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A new neural network model that detects graft ruptures and contralateral anterior cruciate ligament injuries
http://hdl.handle.net/10129/0002000944
http://hdl.handle.net/10129/0002000944d6ec1d23-3fdf-4848-9e44-7af73a6b2166
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| アイテムタイプ | リポジトリ登録用アイテムタイプ（シンプル）(1) | |||||||
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| 公開日 | 2025-06-20 | |||||||
| タイトル | ||||||||
| タイトル | A new neural network model that detects graft ruptures and contralateral anterior cruciate ligament injuries | |||||||
| 言語 | en | |||||||
| 資源タイプ | ||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_db06 | |||||||
| 資源タイプ | doctoral thesis | |||||||
| アクセス権 | ||||||||
| アクセス権 | open access | |||||||
| アクセス権URI | http://purl.org/coar/access_right/c_abf2 | |||||||
| 著者 |
宇佐美, 真太郎
× 宇佐美, 真太郎
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| 抄録 | ||||||||
| 内容記述タイプ | Abstract | |||||||
| 内容記述 | Purpose: The high risk of second anterior cruciate ligament (ACL) injuries including graft rupture and contralateral ACL injury after ACL reconstruction is a significant concern for athletes returning to sports. Risk factors for a second ACL injury include multifactorial and complex interactions. The purpose of study was to develop a neural network model for predicting second ACL injury risk in athletes following primary ACL reconstruction using patient characteristics from medical records. Methods: Three hundred and eighty-six patients were included in this study. All patients underwent primary unilateral ACL reconstruction and were followed up for a minimum of 2 years. Fifty-eight features, including demographic data, surgical findings, methods, and pre-and post-operative data, were retrospectively collected from medical records, and features with an incidence of less than 5% were excluded. Finally, 14 features were used for analyzed. The multilayer perceptron (MLP) was composed of four hidden layers with a rectified linear unit as activation and was trained to maximize the area under the receiver operating characteristic curve (auROC) by 3-fold cross-validation. To ascertain the most efficacious combination of characteristics with the highest auROC, a single characteristic was sequentially removed from the full set of variables was conducted. Results: At a minimum 2-year follow-up, 57 knees (14.6%) had a second ACL injury, with an ACL graft rupture rate of 7.7% and a contralateral ACL injury rate of 6.9%. The maximum auROC for predicting graft rupture was 0.81 with two features: young age and hamstring graft. Meanwhile, the maximum auROC for predicting contralateral ACL injury was 0.74 with seven features, including young age, presence of medial meniscus tear at primary ACL reconstruction, small body mass index, hamstring graft, female sex, medial meniscus repair, or treatment simultaneously at primary ACL reconstruction. Conclusion: A neural network model with patient features from medical records detected graft ruptures and contralateral ACL injuries after primary ACL reconstruction with acceptable accuracy. |
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| 言語 | en | |||||||
| 書誌情報 |
en : Knee Surgery, Sports Traumatology, Arthroscopy 巻 32, 号 4, p. 872-880 |
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| 収録物識別子タイプ | EISSN | |||||||
| 収録物識別子 | 1433-7347 | |||||||
| DOI | ||||||||
| 関連タイプ | isVersionOf | |||||||
| 識別子タイプ | DOI | |||||||
| 関連識別子 | https://doi.org/10.1002/ksa.12123 | |||||||
| 権利情報 | ||||||||
| 権利情報 | © 2024 European Society of Sports Traumatology, Knee Surgery and Arthroscopy. | |||||||
| 言語 | en | |||||||
| 権利情報 | ||||||||
| 権利情報 | This is the peer reviewed version of the following article: A new neural network model that detects graft ruptures and contralateral anterior cruciate ligament injuries, which has been published in final form at https://doi.org/10.1002/ksa.12123. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited. | |||||||
| 言語 | en | |||||||
| 出版タイプ | ||||||||
| 出版タイプ | NA | |||||||
| 出版タイプResource | http://purl.org/coar/version/c_be7fb7dd8ff6fe43 | |||||||
| 出版者 | ||||||||
| 出版者 | Wiley | |||||||
| 言語 | en | |||||||
| 学位名 | ||||||||
| 学位名 | 博士（医学） | |||||||
| 言語 | ja | |||||||
| 学位授与機関 | ||||||||
| 学位授与機関名 | 弘前大学 | |||||||
| 言語 | ja | |||||||
| 学位授与年月日 | ||||||||
| 学位授与年月日 | 2025-03-24 | |||||||
| 学位授与番号 | ||||||||
| 学位授与番号 | 甲第2327号 | |||||||
