Dataset Information

Detection of Potentially Severe Retinopathy of Prematurity by Remote Image Grading.

ABSTRACT: Importance:Telemedicine in retinopathy of prematurity (ROP) has the potential for delivering timely care to premature infants at risk for serious ROP. Objective:To describe the characteristics of eyes at risk for ROP to provide insights into what types of ROP are most easily detected early by image grading. Design, Setting, and Participants:Secondary analysis of eyes with referral-warranted (RW) ROP (stage 3 ROP, zone I ROP, plus disease) on diagnostic examination from the Telemedicine Approaches to Evaluating Acute-Phase Retinopathy of Prematurity (e-ROP) study was conducted from May 1, 2011, to October 31, 2013, in 1257 premature infants with birth weights less than 1251 g in 13 neonatal units in North America. Data analysis was performed between February 1, 2016, and June 5, 2017. Interventions:Serial imaging sessions with concurrent diagnostic examinations for ROP. Main Outcomes and Measures:Time of detecting RW-ROP on image evaluation compared with clinical examination. Results:In the e-ROP study, 246 infants (492 eyes) were included in the analysis; 138 (56.1%) were male. A total of 447 eyes had RW-ROP on diagnostic examination. Image grading in 123 infants (mean [SD] gestational age, 24.8 [1.4] weeks) detected RW-ROP earlier than diagnostic examination (early) in 191 (42.7%) eyes by about 15 days and detected RW-ROP in 123 infants (mean [SD] gestational age, 24.6 [1.5] weeks) at the same time (same) in 200 (44.7%) eyes. Most of the early eyes (153 [80.1%]) interpreted as being RW-ROP positive on imaging evaluation agreed with examination findings when the examination subsequently documented RW-ROP. At the sessions in which RW-ROP was first found by examination, stage 3 or more in 123 infants (mean [SD] gestational age, 24.8 [1.4] weeks) ROP was noted earlier on image evaluation in 151 of 191 early eyes (79.1%) and in 172 of 200 of same eyes (86.0%) (P?=?.08); the presence of zone I ROP was detected in 57 of 191 (29.8%) early eyes vs 64 of 200 (32.0%) same eyes (P?=?.90); and plus disease was noted in 30 of 191 (15.7%) early eyes and 45 of 200 (22.5%) same eyes (P?=?.08). Conclusions and Relevance:In both early and same eyes, zone I and/or stage 3 ROP determined a significant proportion of RW-ROP; plus disease played a relatively minor role. In most early RW-ROP eyes, the findings were consistent with clinical examination and/or image grading at the next session. Because ROP telemedicine is used more widely, development of standard approaches and protocols is essential.

SUBMITTER: Quinn GE

PROVIDER: S-EPMC6005697 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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Detection of Potentially Severe Retinopathy of Prematurity by Remote Image Grading.

Quinn Graham E GE Ying Gui-Shuang GS Pan Wei W Baumritter Agnieshka A Daniel Ebenezer E

JAMA ophthalmology 20170901 9

<h4>Importance</h4>Telemedicine in retinopathy of prematurity (ROP) has the potential for delivering timely care to premature infants at risk for serious ROP.<h4>Objective</h4>To describe the characteristics of eyes at risk for ROP to provide insights into what types of ROP are most easily detected early by image grading.<h4>Design, setting, and participants</h4>Secondary analysis of eyes with referral-warranted (RW) ROP (stage 3 ROP, zone I ROP, plus disease) on diagnostic examination from the ...[more]

