Assessment of Carotid Artery Vibrations by Using Optical Flow Methods on Ultrasound Images
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Abstract
Background: This study aims to extract carotid wall vibrations non-invasively and evaluate changes in the carotid artery caused by age, BMI, and sex. Such evaluation can increase the possibility of detecting wall stiffness and atherosclerosis in the early stages and prevent heart attack and death.
Method: To extract small vibrations of the carotid wall, the image-tracking method, and optical flow with four different methods were used. The study involved twenty participants, comprising six females and fourteen males, with a mean age of 36.25 years, mean weight of 75.2 Kg, and mean BMI of 25. The posterior wall motion and vibration were extracted using ultrasound RF signals.
Result: 4 optical flow methods, Gunnar-Farneback, Horn- Schunk, Lucas-Kanade, and Lucas-Kanade derivative were used for all samples, and covariance, correlation, P-value, and R-squared were estimated. Results showed the differences in parameters such as age and BMI with carotid wall vibration. These values for age are ( , , , ) and for BMI are ( , , , ), respectively. For gender as a new parameter, a comparison between men’s and women’s vibrations was estimated. The range of measured vibrations by optical flow methods is about to and the mean standard deviation is .
Conclusion: The results presented that gender affects the vibration of the vessel wall, which in men is more than in women. In addition, increasing age and BMI may increase the stiffness of the carotid wall and reduce vibrations that were evaluated previously. Using the Gunner-Farneback method as image tracking for small vibrations is the best way with the highest accuracy.
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3- IS Alimova and EV Tutubalina, "Entity-level classification of adverse drug reaction: A comparative analysis of neural network models." Programming and Computer Software, Vol. 45pp. 439-47, (2019).
4- Claudia Giardina et al., "Adverse drug reactions in hospitalized patients: results of the FORWARD (Facilitation of Reporting in Hospital Ward) study." Frontiers in pharmacology, Vol. 9p. 350, (2018).
5- Jonathan A Galli, Aloknath Pandya, Michelle Vega‐Olivo, Chandra Dass, Huaqing Zhao, and Gerard J Criner, "Pirfenidone and nintedanib for pulmonary fibrosis in clinical practice: tolerability and adverse drug reactions." Respirology, Vol. 22 (No. 6), pp. 1171-78, (2017).
6- Mehdi Gheisari et al., "Deep learning: Applications, architectures, models, tools, and frameworks: A comprehensive survey." CAAI Transactions on Intelligence Technology, (2023).
7- Mustafa Ghaderzadeh, Mehrad Aria, Azamossadat Hosseini, Farkhondeh Asadi, Davood Bashash, and Hassan Abolghasemi, "A fast and efficient CNN model for B‐ALL diagnosis and its subtypes classification using peripheral blood smear images." International Journal of Intelligent Systems, Vol. 37 (No. 8), pp. 5113-33, (2022).
8- Mustafa Ghaderzadeh, Farkhondeh Asadi, Ramezan Jafari, Davood Bashash, Hassan Abolghasemi, and Mehrad Aria, "Deep convolutional neural network–based computer-aided detection system for COVID-19 using multiple lung scans: design and implementation study." Journal of Medical Internet Research, Vol. 23 (No. 4), p. e27468, (2021).
9- Elena Tutubalina and Sergey Nikolenko, "Combination of deep recurrent neural networks and conditional random fields for extracting adverse drug reactions from user reviews." Journal of healthcare engineering, Vol. 2017(2017).
10- Mert Tiftikci, Arzucan Özgür, Yongqun He, and Junguk Hur, "Machine learning-based identification and rule-based normalization of adverse drug reactions in drug labels." BMC bioinformatics, Vol. 20 (No. 21), pp. 1-9, (2019).
11- Kathy Lee et al., "Adverse drug event detection in tweets with semi-supervised convolutional neural networks." in Proceedings of the 26th international conference on world wide web, (2017), pp. 705-14.
12- Chuhan Wu, Fangzhao Wu, Zhigang Yuan, Junxin Liu, Yongfeng Huang, and Xing Xie, "MSA: Jointly detecting drug name and adverse drug reaction mentioning tweets with multi-head self-attention." in Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, (2019), pp. 33-41.
