Center of MicroElectroMechanical Systems (CMEMS), University of Minho, Guimarães, Portugal.
MIT Portugal Program, School of Engineering, University of Minho, Guimarães, Portugal.
Eur Radiol. 2024 Sep;34(9):5736-5747. doi: 10.1007/s00330-024-10596-9. Epub 2024 Feb 10.
To develop and validate a deep learning-based approach to automatically measure the patellofemoral instability (PFI) indices related to patellar height and trochlear dysplasia in knee magnetic resonance imaging (MRI) scans.
A total of 763 knee MRI slices from 95 patients were included in the study, and 3393 anatomical landmarks were annotated for measuring sulcus angle (SA), trochlear facet asymmetry (TFA), trochlear groove depth (TGD) and lateral trochlear inclination (LTI) to assess trochlear dysplasia, and Insall-Salvati index (ISI), modified Insall-Salvati index (MISI), Caton Deschamps index (CDI) and patellotrochlear index (PTI) to assess patellar height. A U-Net based network was implemented to predict the landmarks' locations. The successful detection rate (SDR) and the mean absolute error (MAE) evaluation metrics were used to evaluate the performance of the network. The intraclass correlation coefficient (ICC) was also used to evaluate the reliability of the proposed framework to measure the mentioned PFI indices.
The developed models achieved good accuracy in predicting the landmarks' locations, with a maximum value for the MAE of 1.38 ± 0.76 mm. The results show that LTI, TGD, ISI, CDI and PTI can be measured with excellent reliability (ICC > 0.9), and SA, TFA and MISI can be measured with good reliability (ICC > 0.75), with the proposed framework.
This study proposes a reliable approach with promising applicability for automatic patellar height and trochlear dysplasia assessment, assisting the radiologists in their clinical practice.
The objective knee landmarks detection on MRI images provided by artificial intelligence may improve the reproducibility and reliability of the imaging evaluation of trochlear anatomy and patellar height, assisting radiologists in their clinical practice in the patellofemoral instability assessment.
• Imaging evaluation of patellofemoral instability is subjective and vulnerable to substantial intra and interobserver variability. • Patellar height and trochlear dysplasia are reliably assessed in MRI by means of artificial intelligence (AI). • The developed AI framework provides an objective evaluation of patellar height and trochlear dysplasia enhancing the clinical practice of the radiologists.
开发并验证一种基于深度学习的方法，以自动测量膝关节磁共振成像（MRI）扫描中与髌骨高度和滑车发育不良相关的髌股不稳定（PFI）指数。
本研究纳入了 95 名患者的 763 个膝关节 MRI 切片，共标注了 3393 个解剖学标志点，用于测量滑车沟角（SA）、滑车面不对称（TFA）、滑车沟深度（TGD）和外侧滑车倾斜角（LTI），以评估滑车发育不良，同时测量 Insall-Salvati 指数（ISI）、改良 Insall-Salvati 指数（MISI）、Caton-Deschamps 指数（CDI）和髌股指数（PTI），以评估髌骨高度。采用 U-Net 网络来预测标志点的位置。采用成功检测率（SDR）和平均绝对误差（MAE）评估指标来评估网络性能。还采用组内相关系数（ICC）来评估所提出的框架测量所述 PFI 指数的可靠性。
所开发的模型在预测标志点位置方面具有良好的准确性，MAE 的最大值为 1.38±0.76mm。结果表明，LTI、TGD、ISI、CDI 和 PTI 的测量具有极好的可靠性（ICC＞0.9），SA、TFA 和 MISI 的测量具有较好的可靠性（ICC＞0.75），使用所提出的框架可以实现。
本研究提出了一种可靠的方法，具有良好的应用前景，可用于自动评估髌骨高度和滑车发育不良，辅助放射科医生进行临床实践。
人工智能提供的膝关节 MRI 图像上的目标性膝关节标志点检测，可能会提高滑车解剖结构和髌骨高度的影像学评估的可重复性和可靠性，有助于放射科医生在髌股不稳定评估的临床实践中。
髌股不稳定的影像学评估具有主观性，容易受到观察者内和观察者间的显著差异的影响。
人工智能（AI）可可靠地评估髌骨高度和滑车发育不良。
所开发的 AI 框架提供了对髌骨高度和滑车发育不良的客观评估，增强了放射科医生的临床实践能力。
