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 Feb;32(2):1353-1361.
doi: 10.1007/s00330-021-08190-4. Epub 2021 Aug 4.

Deep learning in knee imaging: a systematic review utilizing a Checklist for Artificial Intelligence in Medical Imaging (CLAIM)

Liping Si #  1 , Jingyu Zhong #  1 , Jiayu Huo  2 , Kai Xuan  2 , Zixu Zhuang  2 , Yangfan Hu  3 , Qian Wang  4 , Huan Zhang  5 , Weiwu Yao  6
Affiliations

Deep learning in knee imaging: a systematic review utilizing a Checklist for Artificial Intelligence in Medical Imaging (CLAIM)

Liping Si et al. Eur Radiol. 2022 Feb.

Abstract

Purpose: Our purposes were (1) to explore the methodologic quality of the studies on the deep learning in knee imaging with CLAIM criterion and (2) to offer our vision for the development of CLAIM to assure high-quality reports about the application of AI to medical imaging in knee joint.

Materials and methods: A Checklist for Artificial Intelligence in Medical Imaging systematic review was conducted from January 1, 2015, to June 1, 2020, using PubMed, EMBASE, and Web of Science databases. A total of 36 articles discussing deep learning applications in knee joint imaging were identified, divided by imaging modality, and characterized by imaging task, data source, algorithm type, and outcome metrics.

Results: A total of 36 studies were identified and divided into: X-ray (44.44%) and MRI (55.56%). The mean CLAIM score of the 36 studies was 27.94 (standard deviation, 4.26), which was 66.53% of the ideal score of 42.00. The CLAIM items achieved an average good inter-rater agreement (ICC 0.815, 95% CI 0.660-0.902). In total, 32 studies performed internal cross-validation on the data set, while only 4 studies conducted external validation of the data set.

Conclusions: The overall scientific quality of deep learning in knee imaging is insufficient; however, deep learning remains a promising technology for diagnostic or predictive purpose. Improvements in study design, validation, and open science need to be made to demonstrate the generalizability of findings and to achieve clinical applications. Widespread application, pre-trained scoring procedure, and modification of CLAIM in response to clinical needs are necessary in the future.

Key points: • Limited deep learning studies were established in knee imaging with mean score of 27.94, which was 66.53% of the ideal score of 42.00, commonly due to invalidated results, retrospective study design, and absence of a clear definition of the CLAIM items in detail. • A previous trained data extraction instrument allowed reaching moderate inter-rater agreement in the application of the CLAIM, while CLAIM still needs improvement in scoring items and result reporting to become a wide adaptive tool in reviews of deep learning studies.

Keywords: Artificial intelligence; Deep learning; Knee; Quality improvement.

PubMed Disclaimer

References

    1. Prieto-Alhambra D, Judge A, Javaid MK, Cooper C, Diez-Perez A, Arden NK (2014) Incidence and risk factors for clinically diagnosed knee, hip and hand osteoarthritis: influences of age, gender and osteoarthritis affecting other joints. Ann Rheum Dis 73:1659–1664 - DOI
    1. Turkiewicz A, Petersson IF, Bjork J et al (2014) Current and future impact of osteoarthritis on health care: a population-based study with projections to year 2032. Osteoarthritis Cartilage 22:1826–1832 - DOI
    1. Roemer FW, Demehri S, Omoumi P et al (2020) State of the art: imaging of osteoarthritis-revisited 2020. Radiology 296(1):5–21 - DOI
    1. Dunn R, Greenhouse J, James D, Ohlssen D, Mesenbrink P (2020) Risk scoring for time to end-stage knee osteoarthritis: data from the Osteoarthritis Initiative. Osteoarthritis Cartilage 28(8):1020–1029 - DOI
    1. Zhai G, Sun X, Randel E et al (2021) Phenylalanine is a novel marker for radiographic knee osteoarthritis progression: the MOST study. J Rheumatol 48(1):123–128 - DOI

Publication types

LinkOut - more resources