A deep learning pipeline for detecting vestibular schwannoma patients with unilateral vestibular loss based on kinematic data Article Swipe
Lou C. Kohler Voinov
,
Sergio Sánchez‐Manso
,
Raabeae Aryan
,
Jennifer L. Millar
,
Michael C Schubert
,
Kathleen E. Cullen
,
Lou C. Kohler Voinov
,
Sergio Sánchez‐Manso
,
Raabeae Aryan
,
Jennifer L. Millar
,
Michael C Schubert
,
Kathleen E. Cullen
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-025-29776-8
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-025-29776-8
Detecting subtle balance and coordination impairments in individuals with vestibular schwannomas (VS) remains a challenge, particularly when traditional clinical assessments appear normal. Focusing on patients prior to surgical intervention, this study offers a unique opportunity to capture early motor adaptations before permanent vestibular loss. We developed a deep learning-based classifier to distinguish VS patients from age-matched healthy controls using kinematic data collected during standardized gait tasks. Participants performed a short-duration (< 10 s) straight-path walk at a normal pace (an item from the Functional Gait Assessment) and a 30-second walk with intermittent eye closure. Six inertial measurement units (IMUs) were placed at different body locations, and models were trained using data from each sensor independently. We employed a convolutional neural network (CNN) tailored to this clinical application. The classifier achieved a maximum accuracy of 0.74 for controls and 0.71 for VS patients. Notably, the model detected early-stage compensatory movement patterns—even in the absence of clinical score differences—by extracting features from wrist and trunk sensors, regions not emphasized in standard evaluations. Performance improved with dataset size, particularly with more subjects, highlighting the value of broader data collection in clinical machine learning. Pretraining on external datasets that included both healthy and pathological subjects further improved accuracy, underscoring the importance of dataset diversity. These findings demonstrate the potential of deep learning to detect functional impairment when clinical scoring falls short and provide practical guidance for sensor placement, dataset design, and model training in vestibular and balance disorders.
Related Topics
Concepts
Deep learning
Vestibular system
Artificial intelligence
Physical medicine and rehabilitation
Convolutional neural network
Computer science
Gait analysis
Medicine
Kinematics
Gait
Torso
Balance (ability)
Schwannoma
Trunk
Center of pressure (fluid mechanics)
Pipeline (software)
STRIDE
Machine learning
Classifier (UML)
Artificial neural network
Fall prevention
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s41598-025-29776-8
- https://www.nature.com/articles/s41598-025-29776-8_reference.pdf
- OA Status
- gold
- References
- 57
- OpenAlex ID
- https://openalex.org/W7106504829
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W7106504829Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1038/s41598-025-29776-8Digital Object Identifier
- Title
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A deep learning pipeline for detecting vestibular schwannoma patients with unilateral vestibular loss based on kinematic dataWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-11-25Full publication date if available
- Authors
-
Lou C. Kohler Voinov, Sergio Sánchez‐Manso, Raabeae Aryan, Jennifer L. Millar, Michael C Schubert, Kathleen E. Cullen, Lou C. Kohler Voinov, Sergio Sánchez‐Manso, Raabeae Aryan, Jennifer L. Millar, Michael C Schubert, Kathleen E. CullenList of authors in order
- Landing page
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https://doi.org/10.1038/s41598-025-29776-8Publisher landing page
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https://www.nature.com/articles/s41598-025-29776-8_reference.pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.nature.com/articles/s41598-025-29776-8_reference.pdfDirect OA link when available
- Concepts
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Deep learning, Vestibular system, Artificial intelligence, Physical medicine and rehabilitation, Convolutional neural network, Computer science, Gait analysis, Medicine, Kinematics, Gait, Torso, Balance (ability), Schwannoma, Trunk, Center of pressure (fluid mechanics), Pipeline (software), STRIDE, Machine learning, Classifier (UML), Artificial neural network, Fall preventionTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
57Number of works referenced by this work
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