13- Liliya Akhtyamova, Mikhail Alexandrov, and John Cardiff, "Adverse drug extraction in twitter data using convolutional neural network." in 2017 28th International Workshop on Database and Expert Systems Applications (DEXA), (2017): IEEE, pp. 88-92.
14- Sunil Kumar Sahu and Ashish Anand, "Drug-drug interaction extraction from biomedical texts using long short-term memory network." Journal of biomedical informatics, Vol. 86pp. 15-24, (2018).
15- Bharath Dandala, Venkata Joopudi, and Murthy Devarakonda, "Adverse drug events detection in clinical notes by jointly modeling entities and relations using neural networks." Drug safety, Vol. 42pp. 135-46, (2019).
16- Jing Liu, Songzheng Zhao, and Xiaodi Zhang, "An ensemble method for extracting adverse drug events from social media." Artificial intelligence in medicine, Vol. 70pp. 62-76, (2016).
17- Hong-Jie Dai, Musa Touray, Jitendra Jonnagaddala, and Shabbir Syed-Abdul, "Feature engineering for recognizing adverse drug reactions from twitter posts." Information, Vol. 7 (No. 2), p. 27, (2016).
18- Zahra Rezaei, Hossein Ebrahimpour-Komleh, Behnaz Eslami, Ramyar Chavoshinejad, and Mehdi Totonchi, "Adverse drug reaction detection in social media by deep learning methods." Cell Journal (Yakhteh), Vol. 22 (No. 3), p. 319, (2020).
19- Sarthak Jain, Xun Peng, and Byron C Wallace, "Detecting Twitter posts with Adverse Drug Reactions using Convolutional Neural Networks." in SMM4H@ AMIA, (2017), pp. 72-75.
20- KM Deepa Lakshmi and A Prakash, "Evaluating the Adverse Drug Reactions Learning Framework Using Novel Text Mining." International Journal of Applied Engineering Research, Vol. 13 (No. 21), pp. 14983-96, (2018).
21- Sarvnaz Karimi, Chen Wang, Alejandro Metke-Jimenez, Raj Gaire, and Cecile Paris, "Text and data mining techniques in adverse drug reaction detection." ACM Computing Surveys (CSUR), Vol. 47 (No. 4), pp. 1-39, (2015).
22- Shashank Gupta, Manish Gupta, Vasudeva Varma, Sachin Pawar, Nitin Ramrakhiyani, and Girish Keshav Palshikar, "Multi-task learning for extraction of adverse drug reaction mentions from tweets." in European Conference on Information Retrieval, (2018): Springer, pp. 59-71.
23- Peng Ding, Xiaobing Zhou, Xuejie Zhang, Jin Wang, and Zhenfeng Lei, "An attentive neural sequence labeling model for adverse drug reactions mentions extraction." Ieee Access, Vol. 6pp. 73305-15, (2018).
24- Daniel S Budnitz, Nadine Shehab, Scott R Kegler, and Chesley L Richards, "Medication use leading to emergency department visits for adverse drug events in older adults." Annals of internal medicine, Vol. 147 (No. 11), pp. 755-65, (2007).
25- Robert P Schumaker, Michael A Veronin, Trevor Rohm, Matthew Boyett, and Rohit R Dixit, "A data driven approach to profile potential SARS-CoV-2 drug interactions using TylerADE." Journal of International Technology and Information Management, Vol. 30 (No. 3), pp. 108-42, (2021).
26- Eva-Lisa Meldau, "Deep neural networks for inverse de-identification of medical case narratives in reports of suspected adverse drug reactions." ed, (2018).
27- Nitin Ramrakhiyani and Girish Keshav Palshikar, "Multi-task Learning for Extraction of Adverse Drug Reaction Mentions from Tweets." in Advances in Information Retrieval: 40th European Conference on IR Research, ECIR 2018, Grenoble, France, March 26-29, 2018, Proceedings, (2018), Vol. 10772: Springer, p. 59.
| Files | ||
| Issue | Vol 11 No 4 (2024) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/fbt.v11i4.16512 | |
| Keywords | ||
| Optical Flow Ultrasound Image Tracking Age Body Mass Index Radio Ferequency Signal | ||
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How to Cite
1.
Rabiee S, Rangraz P, Shalbaf A. Assessment of Carotid Artery Vibrations by Using Optical Flow Methods on Ultrasound Images. Frontiers Biomed Technol. 2023;11(4):631-641.
