Your browser doesn't support javascript.
loading
Applied deep learning in neurosurgery: identifying cerebrospinal fluid (CSF) shunt systems in hydrocephalus patients.
Rhomberg, Thomas; Trivik-Barrientos, Felipe; Hakim, Arsany; Raabe, Andreas; Murek, Michael.
Afiliação
  • Rhomberg T; Department of Neurosurgery, Inselspital, University Hospital Bern, Bern, Switzerland. thomas.rhomberg.1@gmail.com.
  • Trivik-Barrientos F; Department of Neurosurgery and Neurorestoration, Klinikum Klagenfurt Am Wörthersee, Klagenfurt, Austria. thomas.rhomberg.1@gmail.com.
  • Hakim A; Department of Neurosurgery, Landesklinikum Wiener Neustadt, Wiener Neustadt, Austria.
  • Raabe A; Department of Neuroradiology, Inselspital, University Hospital Bern, Bern, Switzerland.
  • Murek M; Department of Neurosurgery, Inselspital, University Hospital Bern, Bern, Switzerland.
Acta Neurochir (Wien) ; 166(1): 69, 2024 Feb 07.
Article em En | MEDLINE | ID: mdl-38321344
ABSTRACT

BACKGROUND:

Over the recent decades, the number of different manufacturers and models of cerebrospinal fluid shunt valves constantly increased. Proper identification of shunt valves on X-ray images is crucial to neurosurgeons and radiologists to derive further details of a specific shunt valve, such as opening pressure settings and MR scanning conditions. The main aim of this study is to evaluate the feasibility of an AI-assisted shunt valve detection system.

METHODS:

The dataset used contains 2070 anonymized images of ten different, commonly used shunt valve types. All images were acquired from skull X-rays or scout CT-images. The images were randomly split into a 80% training and 20% validation set. An implementation in Python with the FastAi library was used to train a convolutional neural network (CNN) using a transfer learning method on a pre-trained model.

RESULTS:

Overall, our model achieved an F1-score of 99% to predict the correct shunt valve model. F1-scores for individual shunt valves ranged from 92% for the Sophysa Sophy Mini SM8 to 100% for several other models.

CONCLUSION:

This technology has the potential to automatically detect different shunt valve models in a fast and precise way and may facilitate the identification of an unknown shunt valve on X-ray or CT scout images. The deep learning model we developed could be integrated into PACS systems or standalone mobile applications to enhance clinical workflows.
Assuntos
Palavras-chave

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Aprendizado Profundo / Hidrocefalia / Neurocirurgia Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Aprendizado Profundo / Hidrocefalia / Neurocirurgia Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2024 Tipo de documento: Article