Table 2 Comparison of previous studies

Zhang et al. [23]

Nonenhanced cine

Fully connected discriminative network (DL)

299 patients

(MI: n = 212)

Detecting and delineating chronic MI. Classification as normal or infarcted myocardium.

-

0.94

Snaauw et al. [18]

Cine

DenseNet (DL)

ACDC

(DC, HCM, MI, ARV, NOR)

End-to-end diagnosis and segmentation.

78

-

Khened et al. [19]

Cine

Random forest method (ML)

ACDC

(DC, HCM, MI, ARV, NOR)

Fully automated segmentation and classification.

90

-

Ammar et al. [8]

Cine

Classifier ensemble combining multilayer perceptron, random forest, and support vector machine (ML)

ACDC

(DC, HCM, MI, ARV, NOR)

Automated pipeline for segmentation and prediction.

92

-

Agibetov et al. [20]

LGE

Cine

T1 mapping

VGG16 CNN pretrained on ImageNet (DL)

502 patients (CA: n = 82)

Detection of potential patterns of CA.

94

0.96

Ohta et al. [12]

MDE

GoogLeNet

AlexNet

ResNet-152 CNNs

(DL)

200 patients

Detection and classification of MDE patterns.

78.9 to

82.1

0.938 to

0.948

Martini et al. [21]

LGE

3 pretrained CNNs (DL),

Comparison to gradient boosting classifier (ML)

206 patients with suspected CA

Automated classification as amyloidosis present or absent based on average probability from the 3 CNNs.

88

(DL)

90

(ML)

0.982

(DL)

0.952

(ML)

El-Rewaidy et al. [22]

Native T1

mapping

Linear support vector machine and regression model (ML)

321 patients

(Control, HCM, DCM)

Texture analyses on myocardial native T1 mapping to differentiate between fibrosis patterns in patients with HCM and DCM.

89.3

-