Re-tear after arthroscopic rotator cuff repair can be predicted using deep learning algorithm
The application of artificial intelligence technology in the medical field has become increasingly prevalent, yet there remains significant room for exploration in its deep implementation. Within the field of orthopedics, which integrates closely with AI due to its extensive data requirements, rotat...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-02-01
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| Series: | Frontiers in Artificial Intelligence |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/frai.2024.1331853/full |
| _version_ | 1797290179493363712 |
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| author | Zhewei Zhang Zhewei Zhang Chunhai Ke Zhibin Zhang Zhibin Zhang Yujiong Chen Yujiong Chen Hangbin Weng Hangbin Weng Jieyang Dong Jieyang Dong Mingming Hao Botao Liu Botao Liu Minzhe Zheng Jin Li Shaohua Ding Yihong Dong Yihong Dong Zhaoxiang Peng |
| author_facet | Zhewei Zhang Zhewei Zhang Chunhai Ke Zhibin Zhang Zhibin Zhang Yujiong Chen Yujiong Chen Hangbin Weng Hangbin Weng Jieyang Dong Jieyang Dong Mingming Hao Botao Liu Botao Liu Minzhe Zheng Jin Li Shaohua Ding Yihong Dong Yihong Dong Zhaoxiang Peng |
| author_sort | Zhewei Zhang |
| collection | DOAJ |
| description | The application of artificial intelligence technology in the medical field has become increasingly prevalent, yet there remains significant room for exploration in its deep implementation. Within the field of orthopedics, which integrates closely with AI due to its extensive data requirements, rotator cuff injuries are a commonly encountered condition in joint motion. One of the most severe complications following rotator cuff repair surgery is the recurrence of tears, which has a significant impact on both patients and healthcare professionals. To address this issue, we utilized the innovative EV-GCN algorithm to train a predictive model. We collected medical records of 1,631 patients who underwent rotator cuff repair surgery at a single center over a span of 5 years. In the end, our model successfully predicted postoperative re-tear before the surgery using 62 preoperative variables with an accuracy of 96.93%, and achieved an accuracy of 79.55% on an independent external dataset of 518 cases from other centers. This model outperforms human doctors in predicting outcomes with high accuracy. Through this methodology and research, our aim is to utilize preoperative prediction models to assist in making informed medical decisions during and after surgery, leading to improved treatment effectiveness. This research method and strategy can be applied to other medical fields, and the research findings can assist in making healthcare decisions. |
| first_indexed | 2024-03-07T19:16:48Z |
| format | Article |
| id | doaj.art-1640ff9cf03d49129f3d634d995fae7b |
| institution | Directory Open Access Journal |
| issn | 2624-8212 |
| language | English |
| last_indexed | 2024-03-07T19:16:48Z |
| publishDate | 2024-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Artificial Intelligence |
| spelling | doaj.art-1640ff9cf03d49129f3d634d995fae7b2024-02-29T12:52:13ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122024-02-01710.3389/frai.2024.13318531331853Re-tear after arthroscopic rotator cuff repair can be predicted using deep learning algorithmZhewei Zhang0Zhewei Zhang1Chunhai Ke2Zhibin Zhang3Zhibin Zhang4Yujiong Chen5Yujiong Chen6Hangbin Weng7Hangbin Weng8Jieyang Dong9Jieyang Dong10Mingming Hao11Botao Liu12Botao Liu13Minzhe Zheng14Jin Li15Shaohua Ding16Yihong Dong17Yihong Dong18Zhaoxiang Peng19Ningbo University affiliated Li Huili Hospital, Ningbo University, Ningbo, ChinaHealth Science Center, Ningbo University, Ningbo, ChinaNingbo University affiliated Li Huili Hospital, Ningbo University, Ningbo, ChinaFaculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, ChinaKey Laboratory of Mobile Network Application Technology of Zhejiang Province, Ningbo University, Ningbo, ChinaNingbo University affiliated Li Huili