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
Review
. 2022 Sep 12:9:914903.
doi: 10.3389/fsurg.2022.914903. eCollection 2022.

The development of machine learning in lung surgery: A narrative review

Anas Taha  1 , Dominik Valentin Flury  2 , Bassey Enodien  3 , Stephanie Taha-Mehlitz  4 , Ralph A Schmid  5
Affiliations
Review

The development of machine learning in lung surgery: A narrative review

Anas Taha et al. Front Surg. .

Abstract

Background: Machine learning reflects an artificial intelligence that allows applications to improve their accuracy to predict outcomes, eliminating the need to conduct explicit programming on them. The medical field has increased its focus on establishing tools for integrating machine learning algorithms in laboratory and clinical settings. Despite their importance, their incorporation is minimal in the medical sector yet. The primary goal of this study is to review the development of machine learning in the field of thoracic surgery, especially lung surgery.

Methods: This article used the Preferred Reporting Items for Systematic and Meta-analyses (PRISMA). The sources used to gather data are the PubMed, Cochrane, and CINAHL databases and the Google Scholar search engine.

Results: The study included 19 articles, where ten concentrated on the application of machine learning in especially lung surgery, six focused on the benefits and limitations of machine learning algorithms in lung surgery, and three provided an overview of the future of machine learning in lung surgery.

Conclusion: The outcome of this study indicates that the field of lung surgery has attempted to integrate machine learning algorithms. However, the implementation rate is low, owing to the newness of the concept and the various challenges it encompasses. Also, this study reveals the absence of sufficient literature discussing the application of machine learning in lung surgery. The necessity for future research on the topic area remains evident.

Keywords: deep learning; lung surgery; machine learning; narrative review; thoracic surgery.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Flow chart of the ML model according to Duc TL et al. (4).
Figure 2
Figure 2
PRISMA flow diagram.
See this image and copyright information in PMC

References

    1. Carleo G, Cirac I, Cranmer K, Daudet L, Schuld M, Tishby N, et al. Machine learning and the physical sciences. Rev Mod Phys. (2019) 91(4):045002. 10.1103/RevModPhys.91.045002 - DOI
    1. Mahesh B. Machine learning algorithms-a review. Intl J Sci Res. (2020) 9:381–6. 10.21275/ART20203995 - DOI
    1. Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med. (2019) 380(14):1347–58. 10.1056/NEJMra1814259 - DOI - PubMed
    1. Duc TL, Leiva RG, Casari P, Östberg PO. Machine learning methods for reliable resource provisioning in edge-cloud computing: a survey. ACM Comput Surv. (2019) 52(5):1–39. 10.1145/3341145 - DOI
    1. Borella P, Bargellini A, Marchegiano P, Vecchi E, Marchesi I. Narrative review: hospital-acquired Legionella infections: an update on the procedures for controlling environmental contamination. Ann Ig. (2016) 28:98–108. 10.7416/ai.2016.2088 - DOI - PubMed