Application of Artificial Intelligence to Ultrasonography
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Published
Jun 30, 2022
Abstract
The use of artificial intelligence (AI) technology in medicine has gained considerable attention, although its application in ultrasound medicine is still in its infancy. Deep learning, the main algorithm of AI technology, can be applied to intelligent ultrasound picture detection and classification. Describe the application status of AI in ultrasound imaging, including thyroid, breast, and liver disease applications. The merging of AI and ultrasound imaging can increase the accuracy and specificity of ultrasound diagnosis and decrease the percentage of incorrect diagnoses.
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Keywords
Ultrasonography, Artificial Intelligence, Convolutional Neural Network, Diagnostics
References
1. McCarthy J, Minsky ML, Rochester N, Shannon CE. A proposal for the Dartmouth summer research project on artificial intelligence. 1956. Artificial Intelligence (AI) Coined at Dartmouth. Last access: June 20, 2022. Available at: https://250.dartmouth.edu/highlights/artificial-intelligence-ai-coined-dartmouth
2. Coiera E. The last mile: Where artificial intelligence meets reality. J Med Internet Res 2019; 21:e16323. DOI: https://doi.org/10.2196/16323
3. Anwar SM, Majid M, Qayyum A, Awais M, Alnowami M, Khan MK. Medical image analysis using convolutional neural networks: A review. J Med Syst 2018; 42(11):226. DOI: https://doi.org/10.1007/s10916-018-1088-1
4. Zuo C, Qian J, Feng S, Yin W, Li Y, Fan P, Han J, Qian K, Chen Q. Deep learning in optical metrology: A review. Light Sci Appl 2022; 11(1):39. DOI: https://doi.org/10.1038/s41377-022-00714-x. Erratum in: Light Sci Appl 2022; 11(1):74.
5. Sippel S, Muruganandan K, Levine A, Shah S. Review article: Use of ultrasound in the developing world. Int J Emerg Med 2011; 4:72. DOI: https://doi.org/10.1186/1865-1380-4-72
6. Lucas VS, Burk RS, Creehan S, Grap MJ. Utility of high-frequency ultrasound: Moving beyond the surface to detect changes in skin integrity. Plast Surg Nurs 2014; 34(1):34-38. DOI: https://doi.org/10.1097/PSN.0000000000000031
7. Acharya UR, Faust O, Sree SV, Molinari F, Garberoglio R, Suri JS. Cost-effective and non-invasive automated benign and malignant thyroid lesion classification in 3D contrast-enhanced ultrasound using combination of wavelets and textures: A class of ThyroScan™ algorithms. Technol Cancer Res Treat 2011; 10(4):371-380. DOI: https://doi.org/10.7785/tcrt.2012.500214
8. Nguyen DT, Pham TD, Batchuluun G, Yoon HS, Park KR. artificial intelligence-based thyroid nodule classification using information from spatial and frequency domains. J Clin Med 2019; 8(11):1976. DOI: https://doi.org/10.3390/jcm8111976
9. Wang S, Xu J, Tahmasebi A, Daniels K, Liu JB, Curry J, Cottrill E, Lyshchik A, Eisenbrey JR. Incorporation of a machine learning algorithm with object detection within the thyroid imaging reporting and data system improves the diagnosis of genetic risk. Front Oncol 2020; 10:591846. DOI: https://doi.org/10.3389/fonc.2020.591846
