Clinical Validation of a Deep Learning Model forSegmenting and Quantifying Intracranial andVentricular Volumes on Computed Tomography Article Swipe
Bruna Garbes Gonçalves Pinto
,
Tayran Milá Mendes Olegário
,
Pedro Vinicius Alves Silva
,
Gabriel Monteiro Ferracioli
,
Artur José Marques Paulo
,
K. Schumacher
,
Mateus Trinconi Cunha
,
Henrique Min Ho Lee
,
Mariana Athaniel Silva Rodrigues
,
Felipe Kitamura
,
Joselisa Péres Queiroz de Paiva
,
Rafael Maffei Loureiro
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-4947326/v1
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-4947326/v1
The field of radiology is experiencing a surge in demand due to advances in medical imaging, particularly in techniques suchas magnetic resonance imaging and computed tomography (CT). However, the interpretation of these scans relies heavilyon the availability of experts, which is challenging in resource-limited regions. Recent advances in artificial intelligence anddeep learning offer promising solutions by assisting radiologists in image interpretation and diagnosis. This study focuses onvalidating DeepCTE3D, a deep learning–based model for segmenting and quantifying intracranial volume (ICV) and lateralventricular volume (LVV) in CT scans. The model’s performance was evaluated using a real-world dataset comprising diversepatient demographics and various scanner models, including normal and pathological scans. The evaluation process involveddeveloping a streamlined pipeline to generate ground-truth results and comparing them to the model’s outputs. DeepCTE3Dachieved high similarity scores for both ICV and LVV. Secondary analyses revealed differences in LVV and ICV between patientsexes and scanner models, although these differences were probably not clinically significant. This study highlights the potentialof DeepCTE3D in enhancing clinical triage and advancing neuroimaging applications, especially in scenarios where MRI is notfeasible.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-4947326/v1
- OA Status
- gold
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403110952
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4403110952Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.21203/rs.3.rs-4947326/v1Digital Object Identifier
- Title
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Clinical Validation of a Deep Learning Model forSegmenting and Quantifying Intracranial andVentricular Volumes on Computed TomographyWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-10-03Full publication date if available
- Authors
-
Bruna Garbes Gonçalves Pinto, Tayran Milá Mendes Olegário, Pedro Vinicius Alves Silva, Gabriel Monteiro Ferracioli, Artur José Marques Paulo, K. Schumacher, Mateus Trinconi Cunha, Henrique Min Ho Lee, Mariana Athaniel Silva Rodrigues, Felipe Kitamura, Joselisa Péres Queiroz de Paiva, Rafael Maffei LoureiroList of authors in order
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https://doi.org/10.21203/rs.3.rs-4947326/v1Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.21203/rs.3.rs-4947326/v1Direct OA link when available
- Concepts
-
Computed tomography, Medical physics, Artificial intelligence, Computer science, Medicine, RadiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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