Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Jun 15;23(1):577.
doi: 10.1186/s12891-022-05524-1.

Automated detection of anterior cruciate ligament tears using a deep convolutional neural network

Yusuke Minamoto  1   2 , Ryuichiro Akagi  3   4   5 , Satoshi Maki  6 , Yuki Shiko  7 , Ryosuke Tozawa  1   2 , Seiji Kimura  1   6   8 , Satoshi Yamaguchi  6   9 , Yohei Kawasaki  10 , Seiji Ohtori  6   8 , Takahisa Sasho  1   6
Affiliations

Automated detection of anterior cruciate ligament tears using a deep convolutional neural network

Yusuke Minamoto et al. BMC Musculoskelet Disord. .

Abstract

Background: The development of computer-assisted technologies to diagnose anterior cruciate ligament (ACL) injury by analyzing knee magnetic resonance images (MRI) would be beneficial, and convolutional neural network (CNN)-based deep learning approaches may offer a solution. This study aimed to evaluate the accuracy of a CNN system in diagnosing ACL ruptures by a single slice from a knee MRI and to compare the results with that of experienced human readers.

Methods: One hundred sagittal MR images from patients with and without ACL injuries, confirmed by arthroscopy, were cropped and used for the CNN training. The final decision by the CNN for intact or torn ACL was based on the probability of ACL tear on a single MRI slice. Twelve board-certified physicians reviewed the same images used by CNN.

Results: The sensitivity, specificity, accuracy, positive predictive value and negative predictive value of the CNN classification was 91.0%, 86.0%, 88.5%, 87.0%, and 91.0%, respectively. The overall values of the physicians' readings were similar, but the specificity was lower than the CNN classification for some of the physicians, thus resulting in lower accuracy for the human readers.

Conclusions: The trained CNN automatically detected the ACL tears with acceptable accuracy comparable to that of human readers.

Keywords: Anterior cruciate ligament; Artificial intelligence; Deep learning; Machine learning; Magnetic resonance imaging.

PubMed Disclaimer

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Image preparation. a The anterior border of the image was cropped at the articular capsule attachment of the anterior border of the tibia, and the posterior border was cropped at the tibial attachment of the posterior cruciate ligament. b The cropped image is used for reading
Fig. 2
Fig. 2
The ROC curve based on the CNN and physicians’ performance. AUC = 0.942 (95% CI, 0.911–0.973). ROC: receiver operating characteristic. CNN: convolutional neural network. AUC: area under the curve
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Mather RC, Koenig L, Kocher MS, Dall TM, Gallo P, Scott DJ, et al. Societal and economic impact of anterior cruciate ligament tears. J Bone Jt Surg - Am. 2013;95:1751–1759. doi: 10.2106/JBJS.L.01705. - DOI - PMC - PubMed
    1. Hootman JM, Dick R, Agel J. Epidemiology of collegiate injuries for 15 sports: Summary and recommendations for injury prevention initiatives. J Athl Train. 2007;42:311–319. - PMC - PubMed
    1. Cimino F, Volk BS, Setter D. Anterior cruciate ligament injury: Diagnosis, management, and prevention. Am Fam Physician. 2010;82:917–922. - PubMed
    1. Ng WHA, Griffith JF, Hung EHY, Paunipagar B, Law BKY, Yung PSH. Imaging of the anterior cruciate ligament. World J Orthop. 2011;2:75–84. doi: 10.5312/wjo.v2.i8.75. - DOI - PMC - PubMed
    1. Mink JH, Levy T, Crues JV. Tears of the anterior cruciate ligament and menisci of the knee: MR imaging evaluation. Radiology. 1988;167:769–774. doi: 10.1148/radiology.167.3.3363138. - DOI - PubMed

MeSH terms