June 2022
Volume 63, Issue 7
Open Access
ARVO Annual Meeting Abstract  |   June 2022
Can the eye be a window to the lungs? AI predicts Bronchopulmonary Dysplasia through Retinal Fundus Photographs
Author Affiliations & Notes
  • Praveer Singh
    Radiology, Massachusetts General Hospital, Boston, Massachusetts, United States
    Harvard Medical School, Boston, Massachusetts, United States
  • Aaron S Coyner
    Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, United States
  • Brian K Jordan
    Neonatology, Oregon Health & Science University, Portland, Oregon, United States
  • Robison Vernon Paul Chan
    Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, Illinois, United States
  • Susan R Ostmo
    Ophthalmology, Oregon Health & Science University, Portland, Oregon, United States
  • Kemal Sonmez
    Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, United States
  • Deniz Erdogmus
    Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts, United States
  • Cindy McEvoy
    Neonatology, Oregon Health & Science University, Portland, Oregon, United States
  • Michael F Chiang
    National Eye Institute, National Institutes of Health, Bethesda, Maryland, United States
  • J. Peter Campbell
    Ophthalmology, Oregon Health & Science University, Portland, Oregon, United States
  • Jayashree Kalpathy-Cramer
    Radiology, Massachusetts General Hospital, Boston, Massachusetts, United States
    Harvard Medical School, Boston, Massachusetts, United States
  • Footnotes
    Commercial Relationships   Praveer Singh None; Aaron Coyner None; Brian Jordan None; Robison Chan Novartis, Alcon, Code C (Consultant/Contractor), Genentech, Regeneron, Code F (Financial Support), Phoenix Technology Group, Code S (non-remunerative); Susan Ostmo None; Kemal Sonmez None; Deniz Erdogmus None; Cindy McEvoy None; Michael Chiang Novartis, Code C (Consultant/Contractor), Genentech, Code F (Financial Support), Inteleretina, Code O (Owner); J. Peter Campbell Boston AI, Code C (Consultant/Contractor), Genentech, Code F (Financial Support); Jayashree Kalpathy-Cramer Genentech, Code F (Financial Support), GE Research, Code F (Financial Support)
  • Footnotes
    Support  NSF grant 1622542, NIH grant R01EY19474, R01 EY031331, and P30 EY10572
Investigative Ophthalmology & Visual Science June 2022, Vol.63, 2985 – F0255. doi:
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      Praveer Singh, Aaron S Coyner, Brian K Jordan, Robison Vernon Paul Chan, Susan R Ostmo, Kemal Sonmez, Deniz Erdogmus, Cindy McEvoy, Michael F Chiang, J. Peter Campbell, Jayashree Kalpathy-Cramer; Can the eye be a window to the lungs? AI predicts Bronchopulmonary Dysplasia through Retinal Fundus Photographs. Invest. Ophthalmol. Vis. Sci. 2022;63(7):2985 – F0255.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : Bronchopulmonary Dysplasia (BPD) is the leading cause of serious pulmonary morbidity in premature infants. Recent work has found that retinal fundus photos (RFPs) can contain information relevant to systemic health in adults. In this study, we evaluated the hypothesis that RFPs obtained as part of ROP screening may predict a future diagnosis of BPD.

Methods : 5255 RFPs were collected from 871 patients as part of a multi-institutional Imaging and Informatics in Retinopathy of Prematurity (i-ROP) study. Apart from clinical information, the dataset comprised of Reference Standard diagnoses for Plus disease for all patients. All RFPs corresponding to either patients without a diagnosis of BPD or captured at PMA>=34 weeks were removed (since BPD is diagnosed at 36 weeks PMA), leaving 477 patients /1284 RFPs (Post Menstrual Age [PMA]- mean:32.43, std:0.88), which were then divided into Train, Val, and TestSets via 80:10:10 splits on patient level. A Deep Learning (DL) model was trained to predict BPD at 36 weeks using the TrainSet (1006 RFPs; BPD:429, Normal:577; Plus:13, Pre-Plus:23, Normal:970; [PMA]- mean:32.48, std:0.89). The best performing model with the highest AUC-ROC score on the ValSet (137 RFPs; BPD:61, Normal:76) was finally evaluated on the TestSet (141 RFPs; BPD:47, Normal:94; Plus:4, Pre-Plus:4, Normal:133; [PMA]- mean:32.27, std:0.74). To avoid the DL model learning any common biomarkers with ROP disease, a secondary model was trained with only Non- Plus/Pre-Plus images using a pruned TrainSet (970 RFPs; BPD:403, Normal:567; Plus:0, Pre-Plus:0, Normal:970; [PMA]- mean:32.47, std:0.89) and ValSet (127 RFPs; BPD:55, Normal:72), though the performance was reported on the original TestSet.

Results : The model trained with original TrainSet, performs with an overall AUC-ROC of 0.82 (image-level) and 0.86 (patient-level) on the TestSet (Fig.1). Performance improves to 0.84 (image-level) and 0.87 (patient-level) when the model is trained on a pruned TrainSet with only Non- Plus/Pre-plus images (Fig.2).

Conclusions : We found that a DL model trained on RFPs could predict a future diagnosis of BPD, even in babies with no clinical signs of ROP. In other words, the model isn’t just learning that BPD and ROP often occur in the same babies. Early identification of babies at high risk for BPD may facilitate interventional trials to reduce morbidity from BPD in the future.

This abstract was presented at the 2022 ARVO Annual Meeting, held in Denver, CO, May 1-4, 2022, and virtually.

 

 

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