10. Chi J, Walia E, Babyn P, Wang J, Groot G, Eramian M. Thyroid nodule classification in ultrasound images by fine-tuning deep convolutional neural network. J Digit Imaging 2017; 30(4):477-486. DOI: https://doi.org/10.1007/s10278-017-9997-y
11. Ma J, Wu F, Zhu J, Xu D, Kong D. A pre-trained convolutional neural network-based method for thyroid nodule diagnosis. Ultrasonics 2017; 73:221-230. DOI: https://doi.org/10.1016/j.ultras.2016.09.011
12. Ma J, Wu F, Jiang T, Zhu J, Kong D. Cascade convolutional neural networks for automatic detection of thyroid nodules in ultrasound images. Med Phys 2017; 44(5):1678-1691. DOI: https://doi.org/10.1002/mp.12134
13. Zhang Y, Wu Q, Chen Y, Wang Y. A clinical assessment of an ultrasound computer-aided diagnosis system in differentiating thyroid nodules with radiologists of different diagnostic experience. Front Oncol 2020; 10:557169. DOI: https://doi.org/10.3389/fonc.2020.557169
14. Li X, Zhang S, Zhang Q, Wei X, Pan Y, Zhao J, Xin X, Qin C, Wang X, Li J, Yang F, Zhao Y, Yang M, Wang Q, Zheng Z, Zheng X, Yang X, Whitlow CT, Gurcan MN, Zhang L, Wang X, Pasche BC, Gao M, Zhang W, Chen K. Diagnosis of thyroid cancer using deep convolutional neural network models applied to sonographic images: A retrospective, multicohort, diagnostic study. Lancet Oncol 2019; 20(2):193-201. DOI: https://doi.org/10.1016/S1470-2045(18)30762-9. Erratum in: Lancet Oncol 2020; 21(10):e462.
15. Wu GG, Zhou LQ, Xu JW, Wang JY, Wei Q, Deng YB, Cui XW, Dietrich CF. Artificial intelligence in breast ultrasound. World J Radiol 2019; 11(2):19-26. DOI: https://doi.org/10.4329/wjr.v11.i2.19
16. Kalafi EY, Jodeiri A, Setarehdan SK, Lin NW, Rahmat K, Taib NA, Ganggayah MD, Dhillon SK. Classification of breast cancer lesions in ultrasound images by using attention layer and loss ensemble in deep convolutional neural networks. Diagnostics (Basel) 2021; 11(10):1859. DOI: https://doi.org/10.3390/diagnostics11101859
17. Lei Y, He X, Yao J, Wang T, Wang L, Li W, Curran WJ, Liu T, Xu D, Yang X. Breast tumor segmentation in 3D automatic breast ultrasound using Mask scoring R-CNN. Med Phys 2021; 48(1):204-214. DOI: https://doi.org/10.1002/mp.14569
18. Park JY. Evaluation of breast cancer size measurement by computer-aided diagnosis (CAD) and a radiologist on breast MRI. J Clin Med 2022; 11(5):1172. DOI: https://doi.org/10.3390/jcm11051172
19. Liu H, Cui G, Luo Y, Guo Y, Zhao L, Wang Y, Subasi A, Dogan S, Tuncer T. Artificial intelligence-based breast cancer diagnosis using ultrasound images and grid-based deep feature generator. Int J Gen Med 2022; 15:2271-2282. DOI: https://doi.org/10.2147/IJGM.S347491
20. Kim K, Song MK, Kim EK, Yoon JH. Clinical application of S-Detect to breast masses on ultrasonography: A study evaluating the diagnostic performance and agreement with a dedicated breast radiologist. Ultrasonography 2017; 36(1):3-9. DOI: https://doi.org/10.14366/usg.16012
