Predicting Postoperative Re-Tear of Arthroscopic Rotator Cuff Repair Using Artificial Intelligence on Imbalanced Data
Retears after rotator cuff surgery are a common complication. Accurate prediction of retear is essential to minimise the risk of retear. Most of the existing big methods for assisted diagnosis are tested on balanced datasets, whereas in the medical field there is data imbalance due to the rare natur...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10870224/ |
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| author | Zhibin Zhang Zhewei Zhang Zhaoxiang Peng Yihong Dong |
| author_facet | Zhibin Zhang Zhewei Zhang Zhaoxiang Peng Yihong Dong |
| author_sort | Zhibin Zhang |
| collection | DOAJ |
| description | Retears after rotator cuff surgery are a common complication. Accurate prediction of retear is essential to minimise the risk of retear. Most of the existing big methods for assisted diagnosis are tested on balanced datasets, whereas in the medical field there is data imbalance due to the rare nature of a particular disease or case. Therefore, this paper proposes a weight-based logits cross-entropy loss function. The model in this paper is mainly divided into two parts. This paper employs a pairwise associative encoder (PAE) to construct edges, calculating similarity scores between different nodes to effectively handle the complementary relationship between phenotypic and non-phenotypic data. The other part is the weight network, which calculates the weights of different nodes of different classes to assign weights to the objective function through the GCN model. The main body of the final objective function is the weighted cross-entropy loss function, while the logits of the model output are adjusted and a regularisation term is added to the objective function in order to deal with the discrete points in the data. The classification performance of the proposed method is tested on the dataset of whether rotator cuff is re-torn after surgery, and the results show that our method achieves state-of-the-art performance in a five-fold cross-validation setting.The code of this work is available at <uri>https://github.com/nishuihan111/WL-GCN.git</uri>. |
| format | Article |
| id | doaj-art-05250625fba7464ca142a77ce69cefa8 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-05250625fba7464ca142a77ce69cefa82025-02-11T00:01:35ZengIEEEIEEE Access2169-35362025-01-0113244872449710.1109/ACCESS.2025.353859510870224Predicting Postoperative Re-Tear of Arthroscopic Rotator Cuff Repair Using Artificial Intelligence on Imbalanced DataZhibin Zhang0https://orcid.org/0009-0003-1805-8614Zhewei Zhang1https://orcid.org/0000-0002-1431-2297Zhaoxiang Peng2https://orcid.org/0000-0001-6508-4100Yihong Dong3https://orcid.org/0000-0002-6048-2377Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, ChinaLi Huili Hospital, Ningbo University, Ningbo, ChinaLi Huili Hospital, Ningbo University, Ningbo, ChinaFaculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, ChinaRetears after rotator cuff surgery are a common complication. Accurate prediction of retear is essential to minimise the risk of retear. Most of the existing big methods for assisted diagnosis are tested on balanced datasets, whereas in the medical field there is data imbalance due to the rare nature of a particular disease or case. Therefore, this paper proposes a weight-based logits cross-entropy loss function. The model in this paper is mainly divided into two parts. This paper employs a pairwise associative encoder (PAE) to construct edges, calculating similarity scores between different nodes to effectively handle the complementary relationship between phenotypic and non-phenotypic data. The other part is the weight network, which calculates the weights of different nodes of different classes to assign weights to the objective function through the GCN model. The main body of the final objective function is the weighted cross-entropy loss function, while the logits of the model output are adjusted and a regularisation term is added to the objective function in order to deal with the discrete points in the data. The classification performance of the proposed method is tested on the dataset of whether rotator cuff is re-torn after surgery, and the results show that our method achieves state-of-the-art performance in a five-fold cross-validation setting.The code of this work is available at <uri>https://github.com/nishuihan111/WL-GCN.git</uri>.https://ieeexplore.ieee.org/document/10870224/Arthroscopic rotator cuff repairimbalance dataartificial intelligencegraph neural network |
| spellingShingle | Zhibin Zhang Zhewei Zhang Zhaoxiang Peng Yihong Dong Predicting Postoperative Re-Tear of Arthroscopic Rotator Cuff Repair Using Artificial Intelligence on Imbalanced Data IEEE Access Arthroscopic rotator cuff repair imbalance data artificial intelligence graph neural network |
| title | Predicting Postoperative Re-Tear of Arthroscopic Rotator Cuff Repair Using Artificial Intelligence on Imbalanced Data |
| title_full | Predicting Postoperative Re-Tear of Arthroscopic Rotator Cuff Repair Using Artificial Intelligence on Imbalanced Data |
| title_fullStr | Predicting Postoperative Re-Tear of Arthroscopic Rotator Cuff Repair Using Artificial Intelligence on Imbalanced Data |
| title_full_unstemmed | Predicting Postoperative Re-Tear of Arthroscopic Rotator Cuff Repair Using Artificial Intelligence on Imbalanced Data |
| title_short | Predicting Postoperative Re-Tear of Arthroscopic Rotator Cuff Repair Using Artificial Intelligence on Imbalanced Data |
| title_sort | predicting postoperative re tear of arthroscopic rotator cuff repair using artificial intelligence on imbalanced data |
| topic | Arthroscopic rotator cuff repair imbalance data artificial intelligence graph neural network |
| url | https://ieeexplore.ieee.org/document/10870224/ |
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