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Weakly supervised deep learning for diagnosis of multiple vertebral compression fractures in CT.
- Source :
-
European Radiology . Jun2024, Vol. 34 Issue 6, p3750-3760. 11p. - Publication Year :
- 2024
-
Abstract
- Objective: This study aims to develop a weakly supervised deep learning (DL) model for vertebral-level vertebral compression fracture (VCF) classification using image-level labelled data. Methods: The training set included 815 patients with normal (n = 507, 62%) or VCFs (n = 308, 38%). Our proposed model was trained on image-level labelled data for vertebral-level classification. Another supervised DL model was trained with vertebral-level labelled data to compare the performance of the proposed model. Results: The test set included 227 patients with normal (n = 117, 52%) or VCFs (n = 110, 48%). For a fair comparison of the two models, we compared sensitivities with the same specificities of the proposed model and the vertebral-level supervised model. The specificity for overall L1–L5 performance was 0.981. The proposed model may outperform the vertebral-level supervised model with sensitivities of 0.770 vs 0.705 (p = 0.080), respectively. For vertebral-level analysis, the specificities for each L1–L5 were 0.974, 0.973, 0.970, 0.991, and 0.995, respectively. The proposed model yielded the same or better sensitivity than the vertebral-level supervised model in L1 (0.750 vs 0.694, p = 0.480), L3 (0.793 vs 0.586, p < 0.05), L4 (0.833 vs 0.667, p = 0.480), and L5 (0.600 vs 0.600, p = 1.000), respectively. The proposed model showed lower sensitivity than the vertebral-level supervised model for L2, but there was no significant difference (0.775 vs 0.825, p = 0.617). Conclusions: The proposed model may have a comparable or better performance than the supervised model in vertebral-level VCF classification. Clinical relevance statement: Vertebral-level vertebral compression fracture classification aids in devising patient-specific treatment plans by identifying the precise vertebrae affected by compression fractures. Key Points: • Our proposed weakly supervised method may have comparable or better performance than the supervised method for vertebral-level vertebral compression fracture classification. • The weakly supervised model could have classified cases with multiple vertebral compression fractures at the vertebral-level, even if the model was trained with image-level labels. • Our proposed method could help reduce radiologists' labour because it enables vertebral-level classification from image-level labels. [ABSTRACT FROM AUTHOR]
- Subjects :
- *VERTEBRAL fractures
*VERTEBRAE injuries
*DEEP learning
*COMPRESSION fractures
Subjects
Details
- Language :
- English
- ISSN :
- 09387994
- Volume :
- 34
- Issue :
- 6
- Database :
- Academic Search Index
- Journal :
- European Radiology
- Publication Type :
- Academic Journal
- Accession number :
- 177797483
- Full Text :
- https://doi.org/10.1007/s00330-023-10394-9