Dataset Information

Evaluation of vascular disease progression in retinopathy of prematurity using static and dynamic retinal images.

ABSTRACT:

Purpose

To measure accuracy and speed for detection of vascular progression in retinopathy of prematurity (ROP) from serial images. Two strategies are compared: static side-by-side presentation and dynamic flickering of superimposed image pairs.

Design

Prospective comparative study.

Methods

Fifteen de-identified, wide-angle retinal image pairs were taken from infants who eventually developed plus disease. Image pairs representing vascular disease progression were taken ?1 week apart, and control images without progression were taken on the same day. Dynamic flickering pairs were created by digital image registration. Ten experts independently reviewed each image pair on a secure website using both strategies, and were asked to identify progression or state that images were identical. Accuracy and speed were measured, using examination date and ophthalmoscopic findings as a reference standard.

Results

Using static images, experts were accurate in a mean (%) ± standard deviation (SD) of 11.4 of 15 (76%) ± 1.7 image pairs. Using dynamic flickering images, experts were accurate in a mean (%) ± SD of 11.3 of 15 (75%) ± 1.7 image pairs. There was no significant difference in accuracy between these strategies (P = .420). Diagnostic speed was faster using dynamic flickering (24.7 ± 8.3 seconds) vs static side-by-side images (40.3 ± 18.3 seconds) (P = .002). Experts reported higher confidence when interpreting dynamic flickering images (P = .001).

Conclusions

Retinal imaging provides objective documentation of vascular appearance, with potentially improved ability to recognize ROP progression compared to standard ophthalmoscopy. Speed of identifying vascular progression was faster by review of dynamic flickering image pairs than by static side-by-side images, although there was no difference in accuracy.

SUBMITTER: Myung JS

PROVIDER: S-EPMC3266465 | biostudies-literature | 2012 Mar

REPOSITORIES: biostudies-literature

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Publications

Evaluation of vascular disease progression in retinopathy of prematurity using static and dynamic retinal images.

Myung Jane S JS Gelman Rony R Aaker Grant D GD Radcliffe Nathan M NM Chan R V Paul RV Chiang Michael F MF

American journal of ophthalmology 20111022 3

<h4>Purpose</h4>To measure accuracy and speed for detection of vascular progression in retinopathy of prematurity (ROP) from serial images. Two strategies are compared: static side-by-side presentation and dynamic flickering of superimposed image pairs.<h4>Design</h4>Prospective comparative study.<h4>Methods</h4>Fifteen de-identified, wide-angle retinal image pairs were taken from infants who eventually developed plus disease. Image pairs representing vascular disease progression were taken ≥1 w ...[more]

PMID: 22019222

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Project description:ImportanceA retinopathy of prematurity (ROP) diagnosis currently relies on indirect ophthalmoscopy assessed by experienced ophthalmologists. A deep learning algorithm based on retinal images may facilitate early detection and timely treatment of ROP to improve visual outcomes.ObjectiveTo develop a retinal image-based, multidimensional, automated, deep learning platform for ROP screening and validate its performance accuracy.Design, setting, and participantsA total of 14 108 eyes of 8652 preterm infants who received ROP screening from 4 centers from November 4, 2010, to November 14, 2019, were included, and a total of 52 249 retinal images were randomly split into training, validation, and test sets. Four main dimensional independent classifiers were developed, including image quality, any stage of ROP, intraocular hemorrhage, and preplus/plus disease. Referral-warranted ROP was automatically generated by integrating the results of 4 classifiers at the image, eye, and patient levels. DeepSHAP, a method based on DeepLIFT and Shapley values (solution concepts in cooperative game theory), was adopted as the heat map technology to explain the predictions. The performance of the platform was further validated as compared with that of the experienced ROP experts. Data were analyzed from February 12, 2020, to June 24, 2020.ExposureA deep learning algorithm.Main outcomes and measuresThe performance of each classifier included true negative, false positive, false negative, true positive, F1 score, sensitivity, specificity, receiver operating characteristic, area under curve (AUC), and Cohen unweighted κ.ResultsA total of 14 108 eyes of 8652 preterm infants (mean [SD] gestational age, 32.9 [3.1] weeks; 4818 boys [60.4%] of 7973 with known sex) received ROP screening. The performance of all classifiers achieved an F1 score of 0.718 to 0.981, a sensitivity of 0.918 to 0.982, a specificity of 0.949 to 0.992, and an AUC of 0.983 to 0.998, whereas that of the referral system achieved an F1 score of 0.898 to 0.956, a sensitivity of 0.981 to 0.986, a specificity of 0.939 to 0.974, and an AUC of 0.9901 to 0.9956. Fine-grained and class-discriminative heat maps were generated by DeepSHAP in real time. The platform achieved a Cohen unweighted κ of 0.86 to 0.98 compared with a Cohen κ of 0.93 to 0.98 by the ROP experts.Conclusions and relevanceIn this diagnostic study, an automated ROP screening platform was able to identify and classify multidimensional pathologic lesions in the retinal images. This platform may be able to assist routine ROP screening in general and children hospitals.

Project description:PURPOSE:Bedside examination of premature infants at risk for retinopathy of prematurity (ROP) is predominantly performed with ophthalmoscopic en face viewing of the retina. While postmortem retinal microstructures have been studied at the vascular-avascular junction, a critical location for pathogenesis of ROP, to date this has not been possible in vivo. Here we present bedside, non-sedated in vivo cross-sectional imaging and analysis of retinal microstructures at the vascular-avascular junction in infants with ROP using handheld spectral-domain optical coherence tomography (SDOCT). DESIGN:Prospective observational study. PARTICIPANTS:Eleven preterm infants consented for research imaging during ROP screening examinations. METHODS:We imaged the vascular-avascular junction in the temporal retina using a SDOCT system (Envisu, Bioptigen Inc., NC) in 18 eyes from 11 preterm infants with zone I or II, stage 0 through 4 ROP. B-scan and en face images were analyzed and compared to historical light micrographs. MAIN OUTCOME MEASURES:SDOCT morphology at the vascular-avascular junction. RESULTS:Multiple bedside SDOCT findings at the vascular-avascular junction were comparable to historic light micrographs: thickened inner retinal ridge structure in stage 2 ROP was comparable to thickened vanguard and rear guard cells in micrographs; vascular tufts on the posterior retinal surface in stage 2 ROP, broad arcs of neovascularization above the retina in stage 3 ROP, and splitting of inner retinal layers into clefts on either side of neovascularization mimicked findings of historic light micrographs. A unique findings was thickening of the avascular inner retinal band adjacent to neovascularization. On SDOCT imaging over several weeks, neovascularization and retinal clefts diminished after intravitreal bevacizumab therapy. CONCLUSIONS:Retinal morphology at the vascular-avascular junction imaged with handheld SDOCT is consistent with known histopathology, and provide the advantage of monitoring change in vivo over time. These unique findings provide new insights into preterm retinal neurovascular development in ROP.

Dataset Information

Evaluation of vascular disease progression in retinopathy of prematurity using static and dynamic retinal images.

Purpose

Design

Methods

Results

Conclusions

Publications

Evaluation of vascular disease progression in retinopathy of prematurity using static and dynamic retinal images.

Similar Datasets

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