Copyright
©The Author(s) 2022.
Artif Intell Med Imaging. Apr 28, 2022; 3(2): 42-54
Published online Apr 28, 2022. doi: 10.35711/aimi.v3.i2.42
Published online Apr 28, 2022. doi: 10.35711/aimi.v3.i2.42
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 |
- Citation: De Rosa L, L'Abbate S, Kusmic C, Faita F. Applications of artificial intelligence in lung ultrasound: Review of deep learning methods for COVID-19 fighting. Artif Intell Med Imaging 2022; 3(2): 42-54
- URL: https://www.wjgnet.com/2644-3260/full/v3/i2/42.htm
- DOI: https://dx.doi.org/10.35711/aimi.v3.i2.42