophthalmol 2021 Sep-Oct;66(5):877-891. Epub 2021 Mar 2.

Casey Eye Institute, Department of Ophthalmology, Oregon Health and Science University, Portland, OR, USA. Electronic address:

Over the past decade there has been a paradigm shift in the treatment of retinopathy of prematurity (ROP) with the introduction of antivascular endothelial growth factor (anti-VEGF) treatments. Anti-VEGF agents have the advantages of being easier to administer, requiring less anesthesia, having the potential for improved peripheral vision, and producing less refractive error than laser treatment. On the other hand, it is known that intravitreal administration of anti-VEGF agents lowers VEGF levels in the blood and raises the theoretical concern of intraocular anti-VEGF causing deleterious effects in other organ systems, including the brain. As a result, there has been increased attention recently on neurodevelopmental outcomes in infants treated with anti-VEGF agents. These studies should be put into context with what is known about systemic comorbidities, socioeconomic influences, and the effects of extreme prematurity itself on neurodevelopmental outcomes. We summarize what is known about neurodevelopmental outcomes in extremely preterm infants with ROP, discuss the implications for determining the neurodevelopmental status using neurodevelopmental testing as well as other indicators, and review the existing literature relating to neurodevelopmental outcomes in babies treated for ROP.
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http://dx.doi.org/10.1016/j.survophthal.2021.02.012DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8351023PMC
March 2021

Identification of candidate genes and pathways in retinopathy of prematurity by whole exome sequencing of preterm infants enriched in phenotypic extremes.

Sci Rep 2021 03 2;11(1):4966. Epub 2021 Mar 2.

Department of Ophthalmology, Casey Eye Institute, Oregon Health and Science University, 3375 SW Terwilliger Boulevard, Portland, OR, 97239, USA.

Retinopathy of prematurity (ROP) is a vasoproliferative retinal disease affecting premature infants. In addition to prematurity itself and oxygen treatment, genetic factors have been suggested to predispose to ROP. We aimed to identify potentially pathogenic genes and biological pathways associated with ROP by analyzing variants from whole exome sequencing (WES) data of premature infants. As part of a multicenter ROP cohort study, 100 non-Hispanic Caucasian preterm infants enriched in phenotypic extremes were subjected to WES. Gene-based testing was done on coding nonsynonymous variants. Genes showing enrichment of qualifying variants in severe ROP compared to mild or no ROP from gene-based tests with adjustment for gestational age and birth weight were selected for gene set enrichment analysis (GSEA). Mean BW of included infants with pre-plus, type-1 or type 2 ROP including aggressive posterior ROP (n = 58) and mild or no ROP (n = 42) were 744 g and 995 g, respectively. No single genes reached genome-wide significance that could account for a severe phenotype. GSEA identified two significantly associated pathways (smooth endoplasmic reticulum and vitamin C metabolism) after correction for multiple tests. WES of premature infants revealed potential pathways that may be important in the pathogenesis of ROP and in further genetic studies.
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http://dx.doi.org/10.1038/s41598-021-83552-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7925531PMC
March 2021

Applications of Artificial Intelligence for Retinopathy of Prematurity Screening.

Pediatrics 2021 03;147(3)

Athinoula A. Martinos Center for Biomedical Imaging and Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts.

Objectives: Childhood blindness from retinopathy of prematurity (ROP) is increasing as a result of improvements in neonatal care worldwide. We evaluate the effectiveness of artificial intelligence (AI)-based screening in an Indian ROP telemedicine program and whether differences in ROP severity between neonatal care units (NCUs) identified by using AI are related to differences in oxygen-titrating capability.

Methods: External validation study of an existing AI-based quantitative severity scale for ROP on a data set of images from the Retinopathy of Prematurity Eradication Save Our Sight ROP telemedicine program in India. All images were assigned an ROP severity score (1-9) by using the Imaging and Informatics in Retinopathy of Prematurity Deep Learning system. We calculated the area under the receiver operating characteristic curve and sensitivity and specificity for treatment-requiring retinopathy of prematurity. Using multivariable linear regression, we evaluated the mean and median ROP severity in each NCU as a function of mean birth weight, gestational age, and the presence of oxygen blenders and pulse oxygenation monitors.

Results: The area under the receiver operating characteristic curve for detection of treatment-requiring retinopathy of prematurity was 0.98, with 100% sensitivity and 78% specificity. We found higher median (interquartile range) ROP severity in NCUs without oxygen blenders and pulse oxygenation monitors, most apparent in bigger infants (>1500 g and 31 weeks' gestation: 2.7 [2.5-3.0] vs 3.1 [2.4-3.8]; = .007, with adjustment for birth weight and gestational age).

Conclusions: Integration of AI into ROP screening programs may lead to improved access to care for secondary prevention of ROP and may facilitate assessment of disease epidemiology and NCU resources.
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http://dx.doi.org/10.1542/peds.2020-016618DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7924138PMC
March 2021

Clinical Documentation as End-User Programming.

Proc SIGCHI Conf Hum Factor Comput Syst 2020 Apr;2020

Medical Informatics & Clinical Epidemiology, Oregon Health & Science University.