21. Alzubaidi L, Zhang J, Humaidi AJ, Al-Dujaili A, Duan Y, Al-Shamma O, Santamaría J, Fadhel MA, Al-Amidie M, Farhan L. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J Big Data 2021; 8(1):53. DOI: https://doi.org/10.1186/s40537-021-00444-8
22. Ghosh S, Biswas B, Ghosh A. A novel stacked sparse denoising autoencoder for mammography restoration to visual interpretation of breast lesion. Evol Intell 2021; 14(1):133-149. DOI: https://doi.org/10.1007/s12065-019-00344-0
23. Yap MH, Pons G, Marti J, Ganau S, Sentis M, Zwiggelaar R, Davison AK, Marti R, Moi Hoon Yap, Pons G, Marti J, Ganau S, Sentis M, Zwiggelaar R, Davison AK, Marti R. Automated breast ultrasound lesions detection using convolutional neural networks. IEEE J Biomed Health Inform 2018; 22(4):1218-1226. DOI: https://doi.org/10.1109/JBHI.2017.2731873
24. Xiao T, Liu L, Li K, Qin W, Yu S, Li Z. Comparison of transferred deep neural networks in ultrasonic breast masses discrimination. Biomed Res Int 2018; 2018:4605191. DOI: https://doi.org/10.1155/2018/4605191
25. Di Segni M, de Soccio V, Cantisani V, Bonito G, Rubini A, Di Segni G, Lamorte S, Magri V, De Vito C, Migliara G, Bartolotta TV, Metere A, Giacomelli L, de Felice C, D'Ambrosio F. Automated classification of focal breast lesions according to S-detect: validation and role as a clinical and teaching tool. J Ultrasound 2018; 21(2):105-118. DOI: https://doi.org/10.1007/s40477-018-0297-2
26. Zahoor S, Shoaib U, Lali IU. Breast cancer mammograms classification using deep neural network and entropy-controlled whale optimization algorithm. Diagnostics (Basel) 2022; 12(2):557. DOI: https://doi.org/10.3390/diagnostics12020557
27. Byra M, Styczynski G, Szmigielski C, Kalinowski P, Michałowski Ł, Paluszkiewicz R, Ziarkiewicz-Wróblewska B, Zieniewicz K, Sobieraj P, Nowicki A. Transfer learning with deep convolutional neural network for liver steatosis assessment in ultrasound images. Int J Comput Assist Radiol Surg 2018; 13(12):1895-1903. DOI: https://doi.org/10.1007/s11548-018-1843-2
28. Biswas M, Kuppili V, Edla DR, Suri HS, Saba L, Marinhoe RT, Sanches JM, Suri JS. Symtosis: A liver ultrasound tissue characterization and risk stratification in optimized deep learning paradigm. Comput Methods Programs Biomed 2018; 155:165-177. DOI: https://doi.org/10.1016/j.cmpb.2017.12.016
29. Ahmad M, Yang J, Ai D, Qadri SF, Wang Y. Deep-Stacked Auto Encoder for Liver Segmentation. In: et al. Advances in Image and Graphics Technologies. IGTA 2017. Communications in Computer and Information Science, 2018; vol 757. ISBN 978-981-10-7388-5. Springer, Singapore. DOI: https://doi.org/10.1007/978-981-10-7389-2_24
30. Meng D, Zhang L, Cao G, Cao W, Zhang G, Hu B. Liver Fibrosis Classification Based on Transfer Learning and FCNet for Ultrasound Images. IEEE Access 2017; 5:5804-5810. DOI: https://doi.org/10.1109/ACCESS.2017.2689058
31. Liu X, Song JL, Wang SH, Zhao JW, Chen YQ. Learning to diagnose cirrhosis with liver capsule guided ultrasound image classification. Sensors (Basel) 2017; 17(1):149. DOI: https://doi.org/10.3390/s17010149
