未经批准:脊柱骨盆运动,人体矢状平衡的基石,在患者特异性全髋关节置换术中至关重要。
UNASSIGNED:这项研究旨在开发一种新颖的模型,该模型使用反向传播神经网络(BPNN)来预测坐下时的骨盆变化,基于站立的侧面脊柱骨盆X光片。
未经评估:年轻的健康志愿者被纳入研究,取18个脊髓骨盆参数,如骨盆发病率(PI)等。首先,通过Pearson相关性确定与坐位骨盆倾斜(PT)和骶骨倾斜(SS)相关的站立参数。然后,用这些参数作为输入,用PT和SS作为输出,建立了BPNN预测网络。最后,用相对误差(RE)评估预测结果,预测精度(PA),和归一化均方根误差(NRMSE)。
UNASSIGNED:该研究包括145名23.1±2.3岁的志愿者(M:F=51:94)。Pearson分析显示,坐姿PT与6次站立测量相关,坐姿SS与5次相关。最佳BPNN模型预测PT和SS的准确率分别为78.48%和77.54%,分别;至于PI,骨盆形态的常数,是95.99%。
未经批准:在这项研究中,BPNN模型在预测坐姿脊柱骨盆参数方面产生了理想的准确性,它提供了新的见解和工具,用于表征整个运动周期中的脊柱骨盆变化。
UNASSIGNED: Spinopelvic motion, the cornerstone of the sagittal balance of the human body, is pivotal in patient-specific total hip arthroplasty.
UNASSIGNED: This study aims to develop a novel model using back propagation neural network (BPNN) to predict pelvic changes when one sits down, based on standing lateral spinopelvic radiographs.
UNASSIGNED: Young healthy volunteers were included in the study, 18 spinopelvic parameters were taken, such as pelvic incidence (PI) and so on. First, standing parameters correlated with sitting pelvic tilt (PT) and sacral slope (SS) were identified via Pearson correlation. Then, with these parameters as inputs and sitting PT and SS as outputs, the BPNN prediction network was established. Finally, the prediction results were evaluated by relative error (RE), prediction accuracy (PA), and normalized root mean squared error (NRMSE).
UNASSIGNED: The study included 145 volunteers of 23.1 ± 2.3 years old (M:F = 51:94). Pearson analysis revealed sitting PT was correlated with six standing measurements and sitting SS with five. The best BPNN model achieved 78.48% and 77.54% accuracy in predicting PT and SS, respectively; As for PI, a constant for pelvic morphology, it was 95.99%.
UNASSIGNED: In this study, the BPNN model yielded desirable accuracy in predicting sitting spinopelvic parameters, which provides new insights and tools for characterizing spinopelvic changes throughout the motion cycle.