Academic Journal
Three-dimensional magnetic resonance imaging-based statistical shape analysis and machine learning-based prediction of patellofemoral instability
| العنوان: | Three-dimensional magnetic resonance imaging-based statistical shape analysis and machine learning-based prediction of patellofemoral instability |
|---|---|
| المؤلفون: | Keita Nagawa, Kaiji Inoue, Yuki Hara, Hirokazu Shimizu, Saki Tsuchihashi, Koichiro Matsuura, Eito Kozawa, Naoki Sugita, Mamoru Niitsu |
| المصدر: | Scientific Reports, Vol 14, Iss 1, Pp 1-10 (2024) |
| بيانات النشر: | Nature Portfolio, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Medicine LCC:Science |
| مصطلحات موضوعية: | Patellofemoral instability, Statistical shape analysis, Three-dimensional magnetic resonance image, Generalized Procrustes analysis, Medicine, Science |
| الوصف: | Abstract This study performed three-dimensional (3D) magnetic resonance imaging (MRI)-based statistical shape analysis (SSA) by comparing patellofemoral instability (PFI) and normal femur models, and developed a machine learning (ML)-based prediction model. Twenty (19 patients) and 31 MRI scans (30 patients) of femurs with PFI and normal femurs, respectively, were used. Bone and cartilage segmentation of the distal femurs was performed and subsequently converted into 3D reconstructed models. The pointwise distance map showed anterior elevation of the trochlea, particularly at the central floor of the proximal trochlea, in the PFI models compared with the normal models. Principal component analysis examined shape variations in the PFI group, and several principal components exhibited shape variations in the trochlear floor and intercondylar width. Multivariate analysis showed that these shape components were significantly correlated with the PFI/non-PFI distinction after adjusting for age and sex. Our ML-based prediction model for PFI achieved a strong predictive performance with an accuracy of 0.909 ± 0.015, and an area under the curve of 0.939 ± 0.009 when using a support vector machine with a linear kernel. This study demonstrated that 3D MRI-based SSA can realistically visualize statistical results on surface models and may facilitate the understanding of complex shape features. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2045-2322 |
| Relation: | https://doaj.org/toc/2045-2322 |
| DOI: | 10.1038/s41598-024-62143-7 |
| URL الوصول: | https://doaj.org/article/27634392fb2544199a2fc0589cb71bfd |
| رقم الانضمام: | edsdoj.27634392fb2544199a2fc0589cb71bfd |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 20452322 |
|---|---|
| DOI: | 10.1038/s41598-024-62143-7 |