Journal of Shandong University (Health Sciences) ›› 2021, Vol. 59 ›› Issue (7): 43-49.doi: 10.6040/j.issn.1671-7554.0.2021.0031

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Machine learning models based on interpretive CMR parameters can predict the prognosis of pediatric myocarditis

TIAN Yaotian1, WANG Bao2, LI Yeqin1, WANG Teng1, TIAN Liwen1, HAN Bo3, WANG Cuiyan4   

  1. 1. School of Medicine, Shandong University, Jinan 250012, Shandong, China;
    2. Department of Radiology, Qilu Hospital of Shandong University, Jinan 250012, Shandong, China;
    3. Department of Pediatric Cardiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, Shandong, China;
    4. Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, Shandong, China
  • Online:2021-07-10 Published:2021-07-16

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Abstract: Objective To develop and validate machine learning models based on interpretive cardiac magnetic resonance(CMR)parameters for prognosis evaluation of pediatric myocarditis. Methods A retrospective analysis of 45 pediatric patients with myocarditis was conducted. According to whether adverse cardiac events(ACE)occurred, the patients were divided into poor prognosis group(n=18)and good prognosis group(n=27). CMR scans were performed after hospitalization and 206 interpretive CMR parameters about myocardial function, myocardial strain, first-pass perfusion and late gadolinium enhancement(LGE)were obtained. The parameters were trained by the classification learner App in MATLAB and the training model with the highest accuracy was chosen as the best model. The receiver-operating characteristics(ROC)curve of the machine learning model was drawn to determine the prognostic performance. Results A total of 14 CMR parameters were selected as predictive factors, and those without correlation were used to construct the combined parameters. Among all these parameters, maximal signal intensity percentage(SI %)of the 7th segment of AHA had the best performance(AUC: 0.790, sensitivity: 0.667, specificity: 0.833). Combined parameters achieved the highest performance(AUC: 0.940, sensitivity: 0.750, specificity: 0.889). Conclusion The machine learning models based on interpretive CMR parameters can be used for prognosis evaluation of pediatric myocarditis, and combination of interpretive CMR parameters training with machine learning is more accurate than single ones.

Key words: Cardiac magnetic resonance, Myocardial strain, Myocarditis, Machine learning, Prognosis

CLC Number: 

  • R574
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