PMID: 28796856

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Project description:ImportanceMeasurable competence derived from comprehensive and advanced training in grading digital images is critical in studies using a reading center to evaluate retinal fundus images from infants at risk for retinopathy of prematurity (ROP). Details of certification for nonphysician trained readers (TRs) have not yet been described.ObjectiveTo describe a centralized system for grading ROP digital images by TRs in the Telemedicine Approaches to Evaluating Acute-Phase Retinopathy of Prematurity (e-ROP) Study.Design, setting, and participantsMulticenter observational cohort study conducted from July 1, 2010, to June 30, 2014. The TRs were trained by experienced ROP specialists and certified to detect ROP morphology in digital retinal images under supervision of an ophthalmologist reading center director. An ROP reading center was developed with standard hardware, secure Internet access, and customized image viewing software with an electronic grading form. A detailed protocol for grading was developed. Based on results of TR gradings, a computerized algorithm determined whether referral-warranted ROP (RW-ROP; defined as presence of plus disease, zone I ROP, and stage 3 or worse ROP) was present in digital images from infants with birth weight less than 1251 g enrolled from May 25, 2011, through October 31, 2013. Independent double grading was done by the TRs with adjudication of discrepant fields performed by the reading center director.ExposureDigital retinal images.Main outcomes and measuresIntragrader and intergrader variability and monitoring for temporal drift.ResultsFour TRs underwent rigorous training and certification. A total of 5520 image sets were double graded, with 24.5% requiring adjudication for at least 1 component of RW-ROP. For individual RW-ROP components, the adjudication rate was 3.9% for plus disease, 12.4% for zone I ROP, and 16.9% for stage 3 or worse ROP. The weighted κ for intergrader agreement (n = 80 image sets) was 0.72 (95% CI, 0.52-0.93) for RW-ROP, 0.57 (95% CI, 0.37-0.77) for plus disease, 0.43 (95% CI, 0.24-0.63) for zone I ROP, and 0.67 (95% CI, 0.47-0.88) for stage 3 or worse ROP. The weighted κ for grade-regrade agreement was 0.77 (95% CI, 0.57-0.97) for RW-ROP, 0.87 (95% CI, 0.67-1.00) for plus disease, 0.70 (95% CI, 0.51-0.90) for zone I ROP, and 0.77 (95% CI, 0.57-0.97) for stage 3 or worse ROP.Conclusions and relevanceThese data suggest that the e-ROP system for training and certifying nonphysicians to grade ROP images under the supervision of a reading center director reliably detects potentially serious ROP with good intragrader and intergrader consistency and minimal temporal drift.

Project description:Summary We described a challenge named “Diabetic Retinopathy (DR)—Grading and Image Quality Estimation Challenge” in conjunction with ISBI 2020 to hold three sub-challenges and develop deep learning models for DR image assessment and grading. The scientific community responded positively to the challenge, with 34 submissions from 574 registrations. In the challenge, we provided the DeepDRiD dataset containing 2,000 regular DR images (500 patients) and 256 ultra-widefield images (128 patients), both having DR quality and grading annotations. We discussed details of the top 3 algorithms in each sub-challenges. The weighted kappa for DR grading ranged from 0.93 to 0.82, and the accuracy for image quality evaluation ranged from 0.70 to 0.65. The results showed that image quality assessment can be used as a further target for exploration. We also have released the DeepDRiD dataset on GitHub to help develop automatic systems and improve human judgment in DR screening and diagnosis. Graphical abstract Highlights • Provides the DeepDRiD dataset, performance evaluation, top methods and results• Presents deep learning approaches in DR image quality assessment and grading• Discusses the future work of DR automatic screening The bigger picture Diabetic retinopathy (DR) is the most common disease caused by diabetes. Challenges are held to address real-world issues encountered in the design of DR automated screening systems to advance the technology in this area. Thus, we described a challenge named "Diabetic Retinopathy (DR)—Grading and Image Quality Estimation Challenge" in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI 2020) for fundus image assessment and DR grading. The scientific community responded positively to the challenge. In the challenge, we provided a deep DR image dataset (DeepDRiD) containing regular DR images and ultra-widefield (UWF) DR images, both having image quality and DR grading diagnosis. We discussed details of the three best algorithms in each sub-challenges. The results by the top algorithms showed that image quality assessment can be used as a target for further exploration. In DeepDRiD challenge, organizers hold a real-world exploration in diabetic retinopathy (DR) auto-screening systems using regular fundus images from 500 participants and ultra-widefield fundus images from 128 participants. Among the 34 participating teams, we summarized the top 3 teams in the three sub-challenges involved in DR grading and image quality assessment. In addition to providing new insights into image quality assessment strategy, these models can enhance the judgment of healthcare workers in DR screening and bring precise screening results.

Dataset Information

Detection of Potentially Severe Retinopathy of Prematurity by Remote Image Grading.

Publications

Detection of Potentially Severe Retinopathy of Prematurity by Remote Image Grading.

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