Applications of artificial intelligence in lung ultrasound: Review of deep learning methods for COVID-19 fighting

Table 2 General characteristics of the studies included in the analysis (part II)

Ref.	DL architecture	Input of DL models	Available dataset	Available code	Pre-trained/TL	Test independent	Data Augmentation	Explainability
Arntfield et al[26]	CNN	SF	No	Yes (on github)	Yes	Yes	Yes	Yes
Awatshi et al[27]	CNN	SF	No	Yes (on github)	Yes	No (five-fold)	Yes	Yes
Barros et al[28]	CNN+LSTM	SF	Yes	Yes (on github)	Yes	No(five-fold)	No	Yes
Born et al[29]	3D CNN	MF	Yes	Yes (on github)	Yes	No(five-fold)	Yes	Yes
Born et al[30]	CNN	SF	Yes	Yes (on github)	Yes	No(five-fold)	Yes	No
Chen et al[31]	MLFCNN	SF	No	Yes (on github)	No	No(five-fold)	No	No
Dastider et al[32]	CNN+LSTM	SF	No	Yes (on github)	Yes	No(five-fold)	Yes	Yes
Diaz Escobar et al[33]	CNN	SF	No	No	Yes	No(five-fold)	Yes	No
Erfanian Ebadi et al[34]	3D CNN	MF	No	Yes (on github)	Yes	No(five-fold)	No	Yes
Hu et al[35]	CNN + MCRF	SF	No	No	Yes	Yes	Yes	Yes
La Salvia et al[36]	CNN	SF	No	No	Yes	Yes	Yes	Yes
Mento et al[37]	CNN+ STN	SF	No	No	No	-	No	No
Roy et al[38]	CNN+ STN	SF	Yes (on request)	Yes (on github)	No	Yes	Yes	Yes
Sadik et al[39]	CNN	SF	No	No	Yes	Yes	Yes	Yes
Muhammad et al[40]	CNN	SF	Yes	No	No	No(five-fold)	Yes	Yes
Tsai et al[41]	CNN+ STN	MF	No	No	Yes	No(ten-fold)	No	No
Xue et al[42]	CNN	SF	No	No	No	Yes	Yes	Yes

CNN: Convolutional neural network; LSTM: Long short-term memory; MCRF: Multimodal channel and receptive field; MLFCNN: Multi-layer fully connected neural network; STN: Spatial transformer network; SF: Single-frame; MF: Multi-frame; DL: Deep learning; TL: Transfer learning.