Percutaneous endoscopic lumbar discectomy for L5-S1 disc herniation based on image analysis and clinical findings: A retrospective review of 345 cases

Bibliometric and Visualization Analysis of Research Hotspots and Frontiers in Endoscopic Lumbar Discectomy

Background

The increasing utilization of endoscopic lumbar discectomy (ELD) in spinal surgery has sparked widespread interest and research. This study utilizes bibliometric analysis to identify current research trends and advancements in this innovative surgical technique, with the goal of informing and improving surgical practices.

Methods

We retrieved relevant literature on ELD from the Science Citation Index Expanded (SCI-Expanded) within the Web of Science Core Collection (WoSCC) database as research samples. Various visualization tools, such as VOSviewer, CiteSpace, Scimago Graphica, Pajek, and online bibliometric platform, were employed to generate scientific knowledge maps for the purpose of visual presentation and data analysis.

Results

Over the past two decades, there has been significant progress in the research related to ELD, particularly since 2016. China has emerged as the most productive country, while South Korea and the United States have exerted greater academic influence. Tongji University has contributed the highest number of research output, while academic achievements published by Wooridul Spine Hospital are highly esteemed by scholars. Lee SH and Ruetten S are the most prolific author and the most highly cited author, respectively. World Neurosurg has published the highest number of publications, while Spine has become the most influential journal. Clinical Neurology and Surgery are the primary subject categories. Research in this field primarily revolves around improving ELD techniques, evaluating postoperative efficacy and prognosis prediction, studying complications and risk factors, as well as comparative research with other surgical techniques. Keywords such as risk factors, LDH, PETD, lumbar spinal stenosis, degeneration, recurrent herniation, laminectomy, local anesthesia, and foraminoplasty highlight the current research hotspots and future cutting-edge trends.

Conclusion

This study employed bibliometric analysis to elucidate the research hotspots and frontiers in ELD. The findings have significant implications for advancing research and development in this field.

Disc Height and Angle Changes on Radiographs and Magnetic Resonance Imaging after Anterior or Posterior Percutaneous Endoscopic Cervical Discectomy

Objectives: Cervical disc herniation (CDH) leads to pain, numbness, and potential disability. Percutaneous endoscopic cervical discectomy (PECD) offers an anterior or posterior approach. This study aims to compare postoperative disc height and angle changes one year after PECD, considering both approaches. Methods: We retrospectively reviewed the data from patients with CDH who underwent PECD from October 2017 to July 2022. Cervical disc height was measured using the preoperative and one-year postoperative magnetic resonance imaging (MRI) examinations. Lordotic angle (LA), global alignment angle (GAA), segmental alignment angle (SAA), and slippage distance (SD) at the surgical level were measured on radiographs in the neutral, flexion, and extension positions. Results: Thirty-eight patients who underwent posterior PECD (PPECD) and five patients who underwent anterior PECD (APECD) were included in the evaluation. The mean age of the patients was 47.4 years (range: 29-69 years). There was a significant difference in the preoperative and one-year postoperative GAA and SAA in extension in the PPECD group (p = 0.003 and 0.031, respectively). The mean decreased disc height one-year postoperative was 1.30 mm in the APECD group and 0.3 mm in the PPECD group by MRI. A significant disc height decrease was observed in the APECD group (p < 0.001). Conclusions: Treating CDH with PPECD or APECD is feasible, as it can relieve symptoms and reduce disability. Stability remained unaffected during the first year after surgery, even though there was an increase in angulation during extension. Despite a significant decrease in disc space following APECD, patients reported significant symptom improvement and no new symptoms.

A Multi-Element Identification System Based on Deep Learning for the Visual Field of Percutaneous Endoscopic Spine Surgery

Background

Lumbar disc herniation is a common degenerative lumbar disease with an increasing incidence. Percutaneous endoscopic lumbar discectomy can treat lumbar disc herniation safely and effectively with a minimally invasive procedure. However, the learning curve of this technology is steep, which means that initial learners are often not sufficiently proficient in endoscopic operations, which can easily lead to iatrogenic damage. At present, the application of computer deep learning technology to clinical diagnosis, treatment, and surgical navigation has achieved satisfactory results.

