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

Table 1 General characteristics of the studies included in the analysis (part I)

Ref.	Publication date	Journal	Sample size1, N° pts/videos/images	Subjects	Main results
Arntfield et al[26]	22/02/2021	BMJ Open	243/612/121k	COVID +, COVID -, HPE	Overall Acc = 0.978AUC = 1/0.934/1 for COVID +, COVID -, HPE
Awatshi et al[27]	23/03/2021	IEEE Trans Ultrason Ferroelectr Freq Control	-/64/1.1k	COVID +, Healthy, PN	5-fold validation: Acc = 0.829
Barros et al[28]	14/08/2021	Sensors	131/185/-	COVID +, PN bacterial, Healthy	Best model (Xception+LSTM): Acc = 0.93 – Se = 0.97
Born et al[29]	12/01/2021	Applied Sciences	216/202/3.2k	COVID +, Healthy, PN	External validation: Se = 0.806 – Sp = 0.962
Born et al[30]	24/01/2021	ISMB TransMed	-/64/1.1k	COVID +, Healthy, PN	Overall Acc = 0.89Binarization COVID y/n: Se = 0.96 – Sp = 0.79 – F1score = 0.92
Chen et al[31]	29/06/2021	IEEE Trans Ultrason Ferroelectr Freq Control	31/45/1.6k	COVID-19 PN	5-fold validation: Acc = 0.87
Dastider et al[32]	20/02/2021	Comput Biol Med	29/60/14.3k	COVID-19 PN	Independent data validation: Acc = 0.677 – Se = 0.677 – Sp = 0.768 – F1score = 0.666
Diaz Escobar et al[33]	13/08/2021	PLos One	216/185/3.3k	COVID +, PN bacterial, Healthy	Best model (InceptionV3): Acc = 0.891 – AUC = 0.971
Erfanian Ebadi et al[34]	04/08/2021	Inform Med Unlocked	300/1.5k/288k	COVID +, PN	5-fold validation: Acc = 0.90 – PP=0.95
Hu et al[35]	20/03/2021	BioMed Eng OnLine	108/-/5.7k	COVID +	COVID detection: Acc = 0.944 – PP = 0.823 – Se = 0.763 – Sp=0.964
La Salvia et al[36]	03/08/2021	Comput Biol Med	450/5.4k/> 60k	Hospitalised COVID-19	External validation (ResNet50): Acc = 0.979 – PP=0.978 – F1score = 0.977 – AUC = 0.998
Mento et al[37]	27/05/2021	J Acoust Soc Am	82/1.5k/315k	COVID-19 confirmed	% Agreement DL and LUS = 96%
Roy et al[38]	14/05/2020	IEEE Trans	35/277/58.9k	COVID-19 confirmed, COVID-19 suspected, Healthy	Segmentation: Acc = 0.96 – DICE = 0.75
Sadik et al[39]	09/07/2021	Health Inf Sci Syst	-/123/41.5k	COVID +, PN, Healthy	COVID y/n (VGG19+SpecMen): PP = 0.81 – F1score = 0.89
Muhammad et al[40]	25/02/2021	Information Fusion	121 videos + 40 frames	COVID +, PN bacterial, Healthy	Overall: Acc = 0.918 – PP = 0.925
Tsai et al[41]	08/03/2021	Phys Med	70/623/99.2k	Healthy, Pleural effusion pts	Pleural effusion detection:Acc = 0.924
Xue et al[42]	20/01/2021	Med Image Anal	313/-/6.9k	COVID-19 confirmed	4-level and binary disease severity:Acc = 0.75 and Acc = 0.85

¹k: Indicates × 10³.

pts: Patients; HPE: Hydrostatic pulmonary edema; PN: Pneumonia; Acc: Accuracy; Se: Sensitivity; Sp: Specificity; AUC: Area under the curve; PP: Precision; DL: Deep learning; LUS: Lung ultrasound.

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.