ObjectiveTo explore the safety and effectiveness of quadriceps snip in complex total knee arthroplasty (TKA).MethodsA clinical data of 19 cases (29 knees) with complex TKA assisted with quadriceps snip between January 2016 and May 2017 were retrospectively analyzed. There were 9 males (13 knees) and 10 females (16 knees). The age of patients ranged from 34 to 66 years (mean, 50.2 years). Four patients (8 knees) were ankylosing spondylitis, 5 patients (7 knees) were rheumatoid arthritis, and 10 patients (14 knees) were knee osteoarthritis. The average disease duration was 10.9 years (range, 8-15 years). There were 12 knees of Kellgren-Lawrence grade Ⅲ and 17 knees of Kellgren-Lawrence grade Ⅳ. The range of motion (ROM) of knee was (19.86±7.23)°. The clinical and function scores of knee society score (KSS) were 47.86±11.26 and 15.52±11.21, respectively. Postoperative complications, ROM, KSS scores, extensor lag, and prosthesis loosening were observed to evaluate the effectiveness.ResultsAll incisions healed by first intention, and no infection or cardiovascular and cerebrovascular accidents occurred. All patients were followed up 25-39 months (mean, 30.3 months). At last follow-up, the ROM of knee was (91.03±7.30) °, the KSS clinical score was 83.62±9.99 and functional score was 66.38±7.89, showing significant differences when compared with preoperative ones (P<0.05). Postoperative extensor lag (10°, 10°, 15°) occurred in 3 cases. There was no evidence of prosthesis loosening or osteolysis on X-ray films during follow-up.ConclusionThe application of quadriceps snip in complex TKA can effectively improve the operative field exposure and reduce incidence of complications such as patella tendon tearing, patella fracture, and quadriceps tendon injury. The surgical technique of Krackow tendon suture can effectively guarantee early rehabilitation without occurrence of other complications.
ObjectiveTo summarize the clinical application and research progress in unicompartmental knee arthroplasty (UKA).MethodsThe literature related to UKA in recent years was reviewed and the emerging indications, implant options, comparisons between other surgical techniques, and recent advances were summarized.ResultsClinical studies show that UKA has many advantages, such as less trauma, faster recovery, and fewer postoperative complications. At present, the operative indication has been expanded. The body mass index more than 25 kg/m2, less than 60 years old, patellofemoral arthritis, and anterior cruciate ligament dysfunction are no longer considered as contraindications. The prosthesis type in UKA should be selected according to the patient’s condition. In recent years, the robot-assisted UKA can effectively improve the effectiveness, improve patient satisfaction, and reduce postoperative complications.ConclusionWith the development of surgical techniques, designs of prosthesis, and the robotic technology, UKA would be further applicated. As more long-term data on UKA become available, it will further guide clinicians in counseling patients on whether UKA should be performed.
ObjectiveTo investigate the accuracy of preoperative digital-template planning in total hip arthroplasty (THA) via direct anterior approach (DAA) and its effect on the short-term effectiveness.MethodsThe clinical data of 77 patients (109 hips) with osteonecrosis of femoral head who underwent THA via DAA between January 2016 and May 2018 was retrospectively analyzed. According to the type of template, patients were divided into digital-template group (group A, 40 patients, 56 hips) and conventional-template group (group B, 37 patients, 53 hips). There was no significant difference in age, gender, body mass index, the stages of osteonecrosis of femoral head, and preoperative Harris hip score (HHS) (P>0.05). The operation time, intraoperative blood loss, frequencies of intraoperative fluoroscopy, and complications were recorded. Otherwise, the consistency rate of preoperative planning and practical prosthesis size was analyzed. Position of acetabular prosthesis and femoral prosthesis alignment were measured on anteroposterior X-ray film of the pelvis at 3 months after operation. HHS was used to evaluate clinical function.ResultsThe consistency rate of preoperative planning and practical acetabular prosthesis size was significantly higher in group A (80.4%, 45/56) than that in group B (62.3%, 33/53), showing significant difference (χ2=4.38, P=0.04). But there was no significant difference in the consistency rate of preoperative planning and practical femoral prosthesis size between group A (83.9%, 47/56) and group B (79.2%, 42/53）(χ2=0.40, P=0.53). The prosthesis abductions were (40.7±6.4)° in group A and (38.8±7.3)° in group B; the femoral prosthesis alignment deviations were (0.1±1.8)° in group A and (0.3±1.7)° in group B. There was no significant difference in the prosthesis abduction and femoral prosthesis alignment deviation between 2 groups (P>0.05). No prosthesis sinking or loosening occurred during follow-up. The operation time and frequencies of intraoperative fluoroscopy were less in group A than those in group B (P<0.05). But there was no significant difference in intraoperative blood loss between 2 groups (t=1.92, P=0.06). The complication occurred in 1 hip of group A and 6 hips of group B, with no significant difference (P=0.06). All patients were followed up 6-22 months (mean 13.8 months) in group A and 6-24 months (mean, 14.6 months) in group B. At last follow-up, the HHS scores were 91.8±3.1 in group A and 92.6±4.2 in group B, and the difference was not significant (t=1.14, P=0.26).ConclusionPreoperative digital-template planning in THA via DAA is accurate, which can reduce the operation time and frequencies of intraoperative fluoroscopy without enhancing the risk of complication.
Objective To develop a neural network architecture based on deep learning to assist knee CT images automatic segmentation, and validate its accuracy. Methods A knee CT scans database was established, and the bony structure was manually annotated. A deep learning neural network architecture was developed independently, and the labeled database was used to train and test the neural network. Metrics of Dice coefficient, average surface distance (ASD), and Hausdorff distance (HD) were calculated to evaluate the accuracy of the neural network. The time of automatic segmentation and manual segmentation was compared. Five orthopedic experts were invited to score the automatic and manual segmentation results using Likert scale and the scores of the two methods were compared. Results The automatic segmentation achieved a high accuracy. The Dice coefficient, ASD, and HD of the femur were 0.953±0.037, (0.076±0.048) mm, and (3.101±0.726) mm, respectively; and those of the tibia were 0.950±0.092, (0.083±0.101) mm, and (2.984±0.740) mm, respectively. The time of automatic segmentation was significantly shorter than that of manual segmentation [(2.46±0.45) minutes vs. (64.73±17.07) minutes; t=36.474, P<0.001). The clinical scores of the femur were 4.3±0.3 in the automatic segmentation group and 4.4±0.2 in the manual segmentation group, and the scores of the tibia were 4.5±0.2 and 4.5±0.3, respectively. There was no significant difference between the two groups (t=1.753, P=0.085; t=0.318, P=0.752). Conclusion The automatic segmentation of knee CT images based on deep learning has high accuracy and can achieve rapid segmentation and three-dimensional reconstruction. This method will promote the development of new technology-assisted techniques in total knee arthroplasty.