The Impact of Deep Learning on Ultrasound in Diagnosis and Therapy: Enhancing Clinical Decision Support, Workflow Efficiency, Quantification, Image Registration, and Real-time Assistance
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 proces...
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| Format: | Article |
| Language: | English |
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Editorial Office of Advanced Ultrasound in Diagnosis and Therapy
2023-06-01
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| Series: | Advanced Ultrasound in Diagnosis and Therapy |
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| Online Access: | http://www.journaladvancedultrasound.com:81/fileup/2576-2516/PDF/1682659494268-1751476978.pdf |
| _version_ | 1797837315885760512 |
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| author | Won-Chul Bang, PhD, Vice President, Yeong Kyeong Seong, PhD, Jinyong Lee |
| author_facet | Won-Chul Bang, PhD, Vice President, Yeong Kyeong Seong, PhD, Jinyong Lee |
| author_sort | Won-Chul Bang, PhD, Vice President, Yeong Kyeong Seong, PhD, Jinyong Lee |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-09T15:22:45Z |
| format | Article |
| id | doaj.art-966e21a720b943b686e948755c12c245 |
| institution | Directory Open Access Journal |
| issn | 2576-2516 |
| language | English |
| last_indexed | 2024-04-09T15:22:45Z |
| publishDate | 2023-06-01 |
| publisher | Editorial Office of Advanced Ultrasound in Diagnosis and Therapy |
| record_format | Article |
| series | Advanced Ultrasound in Diagnosis and Therapy |
| spelling | doaj.art-966e21a720b943b686e948755c12c2452023-04-29T04:16:27ZengEditorial Office of Advanced Ultrasound in Diagnosis and TherapyAdvanced Ultrasound in Diagnosis and Therapy2576-25162023-06-0172204216The Impact of Deep Learning on Ultrasound in Diagnosis and Therapy: Enhancing Clinical Decision Support, Workflow Efficiency, Quantification, Image Registration, and Real-time AssistanceWon-Chul Bang, PhD, Vice President, Yeong Kyeong Seong, PhD, Jinyong Lee0a Samsung Medison Co., Ltd;b Samsung ElectronicsThis 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.http://www.journaladvancedultrasound.com:81/fileup/2576-2516/PDF/1682659494268-1751476978.pdf|deep learning|convolutional neural network|artificial intelligence|real-time ai, generative ai, chatgpt|computer-aided diagnosis|workflow efficiency|quantification|image registration |
| spellingShingle | 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 |deep learning|convolutional neural network|artificial intelligence|real-time ai, generative ai, chatgpt|computer-aided diagnosis|workflow efficiency|quantification|image registration |
| title | The Impact of Deep Learning on Ultrasound in Diagnosis and Therapy: Enhancing Clinical Decision Support, Workflow Efficiency, Quantification, Image Registration, and Real-time Assistance |
| title_full | The Impact of Deep Learning on Ultrasound in Diagnosis and Therapy: Enhancing Clinical Decision Support, Workflow Efficiency, Quantification, Image Registration, and Real-time Assistance |
| title_fullStr | The Impact of Deep Learning on Ultrasound in Diagnosis and Therapy: Enhancing Clinical Decision Support, Workflow Efficiency, Quantification, Image Registration, and Real-time Assistance |
| title_full_unstemmed | The Impact of Deep Learning on Ultrasound in Diagnosis and Therapy: Enhancing Clinical Decision Support, Workflow Efficiency, Quantification, Image Registration, and Real-time Assistance |
| title_short | The Impact of Deep Learning on Ultrasound in Diagnosis and Therapy: Enhancing Clinical Decision Support, Workflow Efficiency, Quantification, Image Registration, and Real-time Assistance |
| title_sort | impact of deep learning on ultrasound in diagnosis and therapy enhancing clinical decision support workflow efficiency quantification image registration and real time assistance |
| topic | |deep learning|convolutional neural network|artificial intelligence|real-time ai, generative ai, chatgpt|computer-aided diagnosis|workflow efficiency|quantification|image registration |
| url | http://www.journaladvancedultrasound.com:81/fileup/2576-2516/PDF/1682659494268-1751476978.pdf |
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