Evidence map›Paper›PMID 42437795›Full record

ArticleEuropean spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society2026

Optimizing the mathematical model and technical standards for unilateral biportal endoscopic spinal surgery through machine learning-based video analysis.

DingZhong Cheng, Hui Wu, ChunBin Zhou, WeiHao Wang, Nan Li, ChenYu Shao, Hu Wang

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Article in European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

7 authors.

DingZhong ChengFirst Affiliated Hospital of Shantou University Medical College, Shantou, China.
Hui WuFirst Affiliated Hospital of Shantou University Medical College, Shantou, China.
ChunBin ZhouFirst Affiliated Hospital of Shantou University Medical College, Shantou, China.
WeiHao WangFirst Affiliated Hospital of Shantou University Medical College, Shantou, China.
Nan LiFirst Affiliated Hospital of Shantou University Medical College, Shantou, China.
ChenYu ShaoFirst Affiliated Hospital of Shantou University Medical College, Shantou, China.
Hu WangFirst Affiliated Hospital of Shantou University Medical College, Shantou, China. wanghu0754@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo enhance the efficacy of the unilateral biportal endoscopic spinal procedure, the spinal triangle concept is proposed, integrating a digital model to establish standardized reference parameters and technical protocols for biportal positioning and incision delineation.

methodsA retrospective-prospective cohort of 105 patients undergoing UBE for lumbar discectomy and unilateral spinal canal decompression between March 2024 and February 2026 was analyzed. The cohort was stratified into three groups: 35 cases for pilot testing, 35 as controls, and 35 as the experimental group where preoperative incision parameters were calculated using a proprietary algorithmic formula. Surgical procedures were executed and meticulously documented, with subsequent manual data annotation, machine learning-assisted computational analysis, and mathematical modeling to enhance the UBE spinal triangle framework. Surgical outcomes were assessed by attending surgeons via a standardized Likert scale, while patient-reported outcomes (VAS and ODI scores) were systematically recorded at postoperative intervals. Quantitative video-derived metrics underwent rigorous statistical evaluation, including Student's t-tests, multivariate linear regression, logistic regression analysis, and Pearson's chi-square tests.

resultsThe optimal surgical angle was determined to be within the range of 30°-50°. A standardized predictive formula for incision positioning and interportal distance was established: [Formula: see text]. The experimental group utilizing this formula demonstrated a significant reduction in operative duration and enhanced surgical ergonomics under comparable BMI conditions, with particularly pronounced benefits observed in patients with BMI > 28 or BMI < 18.5. Personalized incision placement and optimized interportal distance contributed to decreased operative time, minimized intraoperative radiation exposure, and improved procedural fluency and surgeon satisfaction.

conclusionIn UBE