Mulford Kellen L, Kaji Elizabeth S, Grove Austin F, Saniei Sami, Girod-Hoffman Miguel, Maradit-Kremers Hilal, Abdel Matthew P, Taunton Michael J, Wyles Cody C
Orthopedic Surgery Artificial Intelligence Laboratory, Department of Orthopedic Surgery, Mayo Clinic, Rochester, Minnesota.
Mayo Clinic Department of Orthopedic Surgery, Mayo Clinic, Rochester, Minnesota.
J Arthroplasty. 2025 Aug;40(8):2001-2006. doi: 10.1016/j.arth.2025.01.038. Epub 2025 Jan 27.
Minimum joint space width (mJSW) is an important continuous quantitative metric of osteoarthritis progression in the knee. The purpose of this study was to develop an automated measurement algorithm for mJSW in the medial and lateral compartments of the knee that can flexibly handle native knees and knees after arthroplasty.
We developed an end-to-end algorithm consisting of a deep learning segmentation model plus a computer vision algorithm to measure mJSW in the medial and lateral compartments of the knee. Trained annotators segmented 583 images to train, validate, and test a deep learning model that segments the relevant structures for the measurement of mJSW. Trained annotators measured mJSW in 330 independent images to provide ground truth measurements for the development and validation of the computer vision algorithm. Algorithm performance was measured by calculating mean absolute error and constructing the Bland-Altman plots.
The trained segmentation model performed with an average dice score of 0.92 across all images and structures in the 50-image test set. The mean absolute error between the human measurements and the algorithm measurements was 0.85 ± 1.20 mm. The mean error without taking the absolute value was 0.019 mm, demonstrating minimal bias toward overestimating or underestimating mJSW. Of the algorithm mJSW measurements, 73.2% were less than 1 mm and different from human measurements.
We developed and validated an automated algorithm for measuring mJSW on anteroposterior knee radiographs. This artificial intelligence-based algorithm for mJSW will streamline future population-level clinical research in the natural history of the knee joint and allow surgeons and physicians the ability to quickly measure a patient's entire longitudinal series of radiographs to quantitatively assess the radiographic progression of arthritis during clinic visits.
最小关节间隙宽度（mJSW）是膝关节骨关节炎进展的一项重要的连续性定量指标。本研究的目的是开发一种用于测量膝关节内侧和外侧间隙mJSW的自动测量算法，该算法能够灵活处理自然膝关节和关节置换术后的膝关节。
我们开发了一种端到端算法，该算法由深度学习分割模型和计算机视觉算法组成，用于测量膝关节内侧和外侧间隙的mJSW。训练有素的注释人员对583张图像进行分割，以训练、验证和测试一个深度学习模型，该模型对测量mJSW的相关结构进行分割。训练有素的注释人员在330张独立图像中测量mJSW，为计算机视觉算法的开发和验证提供真实测量值。通过计算平均绝对误差和构建布兰德-奥特曼图来衡量算法性能。
在50张图像的测试集中，训练后的分割模型在所有图像和结构上的平均骰子系数得分为0.92。人工测量值与算法测量值之间的平均绝对误差为0.85±1.20毫米。不取绝对值的平均误差为0.019毫米，表明在高估或低估mJSW方面偏差极小。在算法测量的mJSW中，73.2%小于1毫米且与人工测量值不同。
我们开发并验证了一种用于在膝关节前后位X线片上测量mJSW的自动算法。这种基于人工智能的mJSW算法将简化未来关于膝关节自然史的人群水平临床研究，并使外科医生和内科医生能够快速测量患者的整个纵向系列X线片，以便在门诊就诊时定量评估关节炎的影像学进展。