32. Yeom SK, Lee CH, Cha SH, Park CM. Prediction of liver cirrhosis, using diagnostic imaging tools. World J Hepatol 2015; 7(17):2069-2079. DOI: https://doi.org/10.4254/wjh.v7.i17.2069
33. Oezdemir I, Wessner CE, Shaw C, Eisenbrey JR, Hoyt K. Tumor vascular networks depicted in contrast-enhanced ultrasound images as a predictor for transarterial chemoembolization treatment response. Ultrasound Med Biol 2020; 46(9):2276-2286. DOI: https://doi.org/10.1016/j.ultrasmedbio.2020.05.010
34. Wilkinson TJ, Ashman J, Baker LA, Watson EL, Smith AC. Quantitative muscle ultrasonography using 2d textural analysis: A novel approach to assess skeletal muscle structure and quality in chronic kidney disease. Ultrason Imaging 2021; 43(3):139-148. DOI: https://doi.org/10.1177/01617346211009788
35. Yu Z, Tan EL, Ni D, Qin J, Chen S, Li S, Lei B, Wang T. A deep convolutional neural network-based framework for automatic fetal facial standard plane recognition. IEEE J Biomed Health Inform 2018; 22(3):874-885. DOI: https://doi.org/10.1109/JBHI.2017.2705031
36. Wu L, Cheng JZ, Li S, Lei B, Wang T, Ni D. FUIQA: Fetal ultrasound image quality assessment with deep convolutional networks. IEEE Trans Cybern 2017; 47(5):1336-1349. DOI: https://doi.org/10.1109/TCYB.2017.2671898
37. Chen H, Wu L, Dou Q, Qin J, Li S, Cheng JZ, Ni D, Heng PA. Ultrasound standard plane detection using a composite neural network framework. IEEE Trans Cybern 2017; 47(6):1576-1586. DOI: https://doi.org/10.1109/TCYB.2017.2685080
38. Hetherington J, Lessoway V, Gunka V, Abolmaesumi P, Rohling R. SLIDE: Automatic spine level identification system using a deep convolutional neural network. Int J Comput Assist Radiol Surg 2017; 12(7):1189-1198. DOI: https://doi.org/10.1007/s11548-017-1575-8
39. Rodziewicz TL, Houseman B, Hipskind JE. Medical Error Reduction and Prevention. [Updated 2022 May 1]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2022. Available at: https://www.ncbi.nlm.nih.gov/books/NBK499956/
40. Drukker L, Noble JA, Papageorghiou AT. Introduction to artificial intelligence in ultrasound imaging in obstetrics and gynecology. Ultrasound Obstet Gynecol 2020; 56(4):498-505. DOI: https://doi.org/10.1002/uog.22122
41. Narang A, Bae R, Hong H, Thomas Y, Surette S, Cadieu C, Chaudhry A, Martin RP, McCarthy PM, Rubenson DS, Goldstein S, Little SH, Lang RM, Weissman NJ, Thomas JD. Utility of a deep-learning algorithm to guide novices to acquire echocardiograms for limited diagnostic use. JAMA Cardiol 2021; 6(6):624-632. DOI: https://doi.org/10.1001/jamacardio.2021.0185
2. Coiera E. The last mile: Where artificial intelligence meets reality. J Med Internet Res 2019; 21:e16323. DOI: https://doi.org/10.2196/16323
3. Anwar SM, Majid M, Qayyum A, Awais M, Alnowami M, Khan MK. Medical image analysis using convolutional neural networks: A review. J Med Syst 2018; 42(11):226. DOI: https://doi.org/10.1007/s10916-018-1088-1
4. Zuo C, Qian J, Feng S, Yin W, Li Y, Fan P, Han J, Qian K, Chen Q. Deep learning in optical metrology: A review. Light Sci Appl 2022; 11(1):39. DOI: https://doi.org/10.1038/s41377-022-00714-x. Erratum in: Light Sci Appl 2022; 11(1):74.
