New ultra-fast algorithm for cochlear implant misalignment detection.

Artificial intelligence Automatic cochlear image registration Cochlea Cochlea implant Electrode misalignment

Journal

European journal of radiology
ISSN: 1872-7727
Titre abrégé: Eur J Radiol
Pays: Ireland
ID NLM: 8106411

Informations de publication

Date de publication:
Jun 2022
Historique:
received: 01 10 2021
revised: 07 03 2022
accepted: 30 03 2022
pubmed: 8 4 2022
medline: 18 5 2022
entrez: 7 4 2022
Statut: ppublish

Résumé

Postoperative imaging following cochlear implant (CI) placement is currently the only means of diagnosing proper electrode position. Manual multiplanar reconstruction (MPR) analysis of CT and CBCT is time-consuming and requires extensive training. This study aims to evaluate the rate of CI misalignment and to determine the amount of time necessary to reach a diagnosis of correct versus incorrect CI placement for readers of different experience levels, using a novel algorithm for image analysis (ACIR) compared to MPR analysis. The retrospective single centre study included 333 patients with cochlear implant surgery between May 2002 and May 2021. Postoperative CT and CBCT images were evaluated in three subgroups and the time to diagnosis was documented. Group 1: image evaluation using conventional MPR analysis; group 2: image evaluation by an experienced neuroradiologist via a novel ultra-fast algorithm; group 3: image evaluation by a young specialist via novel ultra-fast algorithm. T-test and Pearson's chi-squared test were used for inter-group comparisons. 333 patients (63.3 ± 15.9 years; 188 men) with 335 CIs were evaluated. The rate of CI misalignment diagnosed from 3D imaging was 14.3% (n = 48). MPR analysis required 255.7 ± 70.4 s per temporal bone, whereas Slicer plugin reduced analysis time to 83.3 ± 7.7 s (p < 0.001) for the experienced reader and 89.6 ± 8.7 s for the young specialist (p < 0.001). 3D postoperative imaging reveals high incidences of CI misalignment. Application of a novel ultra-fast algorithm significantly reduces the time for diagnosis compared to MPR analysis for readers of varying experience levels.

Identifiants

pubmed: 35390602
pii: S0720-048X(22)00133-4
doi: 10.1016/j.ejrad.2022.110283
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

110283

Informations de copyright

Copyright © 2022 Elsevier B.V. All rights reserved.

Auteurs

Stephan Waldeck (S)

Department of Diagnostic and Interventional Radiology and Neuroradiology, Bundeswehr Central Hospital Koblenz, Rübenacher Straße 170, 56072 Koblenz, Germany; Institute of Neuroradiology, University Medical Centre Johannes Gutenberg University Mainz, Langenbeckstraße 1, 55131 Mainz, Germany. Electronic address: Waldeck@bundeswehrzentralkrankenhaus.de.

Rania Helal (R)

Department of Radiodiagnosis, Ain Shams University, Cairo, Egypt.

Ibraheem Al-Dhamari (I)

Department of Computational Visualistics, Koblenz University, Universtaetstrasse 1, 56070 Koblenz, Germany.

Sandra Schmidt (S)

Department of ENT Surgery, Bundeswehr Central Hospital Koblenz, Rübenacher Straße 170, 56072 Koblenz, Germany.

Christian von Falck (C)

Institute of Diagnostic and Interventional Radiology, Hannover Medical School, Carl-Neuberg-Straße 1, 30625 Hanover, Germany.

René Chapot (R)

Department of Neuroradiology, Alfried Krupp Krankenhaus, Alfried-Krupp-Strasse 21, 45131 Essen, Germany.

Marc Brockmann (M)

Institute of Neuroradiology, University Medical Centre Johannes Gutenberg University Mainz, Langenbeckstraße 1, 55131 Mainz, Germany.

Daniel Overhoff (D)

Department of Diagnostic and Interventional Radiology and Neuroradiology, Bundeswehr Central Hospital Koblenz, Rübenacher Straße 170, 56072 Koblenz, Germany; Department of Radiology and Nuclear Medicine, University Medical Centre Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.

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