IMPORTANCE: Retinopathy of prematurity (ROP) is a leading cause of childhood blindness worldwide. The decision to treat is primarily based on the presence of plus disease, defined as dilation and ...tortuosity of retinal vessels. However, clinical diagnosis of plus disease is highly subjective and variable. OBJECTIVE: To implement and validate an algorithm based on deep learning to automatically diagnose plus disease from retinal photographs. DESIGN, SETTING, AND PARTICIPANTS: A deep convolutional neural network was trained using a data set of 5511 retinal photographs. Each image was previously assigned a reference standard diagnosis (RSD) based on consensus of image grading by 3 experts and clinical diagnosis by 1 expert (ie, normal, pre–plus disease, or plus disease). The algorithm was evaluated by 5-fold cross-validation and tested on an independent set of 100 images. Images were collected from 8 academic institutions participating in the Imaging and Informatics in ROP (i-ROP) cohort study. The deep learning algorithm was tested against 8 ROP experts, each of whom had more than 10 years of clinical experience and more than 5 peer-reviewed publications about ROP. Data were collected from July 2011 to December 2016. Data were analyzed from December 2016 to September 2017. EXPOSURES: A deep learning algorithm trained on retinal photographs. MAIN OUTCOMES AND MEASURES: Receiver operating characteristic analysis was performed to evaluate performance of the algorithm against the RSD. Quadratic-weighted κ coefficients were calculated for ternary classification (ie, normal, pre–plus disease, and plus disease) to measure agreement with the RSD and 8 independent experts. RESULTS: Of the 5511 included retinal photographs, 4535 (82.3%) were graded as normal, 805 (14.6%) as pre–plus disease, and 172 (3.1%) as plus disease, based on the RSD. Mean (SD) area under the receiver operating characteristic curve statistics were 0.94 (0.01) for the diagnosis of normal (vs pre–plus disease or plus disease) and 0.98 (0.01) for the diagnosis of plus disease (vs normal or pre–plus disease). For diagnosis of plus disease in an independent test set of 100 retinal images, the algorithm achieved a sensitivity of 93% with 94% specificity. For detection of pre–plus disease or worse, the sensitivity and specificity were 100% and 94%, respectively. On the same test set, the algorithm achieved a quadratic-weighted κ coefficient of 0.92 compared with the RSD, outperforming 6 of 8 ROP experts. CONCLUSIONS AND RELEVANCE: This fully automated algorithm diagnosed plus disease in ROP with comparable or better accuracy than human experts. This has potential applications in disease detection, monitoring, and prognosis in infants at risk of ROP.
The advent of computer graphic processing units, improvement in mathematical models and availability of big data has allowed artificial intelligence (AI) using machine learning (ML) and deep learning ...(DL) techniques to achieve robust performance for broad applications in social-media, the internet of things, the automotive industry and healthcare. DL systems in particular provide improved capability in image, speech and motion recognition as well as in natural language processing. In medicine, significant progress of AI and DL systems has been demonstrated in image-centric specialties such as radiology, dermatology, pathology and ophthalmology. New studies, including pre-registered prospective clinical trials, have shown DL systems are accurate and effective in detecting diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD), retinopathy of prematurity, refractive error and in identifying cardiovascular risk factors and diseases, from digital fundus photographs. There is also increasing attention on the use of AI and DL systems in identifying disease features, progression and treatment response for retinal diseases such as neovascular AMD and diabetic macular edema using optical coherence tomography (OCT). Additionally, the application of ML to visual fields may be useful in detecting glaucoma progression. There are limited studies that incorporate clinical data including electronic health records, in AL and DL algorithms, and no prospective studies to demonstrate that AI and DL algorithms can predict the development of clinical eye disease. This article describes global eye disease burden, unmet needs and common conditions of public health importance for which AI and DL systems may be applicable. Technical and clinical aspects to build a DL system to address those needs, and the potential challenges for clinical adoption are discussed. AI, ML and DL will likely play a crucial role in clinical ophthalmology practice, with implications for screening, diagnosis and follow up of the major causes of vision impairment in the setting of ageing populations globally.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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.
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.
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.
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.
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.
The National Eye Institute (NEI) has been a world leader in directing and funding eye and vision research since 1968, when Congress and President Lyndon Johnson established it as an independent ...entity within the National Institutes of Health (NIH) to manage national efforts in vision science.1 The current annual NEI budget is $835 million. In the United States, groups including people of color, women, and people with disabilities are less represented in the vision workforce5-8 and have been found to have lower success rates in obtaining NIH research grants or fewer senior authorship publications in the ophthalmology literature.9-12 For example, a study from 2011 found that Asian grant applicants were 4 percentage points and Black or African-American applicants were 13 percentage points less likely to receive NIH investigator-initiated awards compared with White applicants.9 This is particularly important at a time when patient demographics are becoming more diverse in the United States,13 when there is increasing awareness of inequities in health care and the social determinants that contribute to those inequities, and when the societal impacts of unconscious bias and structural racism have become more clear. The COVID-19 pandemic has exposed many underlying health disparities in the United States and throughout the world, particularly in high-risk groups such as the elderly, children, women, people of color, and urban and rural underserved communities.14-18 This has reinforced the principles that scientific advances must be accessible to the entire population and that it is essential to recognize and eliminate bias in research studies. Based on the premise that visual loss and blindness is a leading cause of disability in the United States and that the associated public health and economic impact are enormous, especially when considering correlated problems such as lost productivity, mental health, and acceleration of dementia,19,31 our Strategic Plan highlights opportunities to develop large-scale representative epidemiologic databases on eye conditions through data sharing and harmonization of research methods.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
To systematically review published literature and identify consistency and variation in the aims, measures, and methods of studies using electronic health record (EHR) audit logs to observe clinical ...activities.
