Accuracy of vestibular schwannoma segmentation using deep learning models - a systematic review & meta-analysis.

Journal: Neuroradiology
PMID: 39179652

Abstract

UNLABELLED: Vestibular Schwannoma (VS) is a rare tumor with varied incidence rates, predominantly affecting the 60-69 age group. In the era of artificial intelligence (AI), deep learning (DL) algorithms show promise in automating diagnosis. However, a knowledge gap exists in the automated segmentation of VS using DL. To address this gap, this meta-analysis aims to provide insights into the current state of DL algorithms applied to MR images of VS.

Authors

  • Paweł Łajczak
    Department of Biophysics, Faculty of Medical Sciences in Zabrze, Medical University of Silesia in Katowice, Jordana 18, Mekelweg 5, Zabrze, 40-043,, Poland. pawel.lajczak03@outlook.com.
  • Jakub Matyja
    TU Delft, Mekelweg 5,, Delft 2628 CD,, Netherlands.
  • Kamil Jóźwik
    Department of Biophysics, Faculty of Medical Sciences in Zabrze, Medical University of Silesia in Katowice, Jordana 18, Zabrze, 40-043, Poland.
  • Zbigniew Nawrat
    2Department of Biophysics, Faculty of Medical Sciences in Zabrze, Medical University of Silesia in Katowice, Jordana 18, Zabrze, 40-043, Poland.

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