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Key factors in a rigorous longitudinal image-based assessment of retinopathy of prematurity
Tatiana R Rosenblatt1, Marco H Ji2, Daniel Vail2
1Department of Ophthalmology, Byers Eye Institute, Stanford School of Medicine, 2452 Watson Court, Palo Alto, CA, 94303, USA. tatianar@stanford.edu.
Insights
A new database of telemedicine retinal images aids in assessing retinopathy of prematurity (ROP) progression. Expert grading of these images, particularly the central view and vascular tortuosity, shows promise for future AI-driven screening.
Area of Science:
- Ophthalmology
- Medical Imaging
- Artificial Intelligence
Background:
- Retinopathy of prematurity (ROP) requires regular monitoring for disease progression.
- Telemedicine offers a potential solution for remote ROP screening and management.
- Assessing longitudinal changes in ROP from retinal images is crucial for timely intervention.
Purpose of the Study:
- To create a longitudinally graded database of telemedicine retinal images for ROP.
- To serve as a comparator for studies on grader bias and ROP detection accuracy.
- To evaluate image parameters influencing the detection of ROP disease trajectory.
Main Methods:
- A cohort of 84 eyes from 42 patients underwent weekly telemedicine ROP screening over 6 weeks.
- De-identified images were graded by an ROP expert for improvement, worsening, or stability (gestalt score).
- Image views and retinal components were analyzed for agreement with gestalt scores using kappa statistics.
Main Results:
- The central image view demonstrated substantial agreement (κ=0.63) with gestalt scores.
- Vascular tortuosity showed the highest agreement (κ=0.42-0.61) among retinal components.
- Other views and components exhibited moderate to slight agreement with the overall clinical assessment.
Conclusions:
- A well-defined ROP telemedicine image database was established, graded by an expert.
- This database can support studies on ROP disease trajectory assessment and AI grading.
- It provides a foundation for expanding patient access to accurate ROP screening.
Abstract:
To describe a database of longitudinally graded telemedicine retinal images to be used as a comparator for future studies assessing grader recall bias and ability to detect typical progression (e.g. International Classification of Retinopathy of Prematurity (ICROP) stages) as well as incremental changes in retinopathy of prematurity (ROP). Cohort comprised of retinal images from 84 eyes of 42 patients who were sequentially screened for ROP over 6 consecutive weeks in a telemedicine program and then followed to vascular maturation or treatment, and then disease stabilization. De-identified retinal images across the 6 weekly exams (2520 total images) were graded by an ROP expert based on whether ROP had improved, worsened, or stayed the same compared to the prior week's images, corresponding to an overall clinical "gestalt" score. Subsequently, we examined which parameters might have influenced the examiner's ability to detect longitudinal change; images were graded by the same ROP expert by image view (central, inferior, nasal, superior, temporal) and by retinal components (vascular tortuosity, vascular dilation, stage, hemorrhage, vessel growth), again determining if each particular retinal component or ROP in each image view had improved, worsened, or stayed the same compared to the prior week's images. Agreement between gestalt scores and view, component, and component by view scores was assessed using percent agreement, absolute agreement, and Cohen's weighted kappa statistic to determine if any of the hypothesized image features correlated with the ability to predict ROP disease trajectory in patients. The central view showed substantial agreement with gestalt scores (κ = 0.63), with moderate agreement in the remaining views. Of retinal components, vascular tortuosity showed the most overall agreement with gestalt (κ = 0.42-0.61), with only slight to fair agreement for all other components. This is a well-defined ROP database graded by one expert in a real-world setting in a masked fashion that correlated with the actual (remote in time) exams and known outcomes. This provides a foundation for subsequent study of telemedicine's ability to longitudinally assess ROP disease trajectory, as well as for potential artificial intelligence approaches to retinal image grading, in order to expand patient access to timely, accurate ROP screening.
