Advanced Ultrasound in Diagnosis and Therapy ›› 2023, Vol. 7 ›› Issue (2): 204-216.
• Technical Papers • Previous Articles
Won-Chul Bang, PhD, Vice Presidenta, Yeong Kyeong Seong, PhDb, Jinyong Leea
Online:2023-06-30
Published:2023-04-27
Won-Chul Bang, PhD, Vice President, Yeong Kyeong Seong, PhD, Jinyong Lee. The Impact of Deep Learning on Ultrasound in Diagnosis and Therapy: Enhancing Clinical Decision Support, Workflow Efficiency, Quantification, Image Registration, and Real-time Assistance. Advanced Ultrasound in Diagnosis and Therapy, 2023, 7(2): 204-216.
Figure 1
The number of published articles with "deep learning" in PubMed (A) and both "deep learning" and "ultrasound" (B)."
Figure 2
Basic components of a convolutional neural network. It consists of convolutional layers performing filtering inputs (A) and pooling layers for down-sampling images (B). Through the training process, the optimal parameters of convolutional weights can be determined."
Figure 3
These examples demonstrate the application of classification, detection, segmentation, and generation techniques to ultrasound images. They include the classification of breast lesion malignancy (A, S-DetectTM), detection and segmentation of the ulnar nerve (B, NerveTrackTM), circumference segmentation of the fetal abdomen (C, BiometryAssistTM on Hera W10), and generated ultrasound images resembling breast lesions (D)."
Figure 4
The Transformer model architecture showing “attention” mechanism, dispensing with recurrence and convolutions."
Figure 5
Diagram depicting the diagnostic workflow in ultrasonography involving deep learning is presented, including the building blocks of computer-aided detection (CADe), computer-aided quantification (CADq), computer-aided diagnosis (CADx), computer-aided triage (CADt), scan/procedure guidance, and real-time detection."
Table 1
Deep learning research on breast diagnosis"
| Study | Total number of images (patients)/Total number of images for evaluation | Methods | Performance of previous methods | Performance of proposed methods |
|---|---|---|---|---|
| Zheng et al. (2020) [ | 584 (584)/118 (118) | ResNet50 image only vs. ResNet50+Clinical information | AUC: 0.796 ACC: 71.6% SENS: 67.4% SPEC: 79.1% PPV: 70.2% NPV: 76.8% | AUC: 0.902 ACC: 81.0% SENS: 81.6% SPEC: 83.6% PPV: 78.4% NPV: 86.2% |
| Sun et al. (2020) [ | 2395 (479)/680 (136) | DeseNet Image only vs. DenseNet image + molecular subtype | AUC: 0.912 ACC: 89.3% SENS: 85.7% SPEC: 90.7% PPV: 77.4% NPV 94.4% | AUC: 0.933 ACC: 90.3% SENS: 89.3% SPEC: 90.7% PPV: 78.1% NPV 95.8% |
| Liao et al. (2019) [ | 256 (141)/51 | VGG19 B-mode only vs. VGG19 B-mode + Strain Elastography images | AUC: 0.93 ACC: 85.26% SENS: 85.31% SPEC: 86.09% | AUC: 0.98 ACC: 92.95% SENS: 91.39% SPEC: 94.71% |
| Tanaka et al. (2019) [ | 8472 (1469)/850 (150) | VGG19 single image vs. Ensemble network of VGG19 and ResNet152 for multiple images | AUC: 0.926 ACC: 86.4% SENS: 90.0% SPEC: 82.3% | AUC: 0.951 ACC: 89.0% SENS: 90.9% SPEC: 87.0% |
Table 2
Deep learning research on thyroid diagnosis"
| Study | Total number of images (patients)/Total number of images for evaluation | Methods | Performance of previous methods | Performance of proposed methods |
|---|---|---|---|---|
| Nguyen et al. (2020) [ | 450 (298)/5-fold validation | Single ResNet50 vs. two fused CNN models | ACC: 87.778% SENS: 91.356% SPEC: 64.018% | ACC: 92.051% SENS: 96.072% SPEC: 65.687% |
| Park et al. (2019) [ | 4919 / 286 | SVM based CAD vs. GoogLeNet image + seven ultrasound features | ACC: 75.9% SENS: 90.4% SPEC: 58.5% PPV: 72.3% NPV: 83.5% | ACC: 86% SENS: 91.0% SPEC: 80.0% PPV: 84.5% NPV: 88.1% |
| Zhu et al. (2019) [ | 467 / 70 | Logistic regression vs. DNN for classifying Bethesda class III and class IV/V/VI | AUC: 0.904 ACC: 86.94% SENS: 89.38% SPEC: 80.47% | AUC: 0.891 ACC: 87.15% SENS: 87.91% SPEC: 85.15% |
Figure 6
The image on the left represents ground truth, the target of the NerveTrackTM machine learning model. The image at right is annotated with the Intel Geti platform for inference model training proof of concept, based on annotated ultrasound images."
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