As healthcare providers have transitioned from paper to electronic health records they have gained access to increasingly sophisticated documentation aids such as custom note templates. However, little is known about how providers use these aids. To address this gap, we examine how 48 ophthalmologists and their staff create and use - a customizable and composable form of note template - to document office visits across two years. In this case study, we find 1) content-importing phrases were used to document the vast majority of visits (95%), 2) most content imported by these phrases was structured data imported by data-links rather than boilerplate text, and 3) providers primarily used phrases they had created while staff largely used phrases created by other people. We conclude by discussing how framing clinical documentation as end-user programming can inform the design of electronic health records and other documentation systems mixing data and narrative text.
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http://dx.doi.org/10.1145/3313831.3376205DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7901830PMC
April 2020

Deep Learning for the Diagnosis of Stage in Retinopathy of Prematurity: Accuracy and Generalizability across Populations and Cameras.

Ophthalmol Retina 2021 10 6;5(10):1027-1035. Epub 2021 Feb 6.

Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland, Oregon. Electronic address:

Purpose: Stage is an important feature to identify in retinal images of infants at risk of retinopathy of prematurity (ROP). The purpose of this study was to implement a convolutional neural network (CNN) for binary detection of stages 1, 2, and 3 in ROP and to evaluate its generalizability across different populations and camera systems.

Design: Diagnostic validation study of CNN for stage detection.

Participants: Retinal fundus images obtained from preterm infants during routine ROP screenings.

Methods: Two datasets were used: 5943 fundus images obtained by RetCam camera (Natus Medical, Pleasanton, CA) from 9 North American institutions and 5049 images obtained by 3nethra camera (Forus Health Incorporated, Bengaluru, India) from 4 hospitals in Nepal. Images were labeled based on the presence of stage by 1 to 3 expert graders. Three CNN models were trained using 5-fold cross-validation on datasets from North America alone, Nepal alone, and a combined dataset and were evaluated on 2 held-out test sets consisting of 708 and 247 images from the Nepali and North American datasets, respectively.

Main Outcome Measures: Convolutional neural network performance was evaluated using area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), sensitivity, and specificity.

Results: Both the North American- and Nepali-trained models demonstrated high performance on a test set from the same population: AUROC, 0.99; AUPRC, 0.98; sensitivity, 94%; and AUROC, 0.97; AUPRC, 0.91; and sensitivity, 73%; respectively. However, the performance of each model decreased to AUROC of 0.96 and AUPRC of 0.88 (sensitivity, 52%) and AUROC of 0.62 and AUPRC of 0.36 (sensitivity, 44%) when evaluated on a test set from the other population. Compared with the models trained on individual datasets, the model trained on a combined dataset achieved improved performance on each respective test set: sensitivity improved from 94% to 98% on the North American test set and from 73% to 82% on the Nepali test set.

Conclusions: A CNN can identify accurately the presence of ROP stage in retinal images, but performance depends on the similarity between training and testing populations. We demonstrated that internal and external performance can be improved by increasing the heterogeneity of the training dataset features of the training dataset, in this case by combining images from different populations and cameras.
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http://dx.doi.org/10.1016/j.oret.2020.12.013DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8364291PMC
October 2021

Evaluation of a Deep Learning-Derived Quantitative Retinopathy of Prematurity Severity Scale.

Ophthalmology 2021 07 27;128(7):1070-1076. Epub 2020 Oct 27.

Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland, Oregon; Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon. Electronic address:

Purpose: To evaluate the clinical usefulness of a quantitative deep learning-derived vascular severity score for retinopathy of prematurity (ROP) by assessing its correlation with clinical ROP diagnosis and by measuring clinician agreement in applying a novel scale.

Design: Analysis of existing database of posterior pole fundus images and corresponding ophthalmoscopic examinations using 2 methods of assigning a quantitative scale to vascular severity.

Participants: Images were from clinical examinations of patients in the Imaging and Informatics in ROP Consortium. Four ophthalmologists and 1 study coordinator evaluated vascular severity on a scale from 1 to 9.

Methods: A quantitative vascular severity score (1-9) was applied to each image using a deep learning algorithm. A database of 499 images was developed for assessment of interobserver agreement.

Main Outcome Measures: Distribution of deep learning-derived vascular severity scores with the clinical assessment of zone (I, II, or III), stage (0, 1, 2, or 3), and extent (<3 clock hours, 3-6 clock hours, and >6 clock hours) of stage 3 evaluated using multivariate linear regression and weighted κ values and Pearson correlation coefficients for interobserver agreement on a 1-to-9 vascular severity scale.

Results: For deep learning analysis, a total of 6344 clinical examinations were analyzed. A higher deep learning-derived vascular severity score was associated with more posterior disease, higher disease stage, and higher extent of stage 3 disease (P < 0.001 for all). For a given ROP stage, the vascular severity score was higher in zone I than zones II or III (P < 0.001). Multivariate regression found zone, stage, and extent all were associated independently with the severity score (P < 0.001 for all). For interobserver agreement, the mean ± standard deviation weighted κ value was 0.67 ± 0.06, and the Pearson correlation coefficient ± standard deviation was 0.88 ± 0.04 on the use of a 1-to-9 vascular severity scale.

Conclusions: A vascular severity scale for ROP seems feasible for clinical adoption; corresponds with zone, stage, extent of stage 3, and plus disease; and facilitates the use of objective technology such as deep learning to improve the consistency of ROP diagnosis.
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http://dx.doi.org/10.1016/j.ophtha.2020.10.025DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8076329PMC
July 2021
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