Lauren Erdman, PhD

CANAIRI: the Collaboration for Translational Artificial Intelligence Trials in healthcare. McCradden, MD; London, AJ; Gichoya, JW; Sendak, M; Erdman, L; Stedman, I; Oakden-Rayner, L; Akrout, I; Anderson, JA; Farmer, L; Tikhomirov, L; van der Vegt, AH; Verspoor, K; Liu, X. Nature Medicine. 2025; 31:9-11.

Use of prenatal ultrasound findings to predict postnatal outcome in fetuses with lower urinary tract obstruction. Richter, J; Shinar, S; Erdman, L; Good, H; Kim, JK; Dos Santos, J; Khondker, A; Chua, M; Van Mieghem, T; Lorenzo, AJ; Rickard, M. Ultrasound in Obstetrics and Gynecology. 2024; 64:768-775.

The Hydronephrosis Severity Index guides paediatric antenatal hydronephrosis management based on artificial intelligence applied to ultrasound images alone. Erdman, L; Rickard, M; Drysdale, E; Skreta, M; Hua, SB; Sheth, K; Alvarez, D; Velaer, KN; Chua, ME; Dos Santos, J; Cooper, CS; Tasian, GE; Lorenzo, AJ; Golenberg, A. Scientific Reports. 2024; 14:22748.

Trade-Offs in Deep Learning Model Loss and Configuration for Sparse Histological Segmentation: A Case Study in Pediatric Ileal Histology. Heekin, S; Lopez-Nunez, O; Smith, J; Denson, N; Denson, L; Miethke, A; Dillman, J; Miraldi, E; Kofron, JM; Dhaliwal, J; Erdman, L. (2024) Institute of Electrical and Electronics Engineers (IEEE). 00:1-8.

Artificial Intelligence Tools in Pediatric Urology: A Comprehensive Review of Recent Advances. Chowdhury, AT; Salam, A; Naznine, M; Abdalla, D; Erdman, L; Chowdhury, ME H; Abbas, TO. Diagnostics. 2024; 14:2059.

Artificial Intelligence Tools in Pediatric Urology: A Comprehensive Assessment of the Landscape and Current Utilization. Ahmad, I; Khondker, A; Kwong, JC C; Erdman, L; Kim, JK; Dos Santos, J; Chua, M; Lorenzo, AJ; Rickard, M. Current treatment options in pediatrics. 2024; 10:88-100.

Development of machine learning models predicting mortality using routinely collected observational health data from 0-59 months old children admitted to an intensive care unit in Bangladesh: critical role of biochemistry and haematology data. Das, S; Erdman, L; Brals, D; Boczek, B; Tafsir Hasan, SM; Massara, P; Alam, MA; Fahim, SM; Mahfuz, M; Hoogendoorn, M; Zuiderent-Jerak, T; Bandsma, RH J; Ahmed, T; Voskuijl, W. BMJ Paediatrics Open. 2024; 8:e002365.

Early prediction of pediatric asthma in the Canadian Healthy Infant Longitudinal Development (CHILD) birth cohort using machine learning. He, P; Moraes, TJ; Dai, D; Reyna-Vargas, ME; Dai, R; Mandhane, P; Simons, E; Azad, MB; Hoskinson, C; Petersen, C; Del Bel, KL; Turvey, SE; Subbarao, P; Goldenberg, A; Erdman, L. Pediatric Research. 2024; 95:1818-1825.

Application of STREAM-URO and APPRAISE-AI reporting standards for artificial intelligence studies in pediatric urology: A case example with pediatric hydronephrosis. Khondker, A; Kwong, JC C; Rickard, M; Erdman, L; Kim, JK; Ahmad, I; Weaver, J; Fernandez, N; Tasian, GE; Kulkarni, GS; Lorenzo, AJ. Journal of Pediatric Urology. 2024; 20:455-467.

Deep-learning computer vision can identify increased nuchal translucency in the first trimester of pregnancy. Kasera, B; Shinar, S; Edke, P; Pruthi, V; Goldenberg, A; Erdman, L; Van Mieghem, T. Prenatal Diagnosis. 2024; 44:535-543.