Approaching expert-level accuracy for differentiating ACL tear types on MRI with deep learning

Xue, Yang1,3; Yang, Shu2; Sun, Wenjie4; Tan, Hui4; Lin, Kaibin1,3; Peng, Li1,3; Wang, Zheng1,3; Zhang, Jianglin5,6,7,8

2024-01-10

10.1038/s41598-024-51666-8

SCIENTIFIC REPORTS   影响因子和分区

2045-2322

Treatment for anterior cruciate ligament (ACL) tears depends on the condition of the ligament. We aimed to identify different tear statuses from preoperative MRI using deep learning-based radiomics with sex and age. We reviewed 862 patients with preoperative MRI scans reflecting ACL status from Hunan Provincial People's Hospital. Based on sagittal proton density-weighted images, a fully automated approach was developed that consisted of a deep learning model for segmenting ACL tissue (ACL-DNet) and a deep learning-based recognizer for ligament status classification (ACL-SNet). The efficacy of the proposed approach was evaluated by using the sensitivity, specificity and area under the receiver operating characteristic curve (AUC) and compared with that of a group of three orthopedists in the holdout test set. The ACL-DNet model yielded a Dice coefficient of 98% +/- 6% on the MRI datasets. Our proposed classification model yielded a sensitivity of 97% and a specificity of 97%. In comparison, the sensitivity of alternative models ranged from 84 to 90%, while the specificity was between 86 and 92%. The AUC of the ACL-SNet model was 99%, demonstrating high overall diagnostic accuracy. The diagnostic performance of the clinical experts as reflected in the AUC was 96%, 92% and 88%, respectively. The fully automated model shows potential as a highly reliable and reproducible tool that allows orthopedists to noninvasively identify the ACL status and may aid in optimizing different techniques, such as ACL remnant preservation, for ACL reconstruction.

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英语

通讯

Hunan provincial nature science foundation of China["2022JJ30189","2021JJ30173"] ; Hunan Provincial Natural Science Foundation of China[HNJG-2021-1120] ; Teaching Reform Research Project of Universities in Hunan Province[23A0643] ; Scientific Research Fund of Hunan Provincial Education Department[[2020]1Y263] ; Science and Technology Foundation of GuiZhou Province[23gyb11] ; Taizhou Science and Technology Plan Project[82073018] ; National Natural Science Foundation of China["JCYJ20210324113001005","JCYJ20210324114212035"]

Science & Technology - Other Topics

Multidisciplinary Sciences

WOS:001139454600004

NATURE PORTFOLIO

Web of Science

1.Hunan First Normal Univ, Sch Comp Sci, Changsha 410205, Peoples R China
2.Hunan Normal Univ, Hunan Prov People′s Hosp, Affiliated Hosp 1, Dept Orthopaed, Changsha 410002, Peoples R China
3.Hunan Prov Key Lab informat technol basic Educ, Changsha 410205, Peoples R China
4.Hunan Normal Univ, Affiliated Hosp 1, Hunan Prov Peoples Hosp, Dept Radiol, Changsha 410002, Peoples R China
5.Jinan Univ, Shenzhen People′s Hosp, Clin Med Coll 2, Dept Dermatol, Shenzhen 518020, Guangdong, Peoples R China
6.Southern Univ Sci & Technol, Affiliated Hosp 1, Shenzhen 518020, Guangdong, Peoples R China
7.Natl Clin Res Ctr Skin Dis, Candidate Branch, Shenzhen 518020, Guangdong, Peoples R China
8.Southern Univ Sci & Technol, Affiliated Hosp 1, Shenzhen 518020, Guangdong, Peoples R China

Xue, Yang,Yang, Shu,Sun, Wenjie,et al. Approaching expert-level accuracy for differentiating ACL tear types on MRI with deep learning[J]. SCIENTIFIC REPORTS,2024,14(1).

Xue, Yang.,Yang, Shu.,Sun, Wenjie.,Tan, Hui.,Lin, Kaibin.,...&Zhang, Jianglin.(2024).Approaching expert-level accuracy for differentiating ACL tear types on MRI with deep learning.SCIENTIFIC REPORTS,14(1).

Xue, Yang,et al."Approaching expert-level accuracy for differentiating ACL tear types on MRI with deep learning".SCIENTIFIC REPORTS 14.1(2024).