| Title | Deep learning for diagnosing osteonecrosis of the femoral head based on magnetic resonance imaging |
| Authors | Wang, Peixu Liu, Xingyu Xu, Jia Li, Tengqi Sun, Wei Li, Zirong Gao, Fuqiang Shi, Lijun Li, Zhizhuo Wu, Xinjie Xu, Xin Fan, Xiaoyu Li, Chengxin Zhang, Yiling An, Yicheng |
| Affiliation | Chinese Acad Med Sci, Peking Union Med Coll, Grad Sch, Dept Orthoped,China Japan Friendship Hosp,China J, Beijing 100029, Peoples R China Peking Union Med Coll, China Japan Friendship Hosp, Dept Orthoped, Beijing Key Lab Immune Mediated Inflammatory Dis, Beijing 100029, Peoples R China Peking Univ, China Japan Friendship Hosp, Beijing 100029, Peoples R China Tsinghua Univ, Sch Life Sci, Beijing 100084, Peoples R China Tsinghua Shenzhen Int Grad Sch, Inst Biomed & Hlth Engn iBHE, Shenzhen, Peoples R China Longwood Valley Med Technol Co Ltd, Beijing, Peoples R China |
| Keywords | LUMBAR CLASSIFICATION |
| Issue Date | Sep-2021 |
| Publisher | COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE |
| Abstract | Background and objective: Early-stage osteonecrosis of the femoral head (ONFH) can be difficult to detect because of a lack of symptoms. Magnetic resonance imaging (MRI) is sufficiently sensitive to detect ONFH; however, the diagnosis of ONFH requires experience and is time consuming. We developed a fully automatic deep learning model for detecting early-stage ONFH lesions on MRI. Methods: This was a single-center retrospective study. Between January 2016 and December 2019, 298 patients underwent MRI and were diagnosed with ONFH. Of these patients, 110 with early-stage ONFH were included. Using a 7:3 ratio, we randomly divided them into training and testing datasets. All 3640 segments were delineated as the ground truth definition. The diagnostic performance of our model was analyzed using the receiver operating characteristic curve with the area under the receiver operating characteristic curve (AUC) and Hausdorff distance (HD). Differences in the area between the prediction and ground truth definition were assessed using the Pearson correlation and Bland-Altman plot. Results: Our model's AUC was 0.97 with a mean sensitivity of 0.95 (0.95, 0.96) and specificity of 0.97 (0.96, 0.97). Our model's prediction had similar results with the ground truth definition with an average HD of 1.491 and correlation coefficient (r) of 0.84. The bias of the Bland-Altman analyses was 1.4 px (-117.7-120.5 px). Conclusions: Our model could detect early-stage ONFH lesions in less time than the experts. However, future multicenter studies with larger data are required to further verify and improve our model. (c) 2021 Elsevier B.V. All rights reserved. |
| URI | http://hdl.handle.net/20.500.11897/623620 |
| ISSN | 0169-2607 |
| DOI | 10.1016/j.cmpb.2021.106229 |
| Indexed | SCI(E) |
| Appears in Collections: | 中日友好医院 |
