Skip to main content
. 2024 Aug 29;11:1408574. doi: 10.3389/fcvm.2024.1408574

Table 1.

Summary of identified publications for this review.

Objective Publication date Authors Input data (CMR sequences) Dataset Methodological considerations Sample Evaluation
To distinguish between myocarditis and MI 19.12.19 Di Noto et al. (43) Cines, SSFP, T2, LGE University Heart Center, University Hospital Zurich Exponential filter, wavelet transform filter, recursive feature elimination 173 patients (111 MI, 62 Myocarditis) Training (90%); Testing (10%). Nested 10-fold cross validation
To predict the presence of LGE in myocarditis patients 16.09.22 Cavallo et al. (44) Short Axis (STIR, PSIR), T1, T2, LGE Policlinico Tor Vergata, Rome, Italy Weka data mining platform
Correlation-based Feature Selection
Ensemble ML
19 patients Training (70%); Testing (30%)
10-fold cross validation
To distinguish myocarditis from healthy controls 04.01.22 Sharifrazi et al. (45) Cine, T1, T2, LGE (PSIR) Z-Alizadeh Sani myocarditis dataset Convolutional neural network combined with k means clustering 32 myocarditis patients, 15 healthy controls Training (90%);
Testing (10%)
To distinguish myocarditis from healthy controls 30.06.22 Moravvej et al. (46) Cine, T1, T2, LGE (PSIR) Z-Alizadeh Sani myocarditis dataset Reinforcement learning with population-based weights strategy 586 myocarditis patients, 307 healthy controls 5-fold cross validation
To distinguish myocarditis from healthy controls 26.10.22 Jafari et al. (47) Cine, T1, T2, LGE (PSIR) Z-Alizadeh Sani myocarditis dataset Turbulence Neural Transformer (TNT) architecture
Explainable-based Grad Cam method
32 myocarditis patients, 15 healthy controls 10-fold cross validation
To distinguish myocarditis from healthy controls 08.04.22 Ghareeb et al. (48) Cines, T1, T2, LGE(PSIR) Heart Hospital, Hamad Medical Corporation, Qatar Unsupervised learning- K means clustering, Bayesian factor analysis 169 patients Silhouette score

NB The reported sample sizes differ between the 3 studies using the Z-Alizadeh Sani myocarditis dataset– we are unable to ascertain the reasons for this and it is unclear in the manuscripts.