Classification of speech arrests and speech impairments during awake craniotomy: a multi-databases analysis.

Journal: International journal of computer assisted radiology and surgery
PMID: 39652158

Abstract

PURPOSE: Awake craniotomy presents a unique opportunity to map and preserve critical brain functions, particularly speech, during tumor resection. The ability to accurately assess linguistic functions in real-time not only enhances surgical precision, but also contributes significantly to improving postoperative outcomes. However, today, its evaluation is subjective as it relies on a clinician's observations only. This paper explores the use of a deep learning based model for the objective assessment of speech arrest and speech impairments during awake craniotomy.

Authors

  • Ilias Maoudj
    Laboratoire de Traitement de l'Information Médicale, UMR INSERM 1101, Brest, France. ilias.maoudj@etudiant.univ-brest.fr.
  • Atsushi Kuwano
    Department of Neurosurgery, Tokyo Women's Medical University, Tokyo, Japan.
  • Céline Panheleux
    Laboratoire de Traitement de l'Information Médicale, UMR INSERM 1101, Brest, France.
  • Yuichi Kubota
    Department of Neurosurgery, Tokyo Women's Medical University Adachi Medical Center, Tokyo, Japan.
  • Takakazu Kawamata
    Department of Neurosurgery, Tokyo Women's Medical University, Tokyo, Japan.
  • Yoshihiro Muragaki
    All authors: Institute of Advanced Biomedical Engineering and Science, Tokyo Women's Medical University, Tokyo, Japan.
  • Ken Masamune
    Faculty of Advanced Techno-Surgery, Institute of Advanced Biomedical Engineering and Science, Tokyo Women's Medical University, 8-1 Kawada-cho, Shinjuku-ku, Tokyo, 162-8666, Japan.
  • Romuald Seizeur
    Laboratoire de Traitement de l'Information Médicale, UMR INSERM 1101, Brest, France.
  • Guillaume Dardenne
    Laboratoire de Traitement de l'Information Médicale, UMR INSERM 1101, Brest, France.
  • Manabu Tamura
    Faculty of Advanced Techno-Surgery, Institute of Advanced Biomedical Engineering and Science, Tokyo Women's Medical University, 8-1 Kawada-cho, Shinjuku-ku, Tokyo, 162-8666, Japan.

Keywords

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