Plus Disease in Retinopathy of Prematurity

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Retinopathy of prematurity (ROP), a leading cause of childhood blindness, is diagnosed by clinical ophthalmoscopic examinations or reading retinal images. Plus disease, defined as abnormal tortuosity and dilation of the posterior retinal blood vessels, is the most important feature to determine treatment-requiring ROP. We aimed to create a complete, publicly available and feature-extraction-based pipeline, I-ROP ASSIST, that achieves convolutional neural network (CNN)-like performance when diagnosing plus disease from retinal images. Methods: We developed two datasets containing 100 and 5512 posterior retinal images, respectively. After segmenting retinal vessels, we detected the vessel centerlines. Then, we extracted features relevant to ROP, including tortuosity and dilation measures, and used these features in the classifiers including logistic regression, support vector machine and neural networks to assess a severity score for the input. We tested our system with fivefold cross-validation and calculated the area under the curve (AUC) metric for each classifier and dataset. Results: For predicting plus versus not-plus categories, we achieved 99% and 94% AUC on the first and second datasets, respectively. For predicting pre-plus or worse versus normal categories, we achieved 99% and 88% AUC on the first and second datasets, respectively. The CNN method achieved 98% and 94% for predicting two categories on the second dataset. Conclusions: Our system combining automatic retinal vessel segmentation, tracing, feature extraction and classification is able to diagnose plus disease in ROP with CNNlike performance. Translational Relevance: The high performance of I-ROP ASSIST suggests potential applications in automated and objective diagnosis of plus disease.

Section: Methodsmentioning

confidence: 99%

Plus Disease in Retinopathy of Prematurity: Convolutional Neural Network Performance Using a Combined Neural Network and Feature Extraction Approach

Yildiz

Tian

Yıldız

et al. 2020

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“…Experts have poor absolute agreement on ROP vascular disease classification (normal, plus, or pre-plus) but good relative agreement on ROP disease severity. 33 As previously described, this finding motivated the development of a continuous vascular severity score using the i-ROP DL system. This continuous ROP vascular score provided a more quantitative diagnosis for plus disease and was associated with both the ICROP category of disease at a single point in time and clinical progression of ROP over time.…”

Section: Recommendations For Future Research In Ai Applied To Ropmentioning

confidence: 64%

“…Vascular disease in ROP presents on a continuum, as shown in Figure 2 , and experts have been shown to have poor absolute agreement on classification (normal, plus, or pre-plus), but good relative agreement on disease severity. 33 This finding motivated the development of a continuous vascular severity score using the i-ROP DL system. Redd et al reported that a scale from 1 to 9 could accurately detect type 1 ROP with an area under the curve of 0.95 and, in theory, could decrease the number of ophthalmoscopic examinations by 80% in a real-world telemedicine program because most babies will have no or mild disease.…”

Section: A Continuous Severity Score For Ropmentioning

confidence: 99%

Artificial Intelligence in Retinopathy of Prematurity Diagnosis

Scruggs

Chan

Kalpathy–Cramer

et al. 2020

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Citation: Scruggs BA, Chan RVP, Kalpathy-Cramer J, Chiang MF, Campbell JP. Artificial intelligence in retinopathy of prematurity diagnosis. Trans Vis Sci Tech. 2020;9(2):5, https://doi.org/10.1167/tvst.9.2.5Retinopathy of prematurity (ROP) is a leading cause of childhood blindness worldwide. The diagnosis of ROP is subclassified by zone, stage, and plus disease, with each area demonstrating significant intra-and interexpert subjectivity and disagreement. In addition to improved efficiencies for ROP screening, artificial intelligence may lead to automated, quantifiable, and objective diagnosis in ROP. This review focuses on the development of artificial intelligence for automated diagnosis of plus disease in ROP and highlights the clinical and technical challenges of both the development and implementation of artificial intelligence in the real world.

“…Intergrader variation in the diagnosis of plus disease has been well documented in the literature, 48 – 50 and may be attributable to grader-specific biases to undercall or overcall pre-plus and plus disease. 51 , 52 This can also apply to a single clinician who determined the plus disease at the bedside (our gold standard). The relatively low intergrader agreement for plus, pre-plus, or neither grades may therefore be due not to limitations of the RVSV-OCT montages themselves, but to unique disease thresholds for each grader, despite the existence of standardized grading criteria.…”

Section: Discussionmentioning

confidence: 99%

Auto-Processed Retinal Vessel Shadow View Images From Bedside Optical Coherence Tomography to Evaluate Plus Disease in Retinopathy of Prematurity

Seely

Wang

Tai

et al. 2020

To describe the creation of en face retinal vessel shadow view (RVSV) optical coherence tomography (OCT) images and assess the feasibility of using these for evaluating vascular disease in preterm infants at risk for retinopathy of prematurity (ROP). Methods: In this exploratory study, we selected images from eyes with a range of ROP vascular disease, prospectively acquired from preterm infants using an investigational, noncontact, handheld, bedside swept-source OCT. We autosegmented OCT volumes using custom infant-specific software, extracted RVSV-OCT images from volumetric data bracketed around the retinal pigment epithelium, and automontaged the resulting RVSV-OCT images. Three masked ophthalmologists graded the RVSV-OCT montages as plus, pre-plus, or neither and ranked them by relative vascular disease severity. Results: We selected images from 17 imaging sessions (7 plus, 4 pre-plus, 6 neither on clinical examination). On review, 15/17 (88%) RVSV-OCT montages were gradable for plus, pre-plus, or neither and all 17 montages were rankable for relative severity. Intergrader agreement for plus, pre-plus, or neither grading was good (κ, 0.67; 95% confidence interval, 0.42-0.86) and for relative severity ranking was excellent (intraclass correlation coefficient, 0.98; 95% confidence interval, 0.96-0.99). Conclusions: Our novel automatic processing method can create RVSV-OCT montages optimized for retinal vessel visualization for ROP screening. Although our data support the feasibility of using RVSV-OCT montages for ranking relative vascular disease severity, there is room for improved OCT image capture and processing methods in preterm infants screened for ROP. Translational Relevance: Creation and grading of RVSV-OCT images could eventually be integrated into an alternative method for ROP screening.