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- Publisher Website: 10.1007/978-981-15-1925-3_31
- Scopus: eid_2-s2.0-85076913503
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Conference Paper: Prediction Model of Scoliosis Progression Bases on Deep Learning
Title | Prediction Model of Scoliosis Progression Bases on Deep Learning |
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Authors | |
Keywords | Scoliosis Deep learning Recurrent neural network Long short-term memory |
Issue Date | 2019 |
Publisher | Springer. |
Citation | Cyberspace Congress 2019: International Conference on Cyberspace Data and Intelligence (CyberDI) & International Conference on Cyber-Living, Cyber-Syndrome and Cyber-Health (CyberLife), Beijing, China, 16-18 December 2019. In Ning, H (ed.). Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health: International 2019 Cyberspace Congress, CyberDI and CyberLife, Proceedings, pt 2, p. 431-440 How to Cite? |
Abstract | By deep learning technique, we present a new approach to model idiopathic single curve scoliosis. We leverage the advanced version of the recurrent neural network, that is, the long short-term memory network, to achieve the goal. We frame scoliosis as a classification problem and a regression problem. A network for classification is designed first. We perform the training and testing with real clinic records that are imputed by various tricks. Using this model, one can classify the current level of scoliosis into three predefined groups via a few publicly measurable indictors, such as body height or arm span. We also design a regression network that can predict the future progression of spine curvature. This model can infer the development in spine curvature at a certain time span according to the changes of other indictors. Both of these models are evaluated by various metrics. The experiment shows that the quantitative picture of the scoliosis can be captured by our models giving a significant performance boost. Hence, the resulting decision-support system can help to decide the necessity of a further intervene both for physicians and patients. |
Description | Session: CyberLife 2019: Cyber Health and Smart Healthcare |
Persistent Identifier | http://hdl.handle.net/10722/290722 |
ISBN | |
Series/Report no. | Communications in Computer and Information Science (CCIS) : v. 1138 |
DC Field | Value | Language |
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dc.contributor.author | Guo, X | - |
dc.contributor.author | Xu, S | - |
dc.contributor.author | Wang, Y | - |
dc.contributor.author | Cheung, JPY | - |
dc.contributor.author | Hu, Y | - |
dc.date.accessioned | 2020-11-02T05:46:11Z | - |
dc.date.available | 2020-11-02T05:46:11Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | Cyberspace Congress 2019: International Conference on Cyberspace Data and Intelligence (CyberDI) & International Conference on Cyber-Living, Cyber-Syndrome and Cyber-Health (CyberLife), Beijing, China, 16-18 December 2019. In Ning, H (ed.). Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health: International 2019 Cyberspace Congress, CyberDI and CyberLife, Proceedings, pt 2, p. 431-440 | - |
dc.identifier.isbn | 978-981-15-1924-6 | - |
dc.identifier.uri | http://hdl.handle.net/10722/290722 | - |
dc.description | Session: CyberLife 2019: Cyber Health and Smart Healthcare | - |
dc.description.abstract | By deep learning technique, we present a new approach to model idiopathic single curve scoliosis. We leverage the advanced version of the recurrent neural network, that is, the long short-term memory network, to achieve the goal. We frame scoliosis as a classification problem and a regression problem. A network for classification is designed first. We perform the training and testing with real clinic records that are imputed by various tricks. Using this model, one can classify the current level of scoliosis into three predefined groups via a few publicly measurable indictors, such as body height or arm span. We also design a regression network that can predict the future progression of spine curvature. This model can infer the development in spine curvature at a certain time span according to the changes of other indictors. Both of these models are evaluated by various metrics. The experiment shows that the quantitative picture of the scoliosis can be captured by our models giving a significant performance boost. Hence, the resulting decision-support system can help to decide the necessity of a further intervene both for physicians and patients. | - |
dc.language | eng | - |
dc.publisher | Springer. | - |
dc.relation.ispartof | Cyberspace Congress 2019: International Conference on Cyberspace Data and Intelligence (CyberDI 2019) and the International Conference on Cyber-Living, Cyber-Syndrome, and Cyber-Health (CyberLife 2019) | - |
dc.relation.ispartof | International 2019 Cyberspace Congress, CyberDI and CyberLife Proceedings | - |
dc.relation.ispartofseries | Communications in Computer and Information Science (CCIS) : v. 1138 | - |
dc.subject | Scoliosis | - |
dc.subject | Deep learning | - |
dc.subject | Recurrent neural network | - |
dc.subject | Long short-term memory | - |
dc.title | Prediction Model of Scoliosis Progression Bases on Deep Learning | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Cheung, JPY: cheungjp@hku.hk | - |
dc.identifier.email | Hu, Y: yhud@hku.hk | - |
dc.identifier.authority | Cheung, JPY=rp01685 | - |
dc.identifier.authority | Hu, Y=rp00432 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1007/978-981-15-1925-3_31 | - |
dc.identifier.scopus | eid_2-s2.0-85076913503 | - |
dc.identifier.hkuros | 317853 | - |
dc.identifier.hkuros | 332057 | - |
dc.identifier.volume | pt 2 | - |
dc.identifier.spage | 431 | - |
dc.identifier.epage | 440 | - |
dc.publisher.place | Singapore | - |