In July 2019, we searched PubMed for articles using EHR audit logs to study clinical activities. We coded and clustered the aims, measures, and methods of each article into recurring categories. We likewise extracted and summarized the methods used to validate measures derived from audit logs and limitations discussed of using audit logs for research.
Eighty-five articles met inclusion criteria. Study aims included examining EHR use, care team dynamics, and clinical workflows. Studies employed 6 key audit log measures: counts of actions captured by audit logs (eg, problem list viewed), counts of higher-level activities imputed by researchers (eg, chart review), activity durations, activity sequences, activity clusters, and EHR user networks. Methods used to preprocess audit logs varied, including how authors filtered extraneous actions, mapped actions to higher-level activities, and interpreted repeated actions or gaps in activity. Nineteen studies validated results (22%), but only 9 (11%) through direct observation, demonstrating varying levels of measure accuracy.
While originally designed to aid access control, EHR audit logs have been used to observe diverse clinical activities. However, most studies lack sufficient discussion of measure definition, calculation, and validation to support replication, comparison, and cross-study synthesis.
EHR audit logs have potential to scale observational research but the complexity of audit log measures necessitates greater methodological transparency and validated standards.
Despite increasing worldwide use of anti-vascular endothelial growth factor agents for treatment of retinopathy of prematurity (ROP), there are few data on their ocular efficacy, the appropriate drug ...and dose, the need for retreatment, and the possibility of long-term systemic effects. We evaluated the efficacy and safety of intravitreal ranibizumab compared with laser therapy in treatment of ROP.
This randomised, open-label, superiority multicentre, three-arm, parallel group trial was done in 87 neonatal and ophthalmic centres in 26 countries. We screened infants with birthweight less than 1500 g who met criteria for treatment for retinopathy, and randomised patients equally (1:1:1) to receive a single bilateral intravitreal dose of ranibizumab 0·2 mg or ranibizumab 0·1 mg, or laser therapy. Individuals were stratified by disease zone and geographical region using computer interactive response technology. The primary outcome was survival with no active retinopathy, no unfavourable structural outcomes, or need for a different treatment modality at or before 24 weeks (two-sided α=0·05 for superiority of ranibizumab 0·2 mg against laser therapy). Analysis was by intention to treat. This trial is registered with ClinicalTrials.gov, NCT02375971.
Between Dec 31, 2015, and June 29, 2017, 225 participants (ranibizumab 0·2 mg n=74, ranibizumab 0·1 mg n=77, laser therapy n=74) were randomly assigned. Seven were withdrawn before treatment (n=1, n=1, n=5, respectively) and 17 did not complete follow-up to 24 weeks, including four deaths in each group. 214 infants were assessed for the primary outcome (n=70, n=76, n=68, respectively). Treatment success occurred in 56 (80%) of 70 infants receiving ranibizumab 0·2 mg compared with 57 (75%) of 76 infants receiving ranibizumab 0·1 mg and 45 (66%) of 68 infants after laser therapy. Using a hierarchical testing strategy, compared with laser therapy the odds ratio (OR) of treatment success following ranibizumab 0·2 mg was 2·19 (95% Cl 0·99–4·82, p=0·051), and following ranibizumab 0·1 mg was 1·57 (95% Cl 0·76–3·26); for ranibizumab 0·2 mg compared with 0·1 mg the OR was 1·35 (95% Cl 0·61–2·98). One infant had an unfavourable structural outcome following ranibizumab 0·2 mg, compared with five following ranibizumab 0·1 mg and seven after laser therapy. Death, serious and non-serious systemic adverse events, and ocular adverse events were evenly distributed between the three groups.
In the treatment of ROP, ranibizumab 0·2 mg might be superior to laser therapy, with fewer unfavourable ocular outcomes than laser therapy and with an acceptable 24-week safety profile.
Novartis.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
PURPOSE OF REVIEWIn this article, we review the current state of artificial intelligence applications in retinopathy of prematurity (ROP) and provide insight on challenges as well as strategies for ...bringing these algorithms to the bedside.
RECENT FINDINGSIn the past few years, there has been a dramatic shift from machine learning approaches based on feature extraction to ‘deep’ convolutional neural networks for artificial intelligence applications. Several artificial intelligence for ROP approaches have demonstrated adequate proof-of-concept performance in research studies. The next steps are to determine whether these algorithms are robust to variable clinical and technical parameters in practice. Integration of artificial intelligence into ROP screening and treatment is limited by generalizability of the algorithms to maintain performance on unseen data and integration of artificial intelligence technology into new or existing clinical workflows.
SUMMARYReal-world implementation of artificial intelligence for ROP diagnosis will require massive efforts targeted at developing standards for data acquisition, true external validation, and demonstration of feasibility. We must now focus on ethical, technical, clinical, regulatory, and financial considerations to bring this technology to the infant bedside to realize the promise offered by this technology to reduce preventable blindness from ROP.
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