5. Sippel S, Muruganandan K, Levine A, Shah S. Review article: Use of ultrasound in the developing world. Int J Emerg Med 2011; 4:72. DOI: https://doi.org/10.1186/1865-1380-4-72
6. Lucas VS, Burk RS, Creehan S, Grap MJ. Utility of high-frequency ultrasound: Moving beyond the surface to detect changes in skin integrity. Plast Surg Nurs 2014; 34(1):34-38. DOI: https://doi.org/10.1097/PSN.0000000000000031
7. Acharya UR, Faust O, Sree SV, Molinari F, Garberoglio R, Suri JS. Cost-effective and non-invasive automated benign and malignant thyroid lesion classification in 3D contrast-enhanced ultrasound using combination of wavelets and textures: A class of ThyroScan™ algorithms. Technol Cancer Res Treat 2011; 10(4):371-380. DOI: https://doi.org/10.7785/tcrt.2012.500214
8. Nguyen DT, Pham TD, Batchuluun G, Yoon HS, Park KR. artificial intelligence-based thyroid nodule classification using information from spatial and frequency domains. J Clin Med 2019; 8(11):1976. DOI: https://doi.org/10.3390/jcm8111976
9. Wang S, Xu J, Tahmasebi A, Daniels K, Liu JB, Curry J, Cottrill E, Lyshchik A, Eisenbrey JR. Incorporation of a machine learning algorithm with object detection within the thyroid imaging reporting and data system improves the diagnosis of genetic risk. Front Oncol 2020; 10:591846. DOI: https://doi.org/10.3389/fonc.2020.591846
10. Chi J, Walia E, Babyn P, Wang J, Groot G, Eramian M. Thyroid nodule classification in ultrasound images by fine-tuning deep convolutional neural network. J Digit Imaging 2017; 30(4):477-486. DOI: https://doi.org/10.1007/s10278-017-9997-y
11. Ma J, Wu F, Zhu J, Xu D, Kong D. A pre-trained convolutional neural network-based method for thyroid nodule diagnosis. Ultrasonics 2017; 73:221-230. DOI: https://doi.org/10.1016/j.ultras.2016.09.011
12. Ma J, Wu F, Jiang T, Zhu J, Kong D. Cascade convolutional neural networks for automatic detection of thyroid nodules in ultrasound images. Med Phys 2017; 44(5):1678-1691. DOI: https://doi.org/10.1002/mp.12134
13. Zhang Y, Wu Q, Chen Y, Wang Y. A clinical assessment of an ultrasound computer-aided diagnosis system in differentiating thyroid nodules with radiologists of different diagnostic experience. Front Oncol 2020; 10:557169. DOI: https://doi.org/10.3389/fonc.2020.557169
14. Li X, Zhang S, Zhang Q, Wei X, Pan Y, Zhao J, Xin X, Qin C, Wang X, Li J, Yang F, Zhao Y, Yang M, Wang Q, Zheng Z, Zheng X, Yang X, Whitlow CT, Gurcan MN, Zhang L, Wang X, Pasche BC, Gao M, Zhang W, Chen K. Diagnosis of thyroid cancer using deep convolutional neural network models applied to sonographic images: A retrospective, multicohort, diagnostic study. Lancet Oncol 2019; 20(2):193-201. DOI: https://doi.org/10.1016/S1470-2045(18)30762-9. Erratum in: Lancet Oncol 2020; 21(10):e462.
15. Wu GG, Zhou LQ, Xu JW, Wang JY, Wei Q, Deng YB, Cui XW, Dietrich CF. Artificial intelligence in breast ultrasound. World J Radiol 2019; 11(2):19-26. DOI: https://doi.org/10.4329/wjr.v11.i2.19
16. Kalafi EY, Jodeiri A, Setarehdan SK, Lin NW, Rahmat K, Taib NA, Ganggayah MD, Dhillon SK. Classification of breast cancer lesions in ultrasound images by using attention layer and loss ensemble in deep convolutional neural networks. Diagnostics (Basel) 2021; 11(10):1859. DOI: https://doi.org/10.3390/diagnostics11101859
