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Review
. 2024 Apr 17:4:102809.
doi: 10.1016/j.bas.2024.102809. eCollection 2024.

Sensitivity and specificity of machine learning and deep learning algorithms in the diagnosis of thoracolumbar injuries resulting in vertebral fractures: A systematic review and meta-analysis

Hakija Bečulić  1   2 , Emir Begagić  3 , Amina Džidić-Krivić  4 , Ragib Pugonja  2 , Namira Softić  1 , Binasa Bašić  5 , Simon Balogun  6 , Adem Nuhović  7 , Emir Softić  8 , Adnana Ljevaković  5 , Haso Sefo  9 , Sabina Šegalo  10 , Rasim Skomorac  2   11 , Mirza Pojskić  12
Affiliations
Review

Sensitivity and specificity of machine learning and deep learning algorithms in the diagnosis of thoracolumbar injuries resulting in vertebral fractures: A systematic review and meta-analysis

Hakija Bečulić et al. Brain Spine. .

Abstract

Introduction: Clinicians encounter challenges in promptly diagnosing thoracolumbar injuries (TLIs) and fractures (VFs), motivating the exploration of Artificial Intelligence (AI) and Machine Learning (ML) and Deep Learning (DL) technologies to enhance diagnostic capabilities. Despite varying evidence, the noteworthy transformative potential of AI in healthcare, leveraging insights from daily healthcare data, persists.

Research question: This review investigates the utilization of ML and DL in TLIs causing VFs.

Materials and methods: Employing Preferred Reporting Items for Systematic Reviews and Meta-Analyzes (PRISMA) methodology, a systematic review was conducted in PubMed and Scopus databases, identifying 793 studies. Seventeen were included in the systematic review, and 11 in the meta-analysis. Variables considered encompassed publication years, geographical location, study design, total participants (14,524), gender distribution, ML or DL methods, specific pathology, diagnostic modality, test analysis variables, validation details, and key study conclusions. Meta-analysis assessed specificity, sensitivity, and conducted hierarchical summary receiver operating characteristic curve (HSROC) analysis.

Results: Predominantly conducted in China (29.41%), the studies involved 14,524 participants. In the analysis, 11.76% (N = 2) focused on ML, while 88.24% (N = 15) were dedicated to deep DL. Meta-analysis revealed a sensitivity of 0.91 (95% CI = 0.86-0.95), consistent specificity of 0.90 (95% CI = 0.86-0.93), with a false positive rate of 0.097 (95% CI = 0.068-0.137).

Conclusion: The study underscores consistent specificity and sensitivity estimates, affirming the diagnostic test's robustness. However, the broader context of ML applications in TLIs emphasizes the critical need for standardization in methodologies to enhance clinical utility.

Keywords: Artificial intelligence; Deep learning; Machine learning; Thoracolumbar injuries; Vertebral fractures.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
PRISMA flowchart.
Fig. 2
Fig. 2
Temporal distribution of included studies.
Fig. 3
Fig. 3
Geographical distribution of included studies.
Fig. 4
Fig. 4
Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2): a) analysis of bias risk; b) analysis of applicability; c) summary of bias risk assessment; d) summary of applicability analysis; e) Deek's funnel plot depicting publication bias.
Fig. 5
Fig. 5
Estimated values of sensitivity and specificity for included studies in meta-analysis Legend: T – thoracal; L – lumbar; TL – thoracolumbar; * and ** - different DL models used od same cohort.
Fig. 6
Fig. 6
Hierarchical summary receiver operating characteristic (HSROC) curve of included studies.
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