24 research outputs found

    Resolution of the lumbosacral fractional curve and evaluation of the risk for adding on in 101 patients with posterior correction of Lenke 3, 4, and 6 curves

    No full text
    OBJECTIVE In double and triple major adolescent idiopathic scoliosis curves it is still controversial whether the lowest instrumented vertebra (LIV) should be L3 or L4. Too short a fusion can impede postoperative distal curve compensation and promote adding on (AON). Longer fusions lower the chance of compensation by alignment changes of the lumbosacral curve (LSC). This study sought to improve prediction accuracy for AON and surgical outcomes in Lenke type 3, 4, and 6 curves.METHODS This was a retrospective multicenter analysis of patients with adolescent idiopathic scoliosis who had Lenke 3, 4, and 6 curves and >= 1 year of follow-up after posterior correction. Resolution of the LSC was studied by changes of LIV tilt, L3 tilt, and L4 tilt, with the variables resembling surrogate measures for the LSC. AON was defined as a disc angle below LIV > 5 degrees at follow-up. A matched-pairs analysis was done of differences between LIV at L3 and at L4. A multivariate prediction analysis evaluated the AON risk in patients with LIV at L3. Clinical outcomes were assessed by the Scoliosis Research Society 22-item questionnaire (SRS-22).RESULTS The sample comprised 101 patients (average age 16 years). The LIV was L3 in 54%, and it was L4 in 39%. At follow-up, 87% of patients showed shoulder balance, 86% had trunk balance, and 64% had a lumbar curve (LC) 5 20 degrees. With an LC 5 20 degrees (p = 0.01), SRS-22 scores were better and AON was less common (26% vs 59%, p = 0.001). Distal extension of the fusion (e.g., LIV at L4) did not have a significant influence on achieving an LSC < 20 degrees; however, higher screw density allowed better LC correction and resulted in better spontaneous LSC correction. AON occurred in 34% of patients, or 40% if the LIV was L3. Patients with AON had a larger residual LSC, worse LC correction, and worse thoracic curve (TC) correction. A total of 44 patients could be included in the matched-pairs analysis. LC correction and TC correction were comparable, but AON was 50% for LIV at L3 and 18% for LIV at L4. Patients without AON had a significantly better LC correction and TC correction (p < 0.01). For patients with LIV at L3, a significant prediction model for AON was established including variables addressed by surgeons: postoperative LC and TC (negative predictive value 78%, positive predictive value 79%, sensitivity 79%, specificity 81%).CONCLUSIONS An analysis of 101 patients with Lenke 3, 4, and 6 curves showed that TC and LC correction had significant influence on LSC resolution and the risk for AON. Improving LC correction and achieving an LC < 20 degrees offers the potential to lower the risk for AON, particularly in patients with LIV at L3

    Surgical treatment of cervical unilateral locked facet in a 9-year-old boy: A case report

    No full text
    Most of the cervical spine injuries in the pediatric population are typically seen in the upper cervical region. Unilateral cervical facet dislocation (UFD) in subaxial region is a rare injury in pediatric population. In this paper, a rare case of delayed locked UFD in a 9-year-old boy with rare injury mechanism treated surgically is reported. Clinical and radiological findings were described. The patient with C6-7 UFD without neurologic deficit was underwent open reduction and internal fixation via anterior and posterior combined approaches. Significant improvement of pain and free motion in cervical spine was obtained. There was no complication during the follow up. Only three case reports presented about the lower cervical spine injury with UFD under the age of 10 were found in the literature

    A Novel Deep Hybrid Model for Automatic Femoral Stem Classification in Hip Arthroplasty From Radiographs: MSFT-Net With CBAM and Transformer Modules

    No full text
    Accurate identification of femoral stem implants in hip arthroplasty is essential for effective revision surgery, minimizing operative complexity, patient morbidity, intraoperative blood loss, and postoperative recovery time. In cases where prior implant data are unavailable, manual identification is often required, posing significant challenges due to its time-consuming and error-prone nature. To solve this problem, a novel hybrid deep learning architecture that includes a convolutional block attention module and a swin transformer with multi-scale feature fusion from pre-trained architectures DenseNet201, VGG19, and InceptionV3 under the transfer learning paradigm was proposed in this study. The proposed multi-scale feature transformer network was trained and validated on a dataset comprising 1266 anteroposterior (A.P.) hip radiographs of 10 different femoral stem implant types. The proposed hybrid deep learning architecture achieved a training accuracy of 96.7% and validation accuracy of 94.86%, significantly outperforming other baseline models. Compared with state-of-the-art methods, the proposed model achieved an absolute accuracy improvement of 9.5% over VGG19 and 7.4% over DenseNet201 and 8.8% over InceptionV3, demonstrating a significant advancement over existing models in femoral stem classification. The average inference time per image was under 1 second. The experimental results demonstrated that the proposed architecture enhances classification performance while reducing overfitting through attention and transformer-based feature refinement. This automated approach facilitates real-time preoperative implant recognition, thereby streamlining surgical planning, potentially reducing operative costs and duration, and improving clinical outcomes
    corecore