17. Lei Y, He X, Yao J, Wang T, Wang L, Li W, Curran WJ, Liu T, Xu D, Yang X. Breast tumor segmentation in 3D automatic breast ultrasound using Mask scoring R-CNN. Med Phys 2021; 48(1):204-214. DOI: https://doi.org/10.1002/mp.14569
18. Park JY. Evaluation of breast cancer size measurement by computer-aided diagnosis (CAD) and a radiologist on breast MRI. J Clin Med 2022; 11(5):1172. DOI: https://doi.org/10.3390/jcm11051172
19. Liu H, Cui G, Luo Y, Guo Y, Zhao L, Wang Y, Subasi A, Dogan S, Tuncer T. Artificial intelligence-based breast cancer diagnosis using ultrasound images and grid-based deep feature generator. Int J Gen Med 2022; 15:2271-2282. DOI: https://doi.org/10.2147/IJGM.S347491
20. Kim K, Song MK, Kim EK, Yoon JH. Clinical application of S-Detect to breast masses on ultrasonography: A study evaluating the diagnostic performance and agreement with a dedicated breast radiologist. Ultrasonography 2017; 36(1):3-9. DOI: https://doi.org/10.14366/usg.16012
21. Alzubaidi L, Zhang J, Humaidi AJ, Al-Dujaili A, Duan Y, Al-Shamma O, Santamaría J, Fadhel MA, Al-Amidie M, Farhan L. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J Big Data 2021; 8(1):53. DOI: https://doi.org/10.1186/s40537-021-00444-8
22. Ghosh S, Biswas B, Ghosh A. A novel stacked sparse denoising autoencoder for mammography restoration to visual interpretation of breast lesion. Evol Intell 2021; 14(1):133-149. DOI: https://doi.org/10.1007/s12065-019-00344-0
23. Yap MH, Pons G, Marti J, Ganau S, Sentis M, Zwiggelaar R, Davison AK, Marti R, Moi Hoon Yap, Pons G, Marti J, Ganau S, Sentis M, Zwiggelaar R, Davison AK, Marti R. Automated breast ultrasound lesions detection using convolutional neural networks. IEEE J Biomed Health Inform 2018; 22(4):1218-1226. DOI: https://doi.org/10.1109/JBHI.2017.2731873
24. Xiao T, Liu L, Li K, Qin W, Yu S, Li Z. Comparison of transferred deep neural networks in ultrasonic breast masses discrimination. Biomed Res Int 2018; 2018:4605191. DOI: https://doi.org/10.1155/2018/4605191
25. Di Segni M, de Soccio V, Cantisani V, Bonito G, Rubini A, Di Segni G, Lamorte S, Magri V, De Vito C, Migliara G, Bartolotta TV, Metere A, Giacomelli L, de Felice C, D'Ambrosio F. Automated classification of focal breast lesions according to S-detect: validation and role as a clinical and teaching tool. J Ultrasound 2018; 21(2):105-118. DOI: https://doi.org/10.1007/s40477-018-0297-2
26. Zahoor S, Shoaib U, Lali IU. Breast cancer mammograms classification using deep neural network and entropy-controlled whale optimization algorithm. Diagnostics (Basel) 2022; 12(2):557. DOI: https://doi.org/10.3390/diagnostics12020557
27. Byra M, Styczynski G, Szmigielski C, Kalinowski P, Michałowski Ł, Paluszkiewicz R, Ziarkiewicz-Wróblewska B, Zieniewicz K, Sobieraj P, Nowicki A. Transfer learning with deep convolutional neural network for liver steatosis assessment in ultrasound images. Int J Comput Assist Radiol Surg 2018; 13(12):1895-1903. DOI: https://doi.org/10.1007/s11548-018-1843-2
28. Biswas M, Kuppili V, Edla DR, Suri HS, Saba L, Marinhoe RT, Sanches JM, Suri JS. Symtosis: A liver ultrasound tissue characterization and risk stratification in optimized deep learning paradigm. Comput Methods Programs Biomed 2018; 155:165-177. DOI: https://doi.org/10.1016/j.cmpb.2017.12.016
29. Ahmad M, Yang J, Ai D, Qadri SF, Wang Y. Deep-Stacked Auto Encoder for Liver Segmentation. In: et al. Advances in Image and Graphics Technologies. IGTA 2017. Communications in Computer and Information Science, 2018; vol 757. ISBN 978-981-10-7388-5. Springer, Singapore. DOI: https://doi.org/10.1007/978-981-10-7389-2_24
