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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.
Beculic, Hakija; Begagic, Emir; Dzidic-Krivic, Amina; Pugonja, Ragib; Softic, Namira; Basic, Binasa; Balogun, Simon; Nuhovic, Adem; Softic, Emir; Ljevakovic, Adnana; Sefo, Haso; Segalo, Sabina; Skomorac, Rasim; Pojskic, Mirza.
Afiliación
  • Beculic H; Department of Neurosurgery, Cantonal Hospital Zenica, Crkvice 67, 72000, Zenica, Bosnia and Herzegovina.
  • Begagic E; Department of Anatomy, School of Medicine, University of Zenica, Travnicka 1, 72000, Zenica, Bosnia and Herzegovina.
  • Dzidic-Krivic A; Department of General Medicine, School of Medicine, University of Zenica, Travnicka 1, 72000, Zenica, Bosnia and Herzegovina.
  • Pugonja R; Department of Neurology, Cantonal Hospital Zenica, Crkvice 67, 72000, Zenica, Bosnia and Herzegovina.
  • Softic N; Department of Anatomy, School of Medicine, University of Zenica, Travnicka 1, 72000, Zenica, Bosnia and Herzegovina.
  • Basic B; Department of Neurosurgery, Cantonal Hospital Zenica, Crkvice 67, 72000, Zenica, Bosnia and Herzegovina.
  • Balogun S; Department of Neurology, General Hospital Travnik, Kalibunar Bb, 72270, Travnik, Bosnia and Herzegovina.
  • Nuhovic A; Division of Neurosurgery, Department of Surgery, Obafemi Awolowo University Teaching Hospitals Complex, Ilesa Road PMB 5538, 220282, Ile-Ife, Nigeria.
  • Softic E; Department of General Medicine, School of Medicine, University of Sarajevo, Univerzitetska 1, 71000, Sarajevo, Bosnia and Herzegovina.
  • Ljevakovic A; Department of Patophysiology, School of Medicine, University of Zenica, Travnicka 1, 72000, Zenica, Bosnia and Herzegovina.
  • Sefo H; Department of Neurology, General Hospital Travnik, Kalibunar Bb, 72270, Travnik, Bosnia and Herzegovina.
  • Segalo S; Neurosurgery Clinic, University Clinical Center Sarajevo, Bolnicka 25, 71000, Sarajevo, Bosnia and Herzegovina.
  • Skomorac R; Department of Laboratory Technologies, Faculty of Health Siences, University of Sarajevo, Stjepana Tomica 1, 71000, Sarajevo, Bosnia and Herzegovina.
  • Pojskic M; Department of Anatomy, School of Medicine, University of Zenica, Travnicka 1, 72000, Zenica, Bosnia and Herzegovina.
Brain Spine ; 4: 102809, 2024.
Article en En | MEDLINE | ID: mdl-38681175
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Brain Spine Año: 2024 Tipo del documento: Article Pais de publicación: Países Bajos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Brain Spine Año: 2024 Tipo del documento: Article Pais de publicación: Países Bajos