ObjectiveThis study aimed to compare the clinical outcomes between oblique (OLIF) and transforaminal lumbar interbody fusion (TLIF) for patients with degenerative spondylolisthesis during a 2-year follow-up.MethodsPatients with symptomatic degenerative spondylolisthesis who underwent OLIF (OLIF group) or TLIF (TLIF group) were prospectively enrolled in the authors’ hospital and followed up for 2 years. The primary outcomes were treatment effects [changes in visual analog score (VAS) and Oswestry disability index (ODI) from baseline] at 2 years after surgery; these were compared between two groups. Patient characteristics, radiographic parameters, fusion status, and complication rates were also compared.ResultsIn total, 45 patients were eligible for the OLIF group and 47 patients for the TLIF group. The rates of follow-up were 89% and 87% at 2 years, respectively. The comparisons of primary outcomes demonstrated no different changes in VAS-leg (OLIF, 3.4 vs. TLIF, 2.7), VAS-back (OLIF, 2.5 vs. TLIF, 2.1), and ODI (OLIF, 26.8 vs. TLIF, 30). The fusion rates were 86.1% in the TLIF group and 92.5% in the OLIF group at 2 years (P = 0.365). The OLIF group had less estimated blood loss (median, 200 ml) than the TLIF group (median, 300 ml) (P < 0.001). Greater restoration of disc height was obtained by OLIF (mean, 4.6 mm) than the TLIF group (mean, 1.3 mm) in the early postoperative period (P < 0.001). The subsidence rate was lower in the OLIF group than that in the TLIF group (17.5% vs. 38.9%, P = 0.037). The rates of total problematic complications were not different between the two groups (OLIF, 14.6% vs. TLIF, 26.2%, P = 0.192).ConclusionOLIF did not show better clinical outcomes than TLIF for degenerative spondylolisthesis, except for lesser blood loss, greater disc height restoration, and lower subsidence rate.

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The purpose of this study is to develop an automated method for identifying the menarche status of adolescents based on EOS radiographs. We designed a deep-learning-based algorithm that contains a region of interest detection network and a classification network. The algorithm was trained and tested on a retrospective dataset of 738 adolescent EOS cases using a five-fold cross-validation strategy and was subsequently tested on a clinical validation set of 259 adolescent EOS cases. On the clinical validation set, our algorithm achieved accuracy of 0.942, macro precision of 0.933, macro recall of 0.938, and a macro F1-score of 0.935. The algorithm showed almost perfect performance in distinguishing between males and females, with the main classification errors found in females aged 12 to 14 years. Specifically for females, the algorithm had accuracy of 0.910, sensitivity of 0.943, and specificity of 0.855 in estimating menarche status, with an area under the curve of 0.959. The kappa value of the algorithm, in comparison to the actual situation, was 0.806, indicating strong agreement between the algorithm and the real-world scenario. This method can efficiently analyze EOS radiographs and identify the menarche status of adolescents. It is expected to become a routine clinical tool and provide references for doctors’ decisions under specific clinical conditions.