30. Meng D, Zhang L, Cao G, Cao W, Zhang G, Hu B. Liver Fibrosis Classification Based on Transfer Learning and FCNet for Ultrasound Images. IEEE Access 2017; 5:5804-5810. DOI: https://doi.org/10.1109/ACCESS.2017.2689058
31. Liu X, Song JL, Wang SH, Zhao JW, Chen YQ. Learning to diagnose cirrhosis with liver capsule guided ultrasound image classification. Sensors (Basel) 2017; 17(1):149. DOI: https://doi.org/10.3390/s17010149
32. Yeom SK, Lee CH, Cha SH, Park CM. Prediction of liver cirrhosis, using diagnostic imaging tools. World J Hepatol 2015; 7(17):2069-2079. DOI: https://doi.org/10.4254/wjh.v7.i17.2069
33. Oezdemir I, Wessner CE, Shaw C, Eisenbrey JR, Hoyt K. Tumor vascular networks depicted in contrast-enhanced ultrasound images as a predictor for transarterial chemoembolization treatment response. Ultrasound Med Biol 2020; 46(9):2276-2286. DOI: https://doi.org/10.1016/j.ultrasmedbio.2020.05.010
34. Wilkinson TJ, Ashman J, Baker LA, Watson EL, Smith AC. Quantitative muscle ultrasonography using 2d textural analysis: A novel approach to assess skeletal muscle structure and quality in chronic kidney disease. Ultrason Imaging 2021; 43(3):139-148. DOI: https://doi.org/10.1177/01617346211009788
35. Yu Z, Tan EL, Ni D, Qin J, Chen S, Li S, Lei B, Wang T. A deep convolutional neural network-based framework for automatic fetal facial standard plane recognition. IEEE J Biomed Health Inform 2018; 22(3):874-885. DOI: https://doi.org/10.1109/JBHI.2017.2705031
36. Wu L, Cheng JZ, Li S, Lei B, Wang T, Ni D. FUIQA: Fetal ultrasound image quality assessment with deep convolutional networks. IEEE Trans Cybern 2017; 47(5):1336-1349. DOI: https://doi.org/10.1109/TCYB.2017.2671898
37. Chen H, Wu L, Dou Q, Qin J, Li S, Cheng JZ, Ni D, Heng PA. Ultrasound standard plane detection using a composite neural network framework. IEEE Trans Cybern 2017; 47(6):1576-1586. DOI: https://doi.org/10.1109/TCYB.2017.2685080
38. Hetherington J, Lessoway V, Gunka V, Abolmaesumi P, Rohling R. SLIDE: Automatic spine level identification system using a deep convolutional neural network. Int J Comput Assist Radiol Surg 2017; 12(7):1189-1198. DOI: https://doi.org/10.1007/s11548-017-1575-8
39. Rodziewicz TL, Houseman B, Hipskind JE. Medical Error Reduction and Prevention. [Updated 2022 May 1]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2022. Available at: https://www.ncbi.nlm.nih.gov/books/NBK499956/
40. Drukker L, Noble JA, Papageorghiou AT. Introduction to artificial intelligence in ultrasound imaging in obstetrics and gynecology. Ultrasound Obstet Gynecol 2020; 56(4):498-505. DOI: https://doi.org/10.1002/uog.22122
41. Narang A, Bae R, Hong H, Thomas Y, Surette S, Cadieu C, Chaudhry A, Martin RP, McCarthy PM, Rubenson DS, Goldstein S, Little SH, Lang RM, Weissman NJ, Thomas JD. Utility of a deep-learning algorithm to guide novices to acquire echocardiograms for limited diagnostic use. JAMA Cardiol 2021; 6(6):624-632. DOI: https://doi.org/10.1001/jamacardio.2021.0185
How to Cite
Santacruz-Yaah, X. E. (2022). Application of Artificial Intelligence to Ultrasonography. Science Insights, 41(1), 577–581. https://doi.org/10.15354/si.22.re069
Section
Review
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