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Lauren Erdman, PhD


  • Assistant Professor, UC Department of Pediatrics

About

Biography

I am a computational researcher aiming to develop technologies to translate and transform pediatric clinical care using machine learning. My foundational training is in economics and statistics. I strive to improve the efficiency and impact of health interventions by getting more out of the information already collected through standards of care.

My work primarily focuses on extracting more information from biomedical imaging data, with the goal of enabling faster and easier interpretation by a larger share of clinicians, such as novices and those who don’t specialize in pediatrics.

I have already developed models for prediction in hydronephrosis ultrasound, pediatric echocardiography, hepatocellular carcinoma recurrence, irritable bowel disease (IBD) diagnosis and drug response, and the diagnosis of motile ciliopathy. I have been a researcher for over 10 years and began working at Cincinnati Children’s in 2023.

Publications

Predictors of weight gain in under-five children with severe acute malnutrition: A machine learning analysis of a tertiary care hospital dataset. Fahim, SM; Massara, P; Das, S; Alam, MA; Hasan, SMT; Brals, D; Erdman, L; Comelli, EM; Mahfuz, M; Voskuijl, W; Ahmed, T; Bandsma, RHJ. Clinical Nutrition ESPEN. 2026; 73:103119.

Does vesicoureteral reflux severity impact breakthrough UTI risk: a multi-institutional analysis and risk stratification. Khondker, A; Kwong, JC; Yadav, P; Kim, JK; Ahmad, I; Ansari, MS; Macneil, E; Keefe, DT; Lorenger, L; Reyes, I; Erdman, L; Cooper, CS; Rickard, M; Lorenzo, AJ. World Journal of Urology. 2026; 44(1).

Closed-loop stimulation modulates attention shifting in children. Warsi, NM; Wong, SM; Mithani, K; Coleman, SC; Arski, ON; Suresh, H; Germann, J; Boutet, A; Erdman, L; Venetucci Gouveia, F; Snead, OC; Kastner, S; Taylor, MJ; Ibrahim, GM. Nature Neuroscience. 2026.

Automated Eosinophil Quantification Using Deep Learning to Predict Therapy Escalation in Pediatric Ulcerative Colitis. Reigle, J; Liu, X; Lopez-Nunez, O; Drysdale, E; Qtaish, D; Walters, TD; Mack, D; Boyle, BM; Kugathasan, S; Denson, N; Denson, LA; Griffiths, AM; Siddiqui, I; Dhaliwal, J. Clinical and Translational Gastroenterology. 2026.

Underrepresentation of children in public medical imaging datasets. Hua, SBZ; Heller, N; He, P; Towbin, AJ; Chen, IY; Lu, AX; Erdman, L. Nature Health. 2026; 1-10.

A deep representation learning model to predict response to vagus nerve stimulation. Suresh, H; Mithani, K; Li, V; Latypov, TH; Warsi, NM; Wong, SM; Erdman, L; Kang, J; Germann, J; Gouveia, FV; Rutka, JT; Lopresti, MA; Jain, P; Ibrahim, GM. Nature Communications. 2026; 17(1).

Reporting of race and ethnicity in studies of artificial intelligence in pediatric urology: A secondary analysis of the AI-PEDURO online repository. Chae, HH; Nguyen, D-D; Khondker, A; Rickard, M; Erdman, L; Gabrielson, AT; Kim, JK; Kwong, JC; Chun, B; Abbas, T; Wang, H-HS; Weaver, J; Lorenzo, AJ; Nelson, CP. Journal of the Canadian Urological Association. 2026.

Automated right ventricular assessment in pediatric echocardiography via deep learning improves measurement reliability and reduces variability. He, P; Zhu, F; Vargas-Gutierrez, M; Kumar, R; Hui, W; Lin, Y; Friedberg, MK; Mertens, L; Erdman, L. Intelligence-Based Medicine. 2026; 13:100344.

The 2025 update on artificial intelligence models in pediatric urology: Results from the AI-PEDURO collaborative. Khondker, A; Ahmad, I; Dhalla, R; Kaushal, S; Kwong, JCC; Rickard, M; Erdman, L; Gabrielson, AT; Nguyen, D-D; Kim, JK; Nelson, CP; Wang, H-HS; Weaver, J; Lorenzo, AJ. Journal of Pediatric Urology. 2026; 22(1):105646.

Predicting non-response to urotherapy in pediatric bowel and bladder dysfunction: A machine learning approach. Dunning, JM; Khondker, A; Cooper, CS; Hansen, J; Rickard, M; Erdman, L; Santos, JD; Lorenzo, AJ; Storm, DW. Journal of Pediatric Urology. 2026; 22(1):105642.

From the Blog

Was This AI Tool Tested in Children?
Imaging Sciences

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Lauren Erdman, PhD4/24/2026