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Variability in Plus Disease Diagnosis using Single and Serial Images
Emily D Cole1, Shin Hae Park2, Sang Jin Kim3
1Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, Illinois.
Insights
Retinopathy of prematurity (ROP) diagnosis varied among graders using single versus serial retinal images. Deep learning shows potential for standardizing ROP assessment and treatment.
Area of Science:
- Ophthalmology
- Medical Imaging
- Artificial Intelligence in Medicine
Background:
- Retinopathy of prematurity (ROP) is a leading cause of blindness in premature infants.
- Accurate diagnosis and grading of ROP are critical for timely intervention.
- Current diagnostic methods rely on expert interpretation of retinal images, which can be subjective.
Purpose of the Study:
- To evaluate how the use of serial retinal images impacts the diagnosis of retinopathy of prematurity (ROP) compared to single images.
- To assess the variability in ROP grading among clinicians when using single versus serial imaging data.
- To explore the utility of a deep learning system in quantifying ROP severity.
Main Methods:
- A cohort study involving 15 ROP cases from the i-ROP consortium.
- Seven ophthalmologists graded single and serial retinal images for ROP severity (plus, preplus, none).
- A deep learning system (i-ROP) generated a vascular severity score (VSS) for each image.
Main Results:
- Over 50% of graders showed changes in ROP severity grading when using serial images, with significant inter-grader variability (Cohen's kappa 0.29-1.0).
- Serial imaging particularly influenced grading for preplus disease.
- The ROP VSS correlated well with expert classifications of plus disease and demonstrated agreement with disease progression.
Conclusions:
- Clinician variability in ROP diagnosis exists, influenced by the use of single versus serial retinal images.
- Deep learning-based quantitative assessment offers a promising approach to standardize ROP diagnosis and treatment.
Purpose:
To assess changes in retinopathy of prematurity (ROP) diagnosis in single and serial retinal images.
Design:
Cohort study.
Participants:
Cases of ROP recruited from the Imaging and Informatics in Retinopathy of Prematurity (i-ROP) consortium evaluated by 7 graders.
Methods:
Seven ophthalmologists reviewed both single and 3 consecutive serial retinal images from 15 cases with ROP, and severity was assigned as plus, preplus, or none. Imaging data were acquired during routine ROP screening from 2011 to 2015, and a reference standard diagnosis was established for each image. A secondary analysis was performed using the i-ROP deep learning system to assign a vascular severity score (VSS) to each image, ranging from 1 to 9, with 9 being the most severe disease. This score has been previously demonstrated to correlate with the International Classification of ROP. Mean plus disease severity was calculated by averaging 14 labels per image in serial and single images to decrease noise.
Main Outcome Measures:
Grading severity of ROP as defined by plus, preplus, or no ROP.
Results:
Assessment of serial retinal images changed the grading severity for > 50% of the graders, although there was wide variability. Cohen's kappa ranged from 0.29 to 1.0, which showed a wide range of agreement from slight to perfect by each grader. Changes in the grading of serial retinal images were noted more commonly in cases of preplus disease. The mean severity in cases with a diagnosis of plus disease and no disease did not change between single and serial images. The ROP VSS demonstrated good correlation with the range of expert classifications of plus disease and overall agreement with the mode class (P = 0.001). The VSS correlated with mean plus disease severity by expert diagnosis (correlation coefficient, 0.89). The more aggressive graders tended to be influenced by serial images to increase the severity of their grading. The VSS also demonstrated agreement with disease progression across serial images, which progressed to preplus and plus disease.
Conclusions:
Clinicians demonstrated variability in ROP diagnosis when presented with both single and serial images. The use of deep learning as a quantitative assessment of plus disease has the potential to standardize ROP diagnosis and treatment.
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