Real-Time Prediction of Transarterial Drug Delivery Based on a Deep Convolutional Neural Network
This study develops a data-driven reduced-order model based on a deep convolutional neural network (CNN) for real-time and accurate prediction of the drug trajectory and concentration field in transarterial chemoembolization therapy to assist in directing the drug to the tumor site. The convolutiona...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/20/10554 |
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| author | Xin-Yi Yuan Yue Hua Nadine Aubry Mansur Zhussupbekov James F. Antaki Zhi-Fu Zhou Jiang-Zhou Peng |
| author_facet | Xin-Yi Yuan Yue Hua Nadine Aubry Mansur Zhussupbekov James F. Antaki Zhi-Fu Zhou Jiang-Zhou Peng |
| author_sort | Xin-Yi Yuan |
| collection | DOAJ |
| description | This study develops a data-driven reduced-order model based on a deep convolutional neural network (CNN) for real-time and accurate prediction of the drug trajectory and concentration field in transarterial chemoembolization therapy to assist in directing the drug to the tumor site. The convolutional and deconvoluational layers are used as the encoder and the decoder, respectively. The input of the network model is designed to contain the information of drug injection location and the blood vessel geometry and the output consists of the drug trajectory and the concentration field. We studied drug delivery in two-dimensional straight, bifurcated blood vessels and the human hepatic artery system and showed that the proposed model can quickly and accurately predict the spatial–temporal drug concentration field. For the human hepatic artery system, the most complex case, the average prediction accuracy was 99.9% compared with the CFD prediction. Further, the prediction time for each concentration field was less than 0.07 s, which is four orders faster than the corresponding CFD simulation. The high performance, accuracy and speed of the CNN model shows the potential for effectively assisting physicians in directing chemoembolization drugs to tumor-bearing segments, thus improving its efficacy in real-time. |
| first_indexed | 2024-03-09T20:45:51Z |
| format | Article |
| id | doaj.art-93237efba1bd4aaca5bb22280481cfad |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T20:45:51Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-93237efba1bd4aaca5bb22280481cfad2023-11-23T22:46:35ZengMDPI AGApplied Sciences2076-34172022-10-0112201055410.3390/app122010554Real-Time Prediction of Transarterial Drug Delivery Based on a Deep Convolutional Neural NetworkXin-Yi Yuan0Yue Hua1Nadine Aubry2Mansur Zhussupbekov3James F. Antaki4Zhi-Fu Zhou5Jiang-Zhou Peng6School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaSino-French Engineer School, Nanjing University of Science and Technology, Nanjing 210094, ChinaDepartment of Mechanical Engineering, Tufts University, Medford, MA 02155, USADepartment of Biomedical Engineering, Cornell University, Ithaca, NY 14853, USADepartment of Biomedical Engineering, Cornell University, Ithaca, NY 14853, USAState Key Laboratory of Multiphase Flow in Power Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaKey Laboratory of Transient Physics, Nanjing University of Science and Technology, Nanjing 210094, ChinaThis study develops a data-driven reduced-order model based on a deep convolutional neural network (CNN) for real-time and accurate prediction of the drug trajectory and concentration field in transarterial chemoembolization therapy to assist in directing the drug to the tumor site. The convolutional and deconvoluational layers are used as the encoder and the decoder, respectively. The input of the network model is designed to contain the information of drug injection location and the blood vessel geometry and the output consists of the drug trajectory and the concentration field. We studied drug delivery in two-dimensional straight, bifurcated blood vessels and the human hepatic artery system and showed that the proposed model can quickly and accurately predict the spatial–temporal drug concentration field. For the human hepatic artery system, the most complex case, the average prediction accuracy was 99.9% compared with the CFD prediction. Further, the prediction time for each concentration field was less than 0.07 s, which is four orders faster than the corresponding CFD simulation. The high performance, accuracy and speed of the CNN model shows the potential for effectively assisting physicians in directing chemoembolization drugs to tumor-bearing segments, thus improving its efficacy in real-time.https://www.mdpi.com/2076-3417/12/20/10554chemoembolizationtransarterial drug deliveryreduced-order modelconvolution neural networksdeep learningconcentration field reconstruction |
| spellingShingle | Xin-Yi Yuan Yue Hua Nadine Aubry Mansur Zhussupbekov James F. Antaki Zhi-Fu Zhou Jiang-Zhou Peng Real-Time Prediction of Transarterial Drug Delivery Based on a Deep Convolutional Neural Network Applied Sciences chemoembolization transarterial drug delivery reduced-order model convolution neural networks deep learning concentration field reconstruction |
| title | Real-Time Prediction of Transarterial Drug Delivery Based on a Deep Convolutional Neural Network |
| title_full | Real-Time Prediction of Transarterial Drug Delivery Based on a Deep Convolutional Neural Network |
| title_fullStr | Real-Time Prediction of Transarterial Drug Delivery Based on a Deep Convolutional Neural Network |
| title_full_unstemmed | Real-Time Prediction of Transarterial Drug Delivery Based on a Deep Convolutional Neural Network |
| title_short | Real-Time Prediction of Transarterial Drug Delivery Based on a Deep Convolutional Neural Network |
| title_sort | real time prediction of transarterial drug delivery based on a deep convolutional neural network |
| topic | chemoembolization transarterial drug delivery reduced-order model convolution neural networks deep learning concentration field reconstruction |
| url | https://www.mdpi.com/2076-3417/12/20/10554 |
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