RESUMEN
Transforaminal lumbar interbody fusion (TLIF) is a commonly used technique for treating lumbar degenerative diseases. In this study, we developed a fully computer-supported pipeline to predict both the cage height and the degree of lumbar lordosis subtraction from the pelvic incidence (PI-LL) after TLIF surgery, utilizing preoperative X-ray images. The automated pipeline comprised two primary stages. First, the pretrained BiLuNet deep learning model was employed to extract essential features from X-ray images. Subsequently, five machine learning algorithms were trained using a five-fold cross-validation technique on a dataset of 311 patients to identify the optimal models to predict interbody cage height and postoperative PI-LL. LASSO regression and support vector regression demonstrated superior performance in predicting interbody cage height and postoperative PI-LL, respectively. For cage height prediction, the root mean square error (RMSE) was calculated as 1.01, and the model achieved the highest accuracy at a height of 12 mm, with exact prediction achieved in 54.43% (43/79) of cases. In most of the remaining cases, the prediction error of the model was within 1 mm. Additionally, the model demonstrated satisfactory performance in predicting PI-LL, with an RMSE of 5.19 and an accuracy of 0.81 for PI-LL stratification. In conclusion, our results indicate that machine learning models can reliably predict interbody cage height and postoperative PI-LL.
RESUMEN
OBJECTIVE: In this systematic review, we summarized the indications for and outcomes of three main unilateral biportal endoscopic (UBE) approaches for the decompression of degenerative lumbar spinal stenosis (DLSS). METHODS: A comprehensive search of the literature was performed using Ovid Embase, PubMed, Web of Science, and Ovid's Cochrane Library. The following information was collected: surgical data; patients' scores on the Visual Analog Scale (VAS), Oswestry Disability Index (ODI), and Macnab criteria; and surgical complications. RESULTS: In total, 23 articles comprising 7 retrospective comparative studies, 2 prospective comparative studies, 12 retrospectives case series, and 2 randomized controlled trials were selected for quantitative analysis. The interlaminar approach for central and bilateral lateral recess stenoses, contralateral approach for isolated lateral recess stenosis, and paraspinal approach for foraminal stenosis were used in 16, 2, and 4 studies, respectively. In one study, both interlaminar and contralateral approaches were used. L4-5 was the most common level decompressed using the interlaminar and contralateral approaches, whereas L5-S1 was the most common level decompressed using the paraspinal approach. All three approaches provided favorable clinical outcomes at the final follow-up, with considerable improvements in patients' VAS scores for leg pain (63.6-73.5%) and ODI scores (67.2-71%). The overall complication rate was <6%. CONCLUSIONS: The three approaches of UBE surgery are effective and safe for the decompression of various types of DLSS. In the future, long-term prospective studies and randomized control trials are warranted to explore this new technique further and to compare it with conventional surgical techniques.
RESUMEN
Spondylolisthesis refers to the displacement of a vertebral body relative to the vertrabra below it, which can cause radicular symptoms, back pain or leg pain. It usually occurs in the lower lumbar spine, especially in women over the age of 60. The prevalence of spondylolisthesis is expected to rise as the global population ages, requiring prudent action to promptly identify it in clinical settings. The goal of this study was to develop a computer-aided diagnostic (CADx) algorithm, LumbarNet, and to evaluate the efficiency of this model in automatically detecting spondylolisthesis from lumbar X-ray images. Built upon U-Net, feature fusion module (FFM) and collaborating with (i) a P-grade, (ii) a piecewise slope detection (PSD) scheme, and (iii) a dynamic shift (DS), LumbarNet was able to analyze complex structural patterns on lumbar X-ray images, including true lateral, flexion, and extension lateral views. Our results showed that the model achieved a mean intersection over union (mIOU) value of 0.88 in vertebral region segmentation and an accuracy of 88.83% in vertebral slip detection. We conclude that LumbarNet outperformed U-Net, a commonly used method in medical image segmentation, and could serve as a reliable method to identify spondylolisthesis.