Retrospective cohort study This study aimed to report the incidence and potential risk factors of polyetheretherketone (PEEK) cage subsidence following oblique lateral interbody fusion (OLIF) for lumbar degenerative diseases. We proposed also an algorithm to minimize subsidence following OLIF surgery. The study included a retrospective cohort of 107 consecutive patients (48 men and 59 women; mean age, 67.4 years) who had received either single- or multi-level OLIF between 2012 and 2019. Patients were classified into subsidence and non-subsidence groups. PEEK cage subsidence was defined as any violation of either endplate from the computed tomography scan in both sagittal and coronal views. Preoperative variables such as age, sex, body mass index, bone mineral density (BMD) measured by preoperative dual-energy X-ray absorptiometry, smoking status, corticosteroid use, diagnosis, operative level, multifidus muscle cross-sectional area, and multifidus muscle fatty degeneration were collected. Age-related variables (height and length) were also documented. Univariate and multivariate logistic regression analyses were used to analyze the risk factors of subsidence. Of the 107 patients (137 levels), 50 (46.7%) met the subsidence criteria. Higher PEEK cage height had the strongest association with subsidence (OR = 9.59, P < .001). Other factors significantly associated with cage subsistence included age >60 years (OR = 3.15, P = .018), BMD <−2.5 (OR = 2.78, P = .006), and severe multifidus muscle fatty degeneration (OR = 1.97, P = .023). Risk factors for subsidence in OLIF were age >60 years, BMD < −2.5, higher cage height, and severe multifidus muscle fatty degeneration. Patients who had subsidence had worse early (3 months) postoperative back and leg pain.

Lumbar interbody fusion stands as a preferred surgical solution for degenerative lumbar spine diseases. The procedure primarily aims to establish lumbar segment stability, directly addressing patient symptoms associated with spinal complications. Traditional open surgery, though effective, is linked with notable morbidities and extended recovery time. To mitigate these concerns, minimally invasive surgery (MIS) has garnered significant popularity, presenting an appealing alternative with numerous benefits such as reduced soft tissue trauma, decreased blood loss, and expedited recovery. Among MIS procedures, full endoscopic spinal surgery, characterized by its minimal invasiveness, holds the potential to further minimize morbidities while enhancing surgical outcomes. Endoscopic lumbar interbody fusion, a novel procedure within this paradigm, has gained attention for offering advantages comparable to those of minimally invasive transforaminal lumbar interbody fusion. However, the safety, efficacy, and associated surgical techniques and instrument design of this method continue to be subjects of ongoing debate. This paper critically reviews current evidence on the safety, efficacy, and advantages of endoscopic lumbar spinal interbody fusion, examining whether it could indeed supersede existing mainstream techniques.

Background and Objectives: Intervertebral disc degeneration (IDD) is a common cause of symptomatic axial low back pain. Magnetic resonance imaging (MRI) is currently the standard for the investigation and diagnosis of IDD. Deep learning artificial intelligence models represent a potential tool for rapidly and automatically detecting and visualizing IDD. This study investigated the use of deep convolutional neural networks (CNNs) for the detection, classification, and grading of IDD. Methods: Sagittal images of 1000 IDD T2-weighted MRI images from 515 adult patients with symptomatic low back pain were separated into 800 MRI images using annotation techniques to create a training dataset (80%) and 200 MRI images to create a test dataset (20%). The training dataset was cleaned, labeled, and annotated by a radiologist. All lumbar discs were classified for disc degeneration based on the Pfirrmann grading system. The deep learning CNN model was used for training in detecting and grading IDD. The results of the training with the CNN model were verified by testing the grading of the dataset using an automatic model. Results: The training dataset of the sagittal intervertebral disc lumbar MRI images found 220 IDDs of grade I, 530 of grade II, 170 of grade III, 160 of grade IV, and 20 of grade V. The deep CNN model was able to detect and classify lumbar IDD with an accuracy of more than 95%. Conclusion: The deep CNN model can reliably automatically grade routine T2-weighted MRIs using the Pfirrmann grading system, providing a quick and efficient method for lumbar IDD classification.