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Donnchadh M O’Sullivan on Muck Rack

Donnchadh M O’Sullivan

As seen in: Nature, BMJ Open
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Articles

Multicenter validation of AI-enabled ECG for pediatric biological sex prediction - npj Digital Medicine

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By Donnchadh M O’Sullivan, Joshua Mayourian, Scott Anjewierden, Kan Liu, Francisco Lopez-Jimenez, Paul Friedman, Tam Doan, Lance Patterson, Jennifer L. Dugan, Jonathan Johnson, Santiago O. Valdes, Santiago Blanco Valdés, Daniel Penny, Jeffrey Kim, Talha Niaz, Shaine A. Morris, Malini Madhavan, Zachi Itzhak Attia, John K. Triedman
| Nature Verified
Abstract Biological sex is closely linked to patterns embedded within the electrocardiogram (ECG) with essential health and disease implications. We report multicenter verification of an AI-enabled ECG model to predict biological sex across pediatric development. A previously published Mayo Clinic model confirmed puberty-linked AUROC gradient during external validation at Texas Children’s Hospital (pre-puberty AUROC 0.64, peri-puberty AUROC 0.84, post-puberty AUROC 0.94).
Open in Who Shared Issue with article?

Artificial intelligence in pediatric electrocardiography: Embracing the next decade of innovation

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By Donnchadh M O’Sullivan
| bcm.edu
AI,  artificial intelligence,  cardiology,  heart disease,  pediatric electrocardiography,  Technology In medicine, every decade brings new tools and methods that reshape how we diagnose and treat disease. Right now, I see artificial intelligence (AI) emerging as that transformative force — especially in the realm of pediatric electrocardiography (ECG).
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Pediatric sex estimation using AI-enabled ECG analysis: influence of pubertal development - npj Digital Medicine

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By Donnchadh M O’Sullivan, Scott Anjewierden, Itzhak Zachi Attia, Paul Friedman, Jason Anderson
| Nature Verified
Abstract AI-enabled ECGs have previously been shown to accurately predict patient sex in adults and correlate with sex hormone levels. We aimed to test the ability of AI-enabled ECGs to predict sex in the pediatric population and study the influence of pubertal development. AI-enabled ECG models were created using a convolutional neural network trained on pediatric 10-second, 12-lead ECGs. The first model was trained de novo using pediatric data.
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