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Advanced Ultrasound in Diagnosis and Therapy ›› 2023, Vol. 7 ›› Issue (2): 204-216.

• • 上一篇    

  

  • 出版日期:2023-06-30 发布日期:2023-04-27

The Impact of Deep Learning on Ultrasound in Diagnosis and Therapy: Enhancing Clinical Decision Support, Workflow Efficiency, Quantification, Image Registration, and Real-time Assistance

Won-Chul Bang, PhD, Vice Presidenta, Yeong Kyeong Seong, PhDb, Jinyong Leea   

  1. a Samsung Medison Co., Ltd
    b Samsung Electronics
  • Online:2023-06-30 Published:2023-04-27

Abstract:

This review article introduces the main concepts and architectures of deep learning networks for medical imaging tasks, such as classification, detection, segmentation, and generation. It then surveys how deep learning has been applied to ultrasound imaging for various purposes, such as image processing, diagnosis, and workflow enhancement. It covers different organs and body parts that can be imaged by ultrasound, such as liver, breast, thyroid, heart, kidney, prostate, nerve, muscle, and fetus. It also discusses how deep learning can help with view recognition, registration, and quantification, measurement, image registration for interventional guidance, and real-time assistance while scanning. Moreover, it explores how generative AI can be used in the future medical field by leveraging deep learning for ultrasound imaging, such as generating realistic and diverse images, virtual organs/patients with diseases, synthesizing missing or corrupted data and augmenting existing data for training and testing.

Key words: Deep learning, Convolutional neural network, Artificial intelligence, Real-time AI, Generative AI, ChatGPT, Computer-aided diagnosis, Workflow efficiency, Quantification, Image registration

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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) [50] 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) [51] 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) [48] 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) [49] 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%

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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) [52] 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) [53] 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) [54] 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%

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