Identification of L5 vertebra on lumbar spine radiographs using deep learning.
J Int Med Res
; 52(1): 3000605231223881, 2024 Jan.
Article
em En
| MEDLINE
| ID: mdl-38206194
ABSTRACT
OBJECTIVE:
Deep learning is an advanced machine-learning approach that is used in several medical fields. Here, we developed a deep learning model using an object detection algorithm to identify the L5 vertebra on anteroposterior lumbar spine radiographs, and assessed its detection accuracy.METHODS:
We retrospectively recruited 150 participants for whom both anteroposterior whole-spine and lumbar spine radiographs were available. The anteroposterior lumbar spine radiographs of these patients were used as the input data. Of the 150 images, 105 (70%) were randomly selected as the training set, and the remaining 45 (30%) were assigned to the validation set. YOLOv5x, of the YOLOv5 family model, was used to detect the L5 vertebra area.RESULTS:
The mean average precisions 0.5 and 0.75 of the trained L5 detection model were 99.2% and 96.9%, respectively. The model's precision was 95.7% and its recall was 97.8%. Furthermore, 93.3% of the validation data were correctly detected.CONCLUSION:
Our deep learning model showed an outstanding ability to identify L5 vertebrae.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Aprendizado Profundo
Tipo de estudo:
Diagnostic_studies
/
Observational_studies
Limite:
Humans
Idioma:
En
Revista:
J Int Med Res
Ano de publicação:
2024
Tipo de documento:
Article