Hospital, Ningbo University, Ningbo, ChinaHealth Science Center, Ningbo University, Ningbo, ChinaNingbo University affiliated Li Huili Hospital, Ningbo University, Ningbo, ChinaHealth Science Center, Ningbo University, Ningbo, ChinaNingbo University affiliated Li Huili Hospital, Ningbo University, Ningbo, ChinaHealth Science Center, Ningbo University, Ningbo, ChinaNingbo University affiliated Li Huili Hospital, Ningbo University, Ningbo, ChinaNingbo University affiliated Li Huili Hospital, Ningbo University, Ningbo, ChinaHealth Science Center, Ningbo University, Ningbo, ChinaNingbo University affiliated Li Huili Hospital, Ningbo University, Ningbo, ChinaNingbo University affiliated Li Huili Hospital, Ningbo University, Ningbo, ChinaNingbo University affiliated Li Huili Hospital, Ningbo University, Ningbo, ChinaFaculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, ChinaKey Laboratory of Mobile Network Application Technology of Zhejiang Province, Ningbo University, Ningbo, ChinaNingbo University affiliated Li Huili Hospital, Ningbo University, Ningbo, ChinaThe application of artificial intelligence technology in the medical field has become increasingly prevalent, yet there remains significant room for exploration in its deep implementation. Within the field of orthopedics, which integrates closely with AI due to its extensive data requirements, rotator cuff injuries are a commonly encountered condition in joint motion. One of the most severe complications following rotator cuff repair surgery is the recurrence of tears, which has a significant impact on both patients and healthcare professionals. To address this issue, we utilized the innovative EV-GCN algorithm to train a predictive model. We collected medical records of 1,631 patients who underwent rotator cuff repair surgery at a single center over a span of 5 years. In the end, our model successfully predicted postoperative re-tear before the surgery using 62 preoperative variables with an accuracy of 96.93%, and achieved an accuracy of 79.55% on an independent external dataset of 518 cases from other centers. This model outperforms human doctors in predicting outcomes with high accuracy. Through this methodology and research, our aim is to utilize preoperative prediction models to assist in making informed medical decisions during and after surgery, leading to improved treatment effectiveness. This research method and strategy can be applied to other medical fields, and the research findings can assist in making healthcare decisions.https://www.frontiersin.org/articles/10.3389/frai.2024.1331853/fulldeep learningrotator cuff reteargraph convolution networkprediction modelbig data |
| spellingShingle | Zhewei Zhang Zhewei Zhang Chunhai Ke Zhibin Zhang Zhibin Zhang Yujiong Chen Yujiong Chen Hangbin Weng Hangbin Weng Jieyang Dong Jieyang Dong Mingming Hao Botao Liu Botao Liu Minzhe Zheng Jin Li Shaohua Ding Yihong Dong Yihong Dong Zhaoxiang Peng Re-tear after arthroscopic rotator cuff repair can be predicted using deep learning algorithm Frontiers in Artificial Intelligence deep learning rotator cuff retear graph convolution network prediction model big data |
| title | Re-tear after arthroscopic rotator cuff repair can be predicted using deep learning algorithm |
| title_full | Re-tear after arthroscopic rotator cuff repair can be predicted using deep learning algorithm |
| title_fullStr | Re-tear after arthroscopic rotator cuff repair can be predicted using deep learning algorithm |
| title_full_unstemmed | Re-tear after arthroscopic rotator cuff repair can be predicted using deep learning algorithm |
| title_short | Re-tear after arthroscopic rotator cuff repair can be predicted using deep learning algorithm |
| title_sort | re tear after arthroscopic rotator cuff repair can be predicted using deep learning algorithm |
| topic | deep learning rotator cuff retear graph convolution network prediction model big data |
| url | https://www.frontiersin.org/articles/10.3389/frai.2024.1331853/full |
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