In this Journal Club episode, Ben and Daphna explore an exciting new frontier in neonatology: oculomics. Reviewing a recent paper from JAMA Ophthalmology, they discuss how deep learning models applied to routine ROP screening images can predict the development of BPD and pulmonary hypertension in preterm infants. By combining visual features extracted via neural networks with standard demographic data, researchers achieved impressive predictive accuracy weeks before clinical diagnosis is typically made. Tune in to hear how the eyes might just be the window to the neonatal pulmonary vasculature!

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Deep Learning-Based Prediction of Cardiopulmonary Disease in Retinal Images of Premature Infants. Singh P, Kumar S, Tyagi R, Young BK, Jordan BK, Scottoline B, Evers PD, Ostmo S, Coyner AS, Lin WC, Gupta A, Erdogmus D, Chan RVP, McCourt EA, Barry JS, McEvoy CT, Chiang MF, Campbell JP, Kalpathy-Cramer J.JAMA Ophthalmol. 2026 Jan 22:e255814. doi: 10.1001/jamaophthalmol.2025.5814. Online ahead of print.PMID: 41569552

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As always, feel free to send us questions, comments, or suggestions to our email: nicupodcast@gmail.com. You can also contact the show through Instagram or Twitter, @nicupodcast. Or contact Ben and Daphna directly via their Twitter profiles: @drnicu and @doctordaphnamd. The papers discussed in today's episode are listed and timestamped on the webpage linked below.

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