Purpose

The objective of our team is to develop a multi-element identification system for the visual field of endoscopic spine surgery using deep learning algorithms and to evaluate the feasibility of this system.

Method

We established an image database by collecting surgical videos of 48 patients diagnosed with lumbar disc herniation, which was labeled by two spinal surgeons. We selected 6000 images of the visual field of percutaneous endoscopic spine surgery (including various tissue structures and surgical instruments), divided into the training data, validation data, and test data according to 2:1:2. We developed convolutional neural network models based on instance segmentation-Solov2, CondInst, Mask R-CNN and Yolact, and set the four network model backbone as ResNet101 and ResNet50 respectively. Mean average precision (mAP) and frames per second (FPS) were used to measure the performance of each model for classification, localization and recognition in real time, and AP (average) is used to evaluate how easily an element is detected by neural networks based on computer deep learning.

Result

Comprehensively comparing mAP and FSP of each model for bounding box test and segmentation task for the test set of images, we found that Solov2 (ResNet101) (mAP = 73.5%, FPS = 28.9), Mask R-CNN (ResNet101) (mAP = 72.8%, FPS = 28.5) models are the most stable, with higher precision and faster image processing speed. Combining the average precision of the elements in the bounding box test and segmentation tasks in each network, the AP(average) was highest for tool 3 (bbox-0.85, segm-0.89) and lowest for tool 5 (bbox-0.63, segm-0.72) in the instrumentation, whereas in the anatomical tissue elements, the fibrosus annulus (bbox-0.68, segm-0.69) and ligamentum flavum (bbox-0.65, segm-0.62) had higher AP(average),while extra-dural fat (bbox-0.42, segm-0.44) was lowest.

Conclusion

Our team has developed a multi-element identification system for the visual field of percutaneous endoscopic spine surgery adapted to the interlaminar and foraminal approaches, which can identify and track anatomical tissue (nerve, ligamentum flavum, nucleus pulposus, etc.) and surgical instruments (endoscopic forceps, an high-speed diamond burr, etc.), which can be used in the future as a virtual educational tool or applied to the intraoperative real-time assistance system for spinal endoscopic operation.

Estimating surgical probability: Development and validation of a prognostic model for patients with lumbar disc herniation treated with acupuncture

Lumbar disc herniation (LDH) is a common cause of pain in the lumbar spine and legs. While acupuncture has become the primary conservative treatment for LDH, some patients experience treatment failure and require surgery, causing substantial concern for clinicians. We developed an effective personalized clinical prediction model to identify the independent risk factors associated with acupuncture failure in patients with LDH. Our model aimed to predict the probability of surgery within 6 months of acupuncture failure in patients with LDH. A total of 738 patients with LDH who underwent acupuncture at 4 Chinese hospitals between January 2019 and October 2021 were selected. The patients were divided into training (n = 496) and validation (n = 242) cohorts. Seven predictive variables, including smoking, Oswestry Disability Index (ODI) score, lower-limb herniation, disc herniation type, lumbar spinal stenosis, lumbar lateral recess stenosis, and acupuncture frequency, were selected as risk factors using least absolute shrinkage and selection operato (LASSO) regression. A prediction model was developed using multivariate logistic regression analysis and a nomogram was constructed. The model exhibited good discrimination, with an area under the ROC curve (AUC) of 0.903 for the development cohort and 0.899 for the validation cohort. The Hosmer-Lemeshow goodness-of-fit test was a good fit for both cohorts (P = .956 for the development cohort; P = .513 for the validation cohort). Decision curve analysis (DCA) demonstrated that the threshold probabilities for the 2 cohorts ranged from > 4% and 5-95%, respectively. Therefore, the prediction model had a good net benefit. The nomogram established in this study, incorporating 7 risk factors, demonstrated a good predictive ability. It could predict acupuncture failure in LDH patients and the risk of surgery within 6 months, enabling physicians to conduct individualized treatment measures.