Skip to main content
PMC home page
Springer Nature - PMC COVID-19 Collection logoLink to Springer Nature - PMC COVID-19 Collection
. 2021 Dec 31;41(6):1158–1164. doi: 10.1007/s11596-021-2501-4

Artificial Intelligence to Diagnose Tibial Plateau Fractures: An Intelligent Assistant for Orthopedic Physicians

Peng-ran Liu 1, Jia-yao Zhang 1, Ming-di Xue 1, Yu-yu Duan 2, Jia-lang Hu 3, Song-xiang Liu 1, Yi Xie 1, Hong-lin Wang 1, Jun-wen Wang 3,✉, Tong-tong Huo 1,✉, Zhe-wei Ye 1,✉
PMCID: PMC8718992  PMID: 34971441

Abstract

Objective

To explore a new artificial intelligence (AI)-aided method to assist the clinical diagnosis of tibial plateau fractures (TPFs) and further measure its validity and feasibility.

Methods

A total of 542 X-rays of TPFs were collected as a reference database. An AI algorithm (RetinaNet) was trained to analyze and detect TPF on the X-rays. The ability of the AI algorithm was determined by indexes such as detection accuracy and time taken for analysis. The algorithm performance was also compared with orthopedic physicians.

Results

The AI algorithm showed a detection accuracy of 0.91 for the identification of TPF, which was similar to the performance of orthopedic physicians (0.92±0.03). The average time spent for analysis of the AI was 0.56 s, which was 16 times faster than human performance (8.44±3.26 s).

Conclusion

The AI algorithm is a valid and efficient method for the clinical diagnosis of TPF. It can be a useful assistant for orthopedic physicians, which largely promotes clinical workflow and further guarantees the health and security of patients.

Key words: artificial intelligence, tibial plateau, fracture, diagnosis

Acknowledgments

The authors would like to thank the generous help and support from the Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology.

Footnotes

This study was supported by the National Natural Science Foundation of China (No. 81974355 and No. 82172525).

Conflict of Interest Statement

The authors declare that there are no conflicts of interest relevant to this article.

Contributor Information

Peng-ran Liu, Email: lprlprlprwd@163.com.

Jun-wen Wang, Email: wjw0730@163.com.

Tong-tong Huo, Email: D202081631@hust.edu.cn.

Zhe-wei Ye, Email: yezhewei@hust.edu.cn.

