BackgroundThe purpose of this study was to evaluate the screw-home movement at the tibiofemoral joint during normal gait by utilizing the 3-dimensional motion capture technique.MethodsFifteen young males and fifteen young females (total 60 knee joints) who had no history of musculoskeletal disease or a particular gait problem were included in this study. Two more markers were attached to the subject in addition to the Helen-Hayes marker set. Thus, two virtual planes, femoral coronal plane (Pf) and tibial coronal plane (Pt), were created by Skeletal Builder software. This study measured the 3-dimensional knee joint movement in the sagittal, coronal, and transverse planes of these two virtual planes (Pf and Pt) during normal gait.ResultsWith respect to kinematics and kinetics, both males and females showed normal adult gait patterns, and the mean difference in the temporal gait parameters was not statistically significant (p > 0.05). In the transverse plane, the screw-home movement occurred as expected during the pre-swing phase and the late-swing phase at an angle of about 17°. However, the tibia rotated externally with respect to the femur, rather than internally, while the knee joint started to flex during the loading response (paradoxical screw-home movement), and the angle was 6°.ConclusionsParadoxical screw-home movement may be an important mechanism that provides stability to the knee joint during the remaining stance phase. Obtaining the kinematic values of the knee joint during gait can be useful in diagnosing and treating the pathological knee joints.

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Purpose This study aimed to evaluate (1) the eicacy of varus-valgus stress radiographs to adjust the preoperative softtissue imbalance and (2) ascertain whether varus-valgus stress radiographs are efective for the correction accuracy in the preoperative planning of the opening wedge high tibial osteotomy (OWHTO). Methods From February 2017 to December 2018, a total of 121 consecutive knees that underwent bi-planar OWHTO were enrolled in this retrospective analysis. Preoperative planning was performed using a weight-bearing line (WBL). Target WBL was determined according to the status of the medial compartments such as cartilage, meniscus, and preoperative arthritic grade. Preoperative varus-valgus stress radiographs were used to assess the preoperative mediolateral ligament imbalance. The inal target correction length of the opening gap was determined by subtracting the diference between the varus-valgus stress radiographs (VVD). All patients were divided into two groups according to the preoperatively planned correction degree: (group A), smaller than average; (group B), larger than average. Patients were also divided into two other groups (VVD adjusted and neglected groups). Results Groups A and B were 56 and 54 knees, respectively. The preoperatively planned correction lengths of the opening gap were 9.33 ± 1.5 and 14.16 ± 3.96 mm, respectively (p < 0.01). Mean values of the VVD were 0.85 ± 0.72, and 1.27 ± 1.78 mm, respectively (p < 0.01). Correction errors were 2.17 ± 2.06 and 3.52 ± 2.16%, respectively (p < 0.01). Planned and inal correction degrees were also signiicantly larger (p < 0.01, and p < 0.01, respectively), because the preoperative WBL ratio was signiicantly smaller in the VVD adjusted group (p < 0.01). ConclusionThe VVD values could reproduce the preoperative soft-tissue imbalance and it was more prominent as the correction degree increased. The strategy of subtracting the VVD as assumed soft-tissue imbalance in the preoperative planning worked well for the correction accuracy during OWHTO. Level of evidence Level IV, retrospective study.

The study compares the diagnostic performance of deep learning (DL) with that of the former radiologist reading of the Kellgren–Lawrence (KL) grade and evaluates whether additional patient data can improve the diagnostic performance of DL. From March 2003 to February 2017, 3000 patients with 4366 knee AP radiographs were randomly selected. DL was trained using knee images and clinical information in two stages. In the first stage, DL was trained only with images and then in the second stage, it was trained with image data and clinical information. In the test set of image data, the areas under the receiver operating characteristic curve (AUC)s of the DL algorithm in diagnosing KL 0 to KL 4 were 0.91 (95% confidence interval (CI), 0.88–0.95), 0.80 (95% CI, 0.76–0.84), 0.69 (95% CI, 0.64–0.73), 0.86 (95% CI, 0.83–0.89), and 0.96 (95% CI, 0.94–0.98), respectively. In the test set with image data and additional patient information, the AUCs of the DL algorithm in diagnosing KL 0 to KL 4 were 0.97 (95% confidence interval (CI), 0.71–0.74), 0.85 (95% CI, 0.80–0.86), 0.75 (95% CI, 0.66–0.73), 0.86 (95% CI, 0.79–0.85), and 0.95 (95% CI, 0.91–0.97), respectively. The diagnostic performance of image data with additional patient information showed a statistically significantly higher AUC than image data alone in diagnosing KL 0, 1, and 2 (p-values were 0.008, 0.020, and 0.027, respectively).The diagnostic performance of DL was comparable to that of the former radiologist reading of the knee osteoarthritis KL grade. Additional patient information improved DL diagnosis in interpreting early knee osteoarthritis.