Two-Stage Deep Learning Model for Diagnosis of Lumbar Spondylolisthesis Based on Lateral X-Ray Images.
World Neurosurg
; 186: e652-e661, 2024 06.
Article
em En
| MEDLINE
| ID: mdl-38608811
ABSTRACT
BACKGROUND:
Diagnosing early lumbar spondylolisthesis is challenging for many doctors because of the lack of obvious symptoms. Using deep learning (DL) models to improve the accuracy of X-ray diagnoses can effectively reduce missed and misdiagnoses in clinical practice. This study aimed to use a two-stage deep learning model, the Res-SE-Net model with the YOLOv8 algorithm, to facilitate efficient and reliable diagnosis of early lumbar spondylolisthesis based on lateral X-ray image identification.METHODS:
A total of 2424 lumbar lateral radiographs of patients treated in the Beijing Tongren Hospital between January 2021 and September 2023 were obtained. The data were labeled and mutually identified by 3 orthopedic surgeons after reshuffling in a random order and divided into a training set, validation set, and test set in a ratio of 721. We trained 2 models for automatic detection of spondylolisthesis. YOLOv8 model was used to detect the position of lumbar spondylolisthesis, and the Res-SE-Net classification method was designed to classify the clipped area and determine whether it was lumbar spondylolisthesis. The model performance was evaluated using a test set and an external dataset from Beijing Haidian Hospital. Finally, we compared model validation results with professional clinicians' evaluation.RESULTS:
The model achieved promising results, with a high diagnostic accuracy of 92.3%, precision of 93.5%, and recall of 93.1% for spondylolisthesis detection on the test set, the area under the curve (AUC) value was 0.934.CONCLUSIONS:
Our two-stage deep learning model provides doctors with a reference basis for the better diagnosis and treatment of early lumbar spondylolisthesis.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Espondilolistese
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Aprendizado Profundo
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Vértebras Lombares
Limite:
Adult
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Aged
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Revista:
World Neurosurg
Ano de publicação:
2024
Tipo de documento:
Article