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Grand Rounds: Machine Learning in Pediatrics - Hype or Hope


By Stephen Manu, MD , Akron Children's Hospital, Akron, OH

Heart Center

More about Stephen Manu, MD

Objectives (Educational Content) :

1. Provide a clear definition of the language surrounding machine learning and artificial intelligence. 2. Develop a basic understanding of machine learning and artificial intelligence. 3. Provide an overview of the current use of machine learning in the practice of pediatric medicine/pediatric cardiology. 4. Describe some of the current challenges of machine learning in medicine, particularly in pediatrics. 5. Describe the potential impact of machine learning in the future of pediatric medicine (Hype or Hope).

Target Audience:

General pediatricians, family physicians, nurse practitioners, physician assistants, social workers, psychologists, and nurses.

Identified Gap:

How machine learning will impact the practice of pediatric medicine in the future.

Estimated Time to Complete the Educational Activity:

1 hour(s)

Expiration Date for CE/CME Credit:


Method of Participation in the Learning Process:

The learner will view the presentation, successfully complete a post-test and complete an activity evaluation.

Evaluation Methods:

All learners must successfully complete a post-test, as well as an activity evaluation, to claim CE/CME credit.


Dr. Manu has indicated that there are no relevant financial or other relationships with any commercial interests and that this activity was developed independent of commercial interest.

Accreditation Statement:

Children’s Hospital Medical Center of Akron is accredited by the Ohio State Medical Association to provide continuing medical education for physicians.

CHMCA designates this enduring material activity for a maximum of 1.0 AMA PRA Category 1 Credit TM.  Physicians should only claim the credit commensurate with the extent of their participation in the activity.


1. Shelmerdine SC, Rosendahl K, Arthurs OJ. Artificial intelligence in paediatric radiology: international survey of health care professionals’ opinions. Pediatr Radiol. 2022;52(1):30-41. Doi:10.1007/s00247-021-05195-5

2. Zhai H, Brady P, Li Q, et al. Developing and Evaluating a Machine Learning Based Algorithm to Predict the Need of Pediatric Intensive Care Unit Transfer for Newly Hospitalized Children. Resuscitation. 2014;85(8):1065-1071. doi:10.1016/j.resuscitation.2014.04.009

3. Karimi-Bidhendi S, Arafati A, Cheng AL, Wu Y, Kheradvar A, Jafarkhani H. Fully-automated deep-learning segmentation of pediatric cardiovascular magnetic resonance of patients with complex congenital heart diseases. J Cardiovasc Magn Reson. 2020;22:80. doi:10.11886/s12968-02000678-0

4. Tang X, Kusmartseva I, Kulkami S, et al. Image-Based Machine Learning Algorithms for Disease Characterization in the Human Type 1 Diabetes Pancreas. The American Journal of Pathology. 2021;191(3):454-462. doi:10.1016/j.ajpath.2020.11-010

5. Garcia-Canadilla P, Sanchez-Martinez S, Crispi F, Bijnens B. Machine Learning in Fetal Cardiology: What to Expect. Fetal Diagn Ther. 2020;47(Suppl.5):363-372. doi:10.1159/000505021

6. Shmoish M, German A. Devir N, et al. Prediction of Adult Height by Machine Learning Technique. The Journal of Clinical Endocrinology & Metabolism. 2021;106(7):e2700-e2710. Doi:10.1210/clinem/dgab093

7. Laino ME, Ammirabile A, Posa A, et al. The applications of Artificial Intelligence in Chest Imaging of COVID-19 Patients: A Literature Review. Diagnostics (Basel). 2021;11(8):1317. Doi:10.3390/diagnostics11081317

8. Diller GP, Vahle J, radke R, et al. Utility of deep learning networks for the generation of artificial cardiac magnetic resonance images in congenital heart disease. BMC Med Imaging. 2020;20:L113. Doi:10.1186/s12880-020-00511-1.

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