Deep learning-based prediction of cardiopulmonary disease in retinal images of premature infants

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Abstract

ImportanceBronchopulmonary dysplasia (BPD) and pulmonary hypertension (PH) are leading causes of morbidity and mortality in premature infants. ObjectiveTo determine whether images obtained as part of retinopathy of prematurity (ROP) screening might contain features associated with BPD and PH in infants, and whether a multi-modal model integrating imaging features with demographic risk factors might outperform a model based on demographic risk alone. DesignA deep learning model was used to study retinal images collected from patients enrolled in the multi-institutional Imaging and Informatics in Retinopathy of Prematurity (i-ROP) study. SettingSeven neonatal intensive care units. Participants493 infants at risk for ROP undergoing routine ROP screening examinations from 2012 to 2020. Images were limited to <=34 weeks post-menstrual age (PMA) so as to precede the clinical diagnosis of BPD or PH. ExposureBPD was diagnosed by the presence of an oxygen requirement at 36 weeks PMA, and PH was diagnosed by echocardiogram at 34 weeks. A support vector machine model was trained to predict BPD, or PH, diagnosis using: A) image features alone (extracted using Resnet18), B) demographics alone, C) image features concatenated with demographics. To reduce the possibility of confounding with ROP, secondary models were trained using only images without clinical signs of ROP. Main Outcome MeasureFor both BPD and PH, we report performance on a held-out testset (99 patients from the BPD cohort and 37 patients from the PH cohort), assessed by the area under receiver operating characteristic curve. ResultsFor BPD, the diagnostic accuracy of a multimodal model was 0.82 (95% CI: 0.72-0.90), compared to demographics 0.72 (0.60-0.82; P=0.07) or imaging 0.72 (0.61-0.82; P=0.002) alone. For PH, it was 0.91 (0.71-1.0) combined compared to 0.68 (0.43-0.9; P=0.04) for demographics and 0.91 (0.78-1.0; P=0.4) for imaging alone. These associations remained even when models were trained on the subset of images without any clinical signs of ROP. Conclusions and RelevanceRetinal images obtained during ROP screening can be used to predict the diagnosis of BPD and PH in preterm infants, which may lead to earlier diagnosis and avoid the need for invasive diagnostic testing in the future. KEY POINTSO_ST_ABSQuestionC_ST_ABSCan an artificial intelligence (AI) algorithm diagnose bronchopulmonary dysplasia (BPD) or pulmonary hypertension (PH) in retinal images in preterm infants obtained during retinopathy of prematurity (ROP) screening examinations? FindingsAI was able to predict the presence of both BPD and PH in retinal images with higher accuracy than what could be predicted based on baseline demographic risk alone. MeaningDeploying AI models using images obtained during retinopathy of prematurity screening could lead to earlier diagnosis and avoid the need for more invasive diagnostic testing.

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