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Tuberc Respir Dis > Volume 86(4); 2023 > Article |
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Study | Database (accessibility) | Datasets | Respiratory sounds | Methods | Results |
---|---|---|---|---|---|
Kim et al. (2021) [49] | Chungnam National University Hospital, Korea (private) | 297 Crackles, 298 wheezes, 101 rhonchi, and 1,222 normal sounds from 871 patients | Crackle, wheeze, rhonchi, and normal sounds | CNN classifier with transfer learning | Accuracy: 85.7% |
AUC: 0.92 | |||||
Chen et al. (2019) [18] | ICBHI dataset (public) | 136 Crackles, 44 wheezes, and 309 normal sounds | Crackle, wheeze, and normal sounds | OST and ResNets | Accuracy: 98.79% |
Sensitivity: 96.27% | |||||
Specificity: 100% | |||||
Grzywalski et al. (2019) [20] | Karol Jonscher University Hospital in Poznan, Poland (private) | 522 Respiratory sounds from 50 pediatric patients | Wheezes, rhonchi, and coarse and fine crackles | ANNs | F1-score: higher than that of pediatricians (8.4%) |
Meng et al. (2020) [12] | China-Japan Friendship Hospital, China (private) | 240 Crackles, 260 rhonchi, and 205 normal sounds from 130 patients | Crackle, rhonchi, and normal sounds | ANNs | Accuracy: 85.43% |
Kevat et al. (2020) [10] | Monash Children’s Hospital, Melbourne, Australia (private) | 192 Respiratory sounds from 25 pediatric patients | Crackle and wheeze | ANNs | True-positive rate |
Crackles: Clinicloud, 0.95; Littman, 0.75 | |||||
Wheeze: Clinicloud, 0.93; Littman, 0.8 | |||||
Chamberlain et al. (2016) [23] | Four separate clinical sites in Maharashtra, India (private) | 890 Respiratory sounds from 284 patients | Crackle and wheeze | SVM | AUC |
Crackle: 0.74 | |||||
Wheeze: 0.86 | |||||
Altan et al. (2020) [8] | Respiratory Database@TR (public) | 600 Respiratory sounds from 50 patients | Wheeze (COPD and non-COPD patients) | DBN classifier | Accuracy: 93.67% |
Sensitivity: 91% | |||||
Specificity: 96.33% | |||||
Fernandez-Granero et al. (2018) [50] | Puerta del Mar University Hospital in Cadiz, Spain (private) | 16 Patients with COPD | Wheeze (acute exacerbation of COPD) | DTF classifier with additional wavelet features | Accuracy: 78.0% |
Fraiwan et al. (2022) [13] | ICBHI dataset, KAUH dataset (public) | 1,483 Respiratory sounds from 213 patients | Asthma, COPD, bronchiectasis, pneumonia, heart failure and normal (control) patients | BDLSTM | Highest average accuracy: 99.62% |
Total agreement: 98.26% | |||||
Sensitivity: 98.43% | |||||
Specificity: 99.69% | |||||
Saldanha et al. (2022) [51] | ICBHI dataset (public) | 6,898 Respiratory sounds from 126 patients | Healthy, pneumonia, LRTI, URTI, bronchiectasis, bronchiolitis, COPD, asthma | MLP, CNN, LSTM, ResNet-50, Efficient Net B0 | Sensitivity: |
97% (MLP) | |||||
96% (CNN) | |||||
92% (LSTM) | |||||
98% (ResNet-50) | |||||
96% (Efficient Net B0) | |||||
Alqudah et al. (2022) [53] | ICBHI dataset, KAUH dataset (public) | 1,457 Respiratory sounds | Normal, asthma, bronchiectasis, bronchiolitis, COPD, LRTI, pneumonia, and URTI | CNN, LSTM, and hybrid model (CNN- LSTM) | Accuracy: |
99.62% (CNN) | |||||
99.25% (LSTM) | |||||
99.81% (CNN- LSTM) |
CNN: convolutional neural network; AUC: area under the curve; ICBHI: International Conference on Biomedical and Health Informatics; OST: optimized S-transform; ResNet: deep residual network; ANN: artificial neural networks; SVM: support vector machine; COPD: chronic obstructive pulmonary disease; DBN: deep belief network; DTF: decision tree forest; KAUH: King Abdullah University Hospital; BDLSTM: bidirectional long short-term memory network; LRTI: lower respiratory tract infection; URTI: upper respiratory tract infection; MLP: multi-layer perceptron; LSTM: long short-term memory.
Yoonjoo Kim
https://orcid.org/0000-0002-9028-0872
Taeyoung Ha
https://orcid.org/0000-0002-9440-1918
Chaeuk Chung
https://orcid.org/0000-0002-3978-0484
National Research Foundation of Korea
https://doi.org/10.13039/501100003725
2022R1F1A1076515
National Institute for Mathematical Sciences
B23910000
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