Advanced Ultrasound in Diagnosis and Therapy ›› 2023, Vol. 7 ›› Issue (2): 61-72.doi: 10.37015/AUDT.2023.230020
• Review Articles • Previous Articles Next Articles
Siyi Xun, MAa, Wei Ke, PhDa, Mingfu Jiang, MAa, Huachao Chen, BAa, Haoming Chen, BAa, Chantong Lam, PhDa, Ligang Cui, MDb,*(
), Tao Tan, PhDa,*()
Received:2023-03-29
Revised:2023-04-07
Accepted:2023-04-22
Online:2023-06-30
Published:2023-04-27
Contact:
Ligang Cui, MD, Tao Tan, PhD,
E-mail:ligangcui@pku.edu.cn;taotan@mpu.edu.mo
Siyi Xun, MA, Wei Ke, PhD, Mingfu Jiang, MA, Huachao Chen, BA, Haoming Chen, BA, Chantong Lam, PhD, Ligang Cui, MD, Tao Tan, PhD. Current Status, Prospect and Bottleneck of Ultrasound AI Development: A Systemic Review. Advanced Ultrasound in Diagnosis and Therapy, 2023, 7(2): 61-72.
Figure 1
The results of AI-based ultrasound image processing research in classification, segmentation and detection in the past ten years."
Figure 2
Keyword cluster analysis results of papers on the application of AI in ultrasound in the last decade. Different colors represent different classes, and the number label represents the size of the class. The smaller the number, the larger the corresponding class, and the higher the frequency of the keyword in papers we reviewed. In the figure, the three largest categories are: “#0 breast cancer”, “#1 computer-aided diagnosis system”, “#2 using carotid ultrasound”."
Figure 3
Application flow of AI model in ultrasound image classification, segmentation and detection. Take breast ultrasound images as an example, the image data is from Al-Dhabyani et al. [6]."
Table 1
Summary the areas of interest, methods, data sets and performance of major researches on AI in the field of medical ultrasound classification in recent years"
| Paper | Region | Method | Dataset | Performance |
|---|---|---|---|---|
| Cao et al. [ | Breast lesions | DenseNet | Private dataset Benign = 579 Malignant = 464 | APR = 0.9689 ARR = 0.6723 F1 = 0.7938 |
| Al-Dhabyani et al. [ | Breast lesions | CNN Transfer learning | BUSI dataset Total = 780 Normal = 133 Benign = 437 Malignant = 210 | Acc = 0.94 |
| Han et al. [ | Breast lesions | DDSTN | Private dataset Total = 106 Benign = 54 Malignant = 51 | Acc = 0.8679 ± 0.0154 Sen = 0.8645 ± 0.0144 Spe = 0.8731 ± 0.0437 |
| Zhou et al. [ | Breast lesions | Multi-task learning | Private dataset Total = 170 | Acc = 0.741 Rec = 0.798 Pre = 0.826 FPR = 0.392 F1 = 0.811 |
| Badawy et al. [ | Breast lesions | FCM U-Net | Private dataset Total = 1200 | Acc = 0.9544 F1 = 0.6807 |
| Bourouis et al. [ | Breast lesions | GWO-WNN | Private dataset Total = 346 Benign = 97 Malignant = 249 | Acc = 0.98 Sen = 0.988 Spe = 0.959 |
| Jabeen et al. [ | Breast lesions | CNN DarkNet53 | BUSI dataset Total = 780 Normal = 133 Benign = 437 Malignant = 210 | Acc = 0.991 |
| Ragab et al. [ | Breast lesions | VGG-16 VGG-19 SqueezeNet | BUSI dataset Total = 780 Normal = 133 Benign = 437 Malignant = 210 | Acc = 0.9709 |
| Gheflati et al. [ | Breast lesions | ViT | BUSI dataset Total = 780 Normal = 133 Benign = 437 Malignant = 210 | Acc = 0.867 AUC = 0.95 |
| Ayana et al. [ | Breast lesions | MSTL | Mendeley dataset Total = 250 Benign = 100 Malignant = 150 | Acc = 0.999 Sen = 1 Spe = 0.98 AUC = 0.999 F1 = 0.989 |
| Liu et al. [ | Thyroid nodules | Multi-branch classification network | Private dataset1 Benign = 2551 Malignant = 5139 Private dataset2 Benign = 128 Malignant = 322 | Acc = 0.971 Sen = 0.982 Spe = 0.951 |
| Kuo et al. [ | Kidney | ResNet | Private dataset Total = 4505 | Acc = 0.856 |
| Roy et al. [ | Lung | CNN | ICLUS-DB video Total = 277 | F1 = 0.61 ± 0.12 Pre = 0.70 ± 0.19 Rec = 0.60 ± 0.07 |
| Xie et al. [ | Brain | CNN | Private dataset Standard = 15372 Abnormal = 14047 | Acc = 0.963 Sen = 0.969 Spe = 0.959 |
| Sanagala et al. [ | Carotid | DCNN Transfer learning | Private dataset Total = Unknown | AUC = 0.8333, 0.9566 |
Table 2
Summary the region of interest, method, dataset, and performance of the major researches on AI in medical ultrasound segmentation in recent years"
| Paper | Region | Method | Dataset | Performance |
|---|---|---|---|---|
| Chen et al. [ | Anatomical structures | Iterative multi-domain regularized deep learning | Private dataset Train = Unknown Test = Unknown | DSC = 0.927 |
| Cui et al. [ | Ultrasound | SegNet | Private dataset Train = Unknown Test = Unknown | Unknown |
| Dangoury et al. [ | Ultrasound | V-net | Private dataset Train = 5635 Test = 5508 | DSC = 0.8501 Sen = 0.8556 Spe = 0.9987 Acc = 0.9992 |
| Yap et al. [ | Breast | CNN | Inbreast Total = 410 | AUC = 0.94 |
| Sharifzadeh et al. [ | Breast | Shift-Invariant Segmentation | US breast images dataset Train = 100 Validation = 30 Test = 33 | DSC = 0.94 JI = 0.91 |
| Gare et al. [ | Subcutaneous, Breast | W-Net | Private dataset Train = 450 Test = 50 | DSC = 0.883 |
| Yin et al. [ | Kidney | Pixelwise Classification Networks | Private dataset Total = 918 | DSC = 0.959 |
| Torres et al. [ | Kidney | BEAS framework | Private dataset Total = 45 | DSC = 0.93 |
| Chen et al. [ | Kidney | SDFNet | Private dataset Train = 450 Test = 50 | DSC = 0.941 |
| Valente et al. [ | Kidney | Deep learning method | Private dataset Training = 2166 Validation = 193 Testing = 358 | DSC = 0.94 |
| Leclerc et al. [ | heart | Deep learning | CAMUS Total = 2000 | DSC = 0.92 |
| Pu et al. [ | Fetal heart | MobileUNet-FPN | Private dataset Train = 575 Validation = 102 Test = 207 | DSC = 0.935 |
| Ma et al. [ | Thyroid | CNN | Private dataset Total = 352 | DSC = 0.901 |
| Li et al. [ | Ovary Follicle | Cr-UNet | Private dataset Train = 2509 Test = 695 | DSC = 0.9601 |
| Qiu et al. [ | Mouse Embryo | Deep Learning | Private dataset Train = Unknown Test = Unknown | ACC = 0.98 |
Table 3
Summary the region of interest, method, dataset, and performance of the major researches on AI in medical ultrasound detection in recent years"
| Paper | Region | Method | Dataset | Performance |
|---|---|---|---|---|
| Kim et al. [ | Breast cancer | Weakly-supervised deep learning method | Private dataset Train = 1000 Test = 400 | AUC = 0.86-0.96 |
| Shen et al. [ | Breast cancer | Deep learning model | NYU Breast Ultrasound Dataset Train = 3265744 Test = 1632872 Validation = 544291 | AUC = 0.976 Sen = 0.918 PPV = 0.38 |
| Niu et al. [ | Breast lesions | Grey level gradient cooccurrence Matrix analysis | Private dataset Total = 206 | BI-RADS 4A: Acc > 0.9 |
| Zhang et al. [ | Breast lesions | Lightweight neural network | Private dataset Train = 5030 Test = Unknown Validation = 1830 | Sen = 0.8925 Spe = 0.9633 Average Pre = 0.85 |
| Qian et al. [ | Breast cancer | Deep-learning system | Private dataset Train = 10815 Test = 912 Validation = Unknown | Bimodal US images: AUC= 0.880 MultimodalUS images: AUC = 0.920 |
| Baloescu et al. [ | Lung lesions | Deep learning automated algorithm | Private dataset Train = 1847 Test = 100 Validation = 468 | Sen = 0.93 Spe = 0. 96 |
| Diaz-Escobar et al. [ | Lung lesions, COVID-19 | Deep learning architectures | POCUS dataset Train = 2661 Test = Unknown Validation = 665 | Average Acc = 0.891 Balanced Acc = 0.893 COVID-19 detection AUC = 0.971 |
| Fang et al. [ | Lung lesions | CNN architectures based on transfer learning | Private dataset Train = 916 Test = Unknown Validation = Unknown | Clinical diagnosis, CXR, Chest CT: Kappa values = 0.943, 0.837, 0.835 |
| Kulhare et al. [ | Lung lesions | Single Shot CNN Model | Private dataset Train = 18713 Test = 444 Validation = Unknown | Acc = 0.89 |
| Abdel-Basset et al. [ | Lung lesions, COVID-19 | CNN | POCUS dataset Total = 3234 | Acc = 0. 934 F1 = 0.931 AUC = 0.97 |
| Choi et al. [ | Thyroid nodule | CAD system using AI | Private dataset Train = Unknown Test = Unknown Validation = Unknown | Sen = 0.907 |
| Wei et al. [ | Thyroid nodule | CNN | Private dataset Train = 5000 Test = 2214 | Acc = 0.92 |
| Wang et al. [ | Thyroid nodule | YOLOv2 neural network | Private dataset Train = 5007 Test = Unknown Validation = 351 | ROC = 0.902 Sen = 0.905 PPV = 0.9522 NPV = 0.8099 Acc = 0.9031 Spe = 0.8991 |
| Jassal et al. [ | Thyroid nodule | AI model | Private dataset Train = 857 Test = 198 Validation = Unknown | Acc = 0.89 Sen = 0.89 Spe = 0.83 F1 = 0.94 AUC = 0.86 |
| Deng et al. [ | Thyroid nodule | ResNet50 Random forest | Private dataset Train = 366 Test = 122 Validation = 122 | Sen = 0.8587 Spe = 0.9718 Acc = 0.9377 AUC = 0.982 |
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