Deep Learning for the Diagnosis of Stage in Retinopathy of Prematurity

Chen, Jimmy; Coyner, Aaron S.; Ostmo, Susan; Sönmez, Kemal; Bajimaya, Sanyam; Pradhan, Eli; Valikodath, Nita; Cole, Emily; Al-Khaled, Tala; Chan, Robison Vernon Paul; Singh, Praveer; Kalpathy–Cramer, Jayashree; Chiang, Michael F.; Campbell, J. Peter

doi:10.1016/j.oret.2020.12.013

Cited by 35 publications

(21 citation statements)

References 47 publications

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“…In ML, the imbalanced data sets are critical problems because the small classes are often more useful, but standard classifiers tend to be weighed down by large classes, and the smaller ones are ignored . Studies of other ophthalmic diseases, such as glaucoma and retinopathy of prematurity, also faced the same problems in imbalanced data sets, which resulted in relatively low AUPRC values.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Models for Predicting Long-Term Visual Acuity in Highly Myopic Eyes

Wang,

Du,

Xie

et al. 2023

JAMA Ophthalmol

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ImportanceHigh myopia is a global concern due to its escalating prevalence and the potential risk of severe visual impairment caused by pathologic myopia. Using artificial intelligence to estimate future visual acuity (VA) could help clinicians to identify and monitor patients with a high risk of vision reduction in advance.ObjectiveTo develop machine learning models to predict VA at 3 and 5 years in patients with high myopia.Design, Setting, and ParticipantsThis retrospective, single-center, cohort study was performed on patients whose best-corrected VA (BCVA) at 3 and 5 years was known. The ophthalmic examinations of these patients were performed between October 2011 and May 2021. Thirty-four variables, including general information, basic ophthalmic information, and categories of myopic maculopathy based on fundus and optical coherence tomography images, were collected from the medical records for analysis.Main Outcomes and MeasuresRegression models were developed to predict BCVA at 3 and 5 years, and a binary classification model was developed to predict the risk of developing visual impairment at 5 years. The performance of models was evaluated by discrimination metrics, calibration belts, and decision curve analysis. The importance of relative variables was assessed by explainable artificial intelligence techniques.ResultsA total of 1616 eyes from 967 patients (mean [SD] age, 58.5 [14.0] years; 678 female [70.1%]) were included in this analysis. Findings showed that support vector machines presented the best prediction of BCVA at 3 years (R2 = 0.682; 95% CI, 0.625-0.733) and random forest at 5 years (R2 = 0.660; 95% CI, 0.604-0.710). To predict the risk of visual impairment at 5 years, logistic regression presented the best performance (area under the receiver operating characteristic curve = 0.870; 95% CI, 0.816-0.912). The baseline BCVA (logMAR odds ratio [OR], 0.298; 95% CI, 0.235-0.378; P &lt; .001), prior myopic macular neovascularization (OR, 3.290; 95% CI, 2.209-4.899; P &lt; .001), age (OR, 1.578; 95% CI, 1.227-2.028; P &lt; .001), and category 4 myopic maculopathy (OR, 4.899; 95% CI, 1.431-16.769; P = .01) were the 4 most important predicting variables and associated with increased risk of visual impairment at 5 years.Conclusions and RelevanceStudy results suggest that developing models for accurate prediction of the long-term VA for highly myopic eyes based on clinical and imaging information is feasible. Such models could be used for the clinical assessments of future visual acuity.

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Section: Discussionmentioning

confidence: 99%

Machine Learning Models for Predicting Long-Term Visual Acuity in Highly Myopic Eyes

Wang,

Du,

Xie

et al. 2023

JAMA Ophthalmol

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“…As an important clinical assistant tool, AI has been widely used in the early screening of retinal vein occlusion, and especially in areas where lacking medical resources, AI can play an important role. To assist in screening for retinal vein occlusion, Chen J. S et al (2021) constructed an AI screening model using four DL algorithms (ResNet-50, Inception-v3, DenseNet-121, SE-ReNeXt-50).…”

Section: Application Of Artificial Intelligence In Retinal Vascular D...mentioning

confidence: 99%

Research progress on diagnosing retinal vascular diseases based on artificial intelligence and fundus images

Liu

et al. 2023

Front. Cell Dev. Biol.

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As the only blood vessels that can directly be seen in the whole body, pathological changes in retinal vessels are related to the metabolic state of the whole body and many systems, which seriously affect the vision and quality of life of patients. Timely diagnosis and treatment are key to improving vision prognosis. In recent years, with the rapid development of artificial intelligence, the application of artificial intelligence in ophthalmology has become increasingly extensive and in-depth, especially in the field of retinal vascular diseases. Research study results based on artificial intelligence and fundus images are remarkable and provides a great possibility for early diagnosis and treatment. This paper reviews the recent research progress on artificial intelligence in retinal vascular diseases (including diabetic retinopathy, hypertensive retinopathy, retinal vein occlusion, retinopathy of prematurity, and age-related macular degeneration). The limitations and challenges of the research process are also discussed.

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“…Since 1984, the International Classification of Retinopathy of Prematurity (ICROP) has established guidelines for interpreting clinical findings from these exams to classify disease according to features including zone and stage of retinopathy, and presence of plus disease [7 ▪▪ ]. Despite longstanding acceptance of these recommendations, classification of ROP remains highly subjective and prone to interobserver variability, undermining standardization of management paradigms across providers and institutions [8–11,12 ▪▪ ]. This observation has driven exploration of alternative approaches for ROP diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Implementation of optical coherence tomography in retinopathy of prematurity screening

Hanif,

Jian,

Young

et al. 2024

Current Opinion in Ophthalmology

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Purpose of review In this review, we explore the investigational applications of optical coherence tomography (OCT) in retinopathy of prematurity (ROP), the insights they have delivered thus far, and key milestones for its integration into the standard of care. Recent findings While OCT has been widely integrated into clinical management of common retinal diseases, its use in pediatric contexts has been undermined by limitations in ergonomics, image acquisition time, and field of view. Recently, investigational handheld OCT devices have been reported with advancements including ultra-widefield view, noncontact use, and high-speed image capture permitting real-time en face visualization. These developments are compelling for OCT as a more objective alternative with reduced neonatal stress compared to indirect ophthalmoscopy and/or fundus photography as a means of classifying and monitoring ROP. Summary OCT may become a viable modality in management of ROP. Ongoing innovation surrounding handheld devices should aim to optimize patient comfort and image resolution in the retinal periphery. Future clinical investigations may seek to objectively characterize features of peripheral stage and explore novel biomarkers of disease activity.

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Deep Learning for the Diagnosis of Stage in Retinopathy of Prematurity

Cited by 35 publications

References 47 publications

Machine Learning Models for Predicting Long-Term Visual Acuity in Highly Myopic Eyes

Machine Learning Models for Predicting Long-Term Visual Acuity in Highly Myopic Eyes

Research progress on diagnosing retinal vascular diseases based on artificial intelligence and fundus images

Implementation of optical coherence tomography in retinopathy of prematurity screening

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