References

  • 1.Schulak DJ, Gunn DR. Fractures of tibial plateaus. A review of the literature. Clin Orthop Relat Res. 1975;109:166–177. doi: 10.1097/00003086-197506000-00025. [DOI] [PubMed] [Google Scholar]
  • 2.Cho JW, Kim J, Cho WT, et al. Approaches and fixation of the posterolateral fracture fragment in tibial plateau fractures: a review with an emphasis on rim plating via modified anterolateral approach. Int Orthop. 2017;41(9):1887–1897. doi: 10.1007/s00264-017-3563-6. [DOI] [PubMed] [Google Scholar]
  • 3.Xie X, Zhan Y, Wang Y, et al. Comparative Analysis of Mechanism-Associated 3-Dimensional Tibial Plateau Fracture Patterns. J Bone Joint Surg Am. 2020;102(5):410–418. doi: 10.2106/JBJS.19.00485. [DOI] [PubMed] [Google Scholar]
  • 4.Hofmann A, Gorbulev S, Guehring T, et al. Autologous Iliac Bone Graft Compared with Biphasic Hydroxyapatite and Calcium Sulfate Cement for the Treatment of Bone Defects in Tibial Plateau Fractures: A Prospective, Randomized, Open-Label, Multicenter Study. J Bone Joint Surg Am. 2020;102(3):179–193. doi: 10.2106/JBJS.19.00680. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Guly HR. Diagnostic errors in an accident and emergency department. Emerg Med J. 2001;18(4):263–269. doi: 10.1136/emj.18.4.263. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Berlin L. Defending the “missed” radiographic diagnosis. AJR Am J Roentgenol. 2001;176(2):317–322. doi: 10.2214/ajr.176.2.1760317. [DOI] [PubMed] [Google Scholar]
  • 7.Pinto A, Reginelli A, Pinto F, et al. Errors in imaging patients in the emergency setting. Br J Radiol. 2016;89(1061):20150914. doi: 10.1259/bjr.20150914. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Myers TG, Ramkumar PN, Ricciardi BF, et al. Artificial Intelligence and Orthopaedics: An Introduction for Clinicians. J Bone Joint Surg Am. 2020;102(9):830–840. doi: 10.2106/JBJS.19.01128. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Wang S, Yang DM, Rong R, et al. Pathology Image Analysis Using Segmentation Deep Learning Algorithms. Am J Pathol. 2019;189(9):1686–1698. doi: 10.1016/j.ajpath.2019.05.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Ding Z, Shi H, Zhang H, et al. Gastroenterologist-Level Identification of Small-Bowel Diseases and Normal Variants by Capsule Endoscopy Using a Deep-Learning Model. Gastroenterology. 2019;157(4):1044–1054. doi: 10.1053/j.gastro.2019.06.025. [DOI] [PubMed] [Google Scholar]
  • 11.Nguyen DT, Pham TD, Batchuluun G, et al. Artificial Intelligence-Based Thyroid Nodule Classification Using Information from Spatial and Frequency Domains. J Clin Med. 2019;8(11):1976. doi: 10.3390/jcm8111976. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Chen K, Zhai X, Sun K, et al. A narrative review of machine learning as promising revolution in clinical practice of scoliosis. Ann Transl Med. 2021;9(1):67. doi: 10.21037/atm-20-5495. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Norman B, Pedoia V, Noworolski A, et al. Applying Densely Connected Convolutional Neural Networks for Staging Osteoarthritis Severity from Plain Radiographs. J Digit Imaging. 2019;32(3):471–477. doi: 10.1007/s10278-018-0098-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Pedoia V, Norman B, Mehany SN, et al. 3D convolutional neural networks for detection and severity staging of meniscus and PFJ cartilage morphological degenerative changes in osteoarthritis and anterior cruciate ligament subjects. J Magn Reson Imaging. 2019;49(2):400–410. doi: 10.1002/jmri.26246. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Yao X, Zhou K, Lv B, et al. 3D mapping and classification of tibial plateau fractures. Bone Joint Res. 2020;9(6):258–267. doi: 10.1302/2046-3758.96.BJR-2019-0382.R2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Samsami S, Patzold R, Winkler M, et al. The effect of coronal splits on the structural stability of bi-condylar tibial plateau fractures: a biomechanical investigation. Arch Orthop Trauma Surg. 2020;140(11):1719–1730. doi: 10.1007/s00402-020-03412-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Wang Y, Wang J, Tang J, et al. Arthroscopy Assisted Reduction Percutaneous Internal Fixation versus Open Reduction Internal Fixation for Low Energy Tibia Plateau Fractures. Sci Rep. 2018;8(1):14068. doi: 10.1038/s41598-018-32201-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Yoo H, Kim KH, Singh R, et al. Validation of a Deep Learning Algorithm for the Detection of Malignant Pulmonary Nodules in Chest Radiographs. JAMA Netw Open. 2020;3(9):e2017135. doi: 10.1001/jamanetworkopen.2020.17135. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Wang M, Xia C, Huang L, et al. Deep learning-based triage and analysis of lesion burden for COVID-19: a retrospective study with external validation. Lancet Digit Health. 2020;2(10):e506–e515. doi: 10.1016/S2589-7500(20)30199-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Gan K, Xu D, Lin Y, et al. Artificial intelligence detection of distal radius fractures: a comparison between the convolutional neural network and professional assessments. Acta Orthop. 2019;90(4):394–400. doi: 10.1080/17453674.2019.1600125. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Lindsey R, Daluiski A, Chopra S, et al. Deep neural network improves fracture detection by clinicians. Proc Natl Acad Sci USA. 2018;115(45):11591–11596. doi: 10.1073/pnas.1806905115. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Choi JW, Cho YJ, Lee S, et al. Using a Dual-Input Convolutional Neural Network for Automated Detection of Pediatric Supracondylar Fracture on Conventional Radiography. Invest Radiol. 2020;55(2):101–110. doi: 10.1097/RLI.0000000000000615. [DOI] [PubMed] [Google Scholar]
  • 23.Chung SW, Han SS, Lee JW, et al. Automated detection and classification of the proximal humerus fracture by using deep learning algorithm. Acta Orthop. 2018;89(4):468–473. doi: 10.1080/17453674.2018.1453714. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Gorelik N, Gyftopoulos S. Applications of Artificial Intelligence in Musculoskeletal Imaging: From the Request to the Report. Can Assoc Radiol J. 2021;72(1):45–59. doi: 10.1177/0846537120947148. [DOI] [PubMed] [Google Scholar]
  • 25.Garwood ER, Tai R, Joshi G, et al. The Use of Artificial Intelligence in the Evaluation of Knee Pathology. Semin Musculoskelet Radiol. 2020;24(1):21–29. doi: 10.1055/s-0039-3400264. [DOI] [PubMed] [Google Scholar]
  • 26.Watanabe K, Aoki Y, Matsumoto M. An Application of Artificial Intelligence to Diagnostic Imaging of Spine Disease: Estimating Spinal Alignment From Moire Images. Neurospine. 2019;16(4):697–702. doi: 10.14245/ns.1938426.213. [DOI] [PMC free article] [PubMed] [Google Scholar]

Articles from Current Medical Science are provided here courtesy of Nature Publishing Group

RESOURCES