Retinopathy of Prematurity (ROP) is a vascular disease affecting the retinas (back of the
eye) of low birth weight infants. Although it can be treated effectively if diagnosed early,
it continues to be a leading cause of childhood blindness in the United States and throughout
the world. The investigators feel that this study will result in specific knowledge discovery
about ROP, as well as general knowledge about how image-based data and genetic data can be
combined to better understand clinical disease.
Participants will be recruited from the neonatal intensive care unit (NICU) at OHSU, along
with 4 collaborating institutions (William Beaumont Hospital, Stanford University, University
of Illinois Chicago and University of Utah). Hospitalized infants who receive ROP screening
examinations for routine care will be eligible for this study, and will be offered the
opportunity to participate. Subjects who provide informed consent will have clinical data
from routine care collected along with demographic characteristics, results from routine ROP
screening examinations, presence of systemic disease or risk factors. Retinal photographs
will be taken during these routine eye exams, using a commercially-available camera that has
been FDA-cleared for taking pictures from retinas of premature infants. These retinal
pictures do not contain any identifiable patient information, and are taken as routine
standard of care.
The long-term goal of this research is to establish a quantitative framework for retinopathy
of prematurity (ROP) care based on clinical, imaging, genetic, and informatics principles.
The investigators have previously recruited and rigorously phenotyped and genotyped a large
study cohort, including implementation of a novel reference standard diagnosis; and built a
world-class research consortium for image, genetic, and bioinformatics analysis.
Description
This NIH funded multi-center study began July 2011 with 8 study sites approved by their
individual IRBs. Recruitment and data was conducted at the following sites: OHSU, Columbia
Universtiy, Cornell College, William Beaumont Hospital, Children's Hospital LA, University of
Miami, University of Illinois Chicago, Cedars Sinai Medical Center and Asociacion para Evitar
la Ceguera (APEC) in Mexico City. For the competitive renewal of the grant which begins
6-01-20, the recruitment sites have been reduced to 5 which include OHSU, William Beaumont,
University of Illinois Chicago, University of Utah and Stanford University.
This study will aim to develop a quantitative framework for ROP care using artificial
intelligence and analytics to improve clinical disease management. The investigators will
evaluate performance of an artificial intelligence system for ROP diagnosis and screening
prospectively. This will include: (a) recruit a target of over 2000 eye exams including
wide-angle retinal images from 375 subjects at 5 centers, (b) optimize an image quality
detection algorithm the investigators have recently developed, and (c) analyze system
accuracy for ROP diagnosis (plus vs. pre-plus vs. normal) and screening (using a novel
quantitative vascular severity scale).
The proposed work will study infants who will receive routine ophthalmoscopic exams and have
retinal images taken at each exam according to the standard of care at each institution. At
least one person at each site is trained to capture wide-angle retinal images using a
commercially-available camera (RetCam; Natus, Pleasanton, CA). This device is FDA-cleared for
premature infants, and has been used throughout the world for 20 years with no known
complications.
All participating infants will undergo retinal photography by trained study personnel for up
to 3 eye exams, or more if clinically indicated and feasible. "Outborn infants," who were
transferred to the study center for specialized ROP care, will have at least one set of
images taken if this is clinically indicated and feasible. These coded retinal images will be
read and interpreted by remote expert graders using the secure web-based system developed for
this study 9 years ago at OHSU. The de-identified images will be housed indefinitely in an
OHSU IRB-approved repository for possible future research studies or for other educational
purposes.
Most infants recruited from the first 9 years of this study between July 2011 and May 2020
had DNA collected from blood or saliva samples. The coded genetic samples are housed in an
OHSU IRB-approved repository and will be analyzed by outside collaborators for specific aim 3
of this study. Note that this current study does not involve new collection of any blood or
saliva samples.
In recent years, our team has successfully developed competitive image assessment methods to
infer ROP status using (i) engineered image features based on translating descriptive and
visual descriptions related to expert assessment . and (ii) deep-learned features based on
end-to-end training of neural networks for image analysis. To improve model explainability,
the models must not only provide classification (diagnostic) labels or severity scores, but
also supplementary information regarding how a model produces its decisions and what about a
particular image drives the decision. To this end, it is helpful for a model to (i) visualize
its training data; and (ii) illustrate which features of the input image its decision relied
heavily on. Visualization can provide an overarching demonstration of how a model produces
its decisions across a dataset with known clinical and demographic characteristics, which
contributes to overall interpretability of the model's logic. Illustration is essential to
gain trust and to facilitate validation when clinicians rely on the model to assess a
particular instance for various purposes, including training or regulatory approval.
While training the AI system in previous work, the investigators excluded 5% of images that
were rated by the majority of graders as "not acceptable quality". For real-world use, it
will be important to balance imageability with diagnostic performance. The investigators
propose to evaluate our existing dataset to determine the optimal operating point in the CNN
quality algorithm that balances imageability with diagnostic performance of the i-ROP DL
classifier. The investigators will continue those studies to systematically examine their
impact on improving image quality and diagnostic performance - and maximize rigor and
reproducibility of study design. This operating point will then be "locked", and the closed
system will be used as below.
Prospective evaluation of i-ROP DL classifier: The investigators propose to calculate the
weighted kappa between the RSD and the i-ROP DL system, along with sensitivity, specificity,
and imageability based on the optimal operating points identified above.
Prospective evaluation of vascular severity score: In a cross sectional analysis, the
investigators will test the hypothesis that the ROP vascular severity score, derived from the
i-ROP DL classifier, may demonstrate high sensitivity both for detection of plus disease and
for identifying treatment-requiring disease in a real-world ROP screening population.