Development of anesthesiology and improvement of surgical instruments enabled aggressive surgical treatment even in elderly patients, who require more active physical activities than they were in the past. However, there are controversies about the clinical outcome of spinal surgery in elderly patients with spinal stenosis or spondylolisthesis. The purpose of this study is to review the clinical outcome of spinal surgery in elderly patients with spinal stenosis or spondylolisthesis. MEDLINE search on English-language articles was performed. There were 39685 articles from 1967 to 2013 regarding spinal disease, among which 70 dealt with geriatric lumbar surgery. Eighteen out of 70 articles dealt with geriatric lumbar surgery under the diagnosis of spinal stenosis or spondylolisthesis. One was non-randomized prospective, and other seventeen reports were retrospective. One non-randomized prospective and twelve out of seventeen retrospective studies showed that old ages did not affect the clinical outcomes. One non-randomized prospective and ten of seventeen retrospective studies elucidated postoperative complications: some reports showed that postoperative complications increased in elderly patients, whereas the other reports showed that they did not increase. Nevertheless, most complications were minor. There were two retrospective studies regarding the mortality. Mortality which was unrelated to surgical procedure increased, but surgical procedure-related mortality did not increase. Surgery as a treatment option in the elderly patients with the spinal stenosis or spondylolisthesis may be reasonable. However, there is insufficient evidence to make strong recommendations regarding spinal surgery for geriatric patients with spinal stenosis and spondylolisthesis.
PurposeEvaluating lower extremity alignment using full‐leg plain radiographs is an essential step in diagnosis and treatment of patients with knee osteoarthritis. The study objective was to present a deep learning‐based anatomical landmark recognition and angle measurement model, using full‐leg radiographs, and validate its performance.
MethodsA total of 11,212 full‐leg plain radiographs were used to create the model. To train the data, 15 anatomical landmarks were marked by two orthopaedic surgeons. Mechanical lateral distal femoral angle (mLDFA), medial proximal tibial angle (MPTA), joint line convergence angle (JLCA), and hip‐knee‐ankle angle (HKAA) were then measured. For inter‐observer reliability, the inter‐observer intraclass correlation coefficient (ICC) was evaluated by comparing measurements from the model, surgeons, and students, to ground truth measurements annotated by an orthopaedic specialist with 14 years of experience. To evaluate test–retest reliability, all measurements were made twice by each measurer. Intra‐observer ICCs were then derived. Performance evaluation metrics used in previous studies were also derived for direct comparison of the model’s performance.
ResultsInter‐observer ICCs for all angles of the model were 0.98 or higher (p < 0.001). Intra‐observer ICCs for all angles were 1.00, which was higher than that of the orthopaedic specialist (0.97–1.00). Measurements made by the model showed no significant systemic variation. Except for JLCA, angles were precisely measured with absolute error averages under 0.52 degrees and proportion of outliers under 4.26%.
ConclusionsThe deep learning model is capable of evaluating lower extremity alignment with performance as accurate as an orthopaedic specialist with 14 years of experience.
Level of evidenceIII, retrospective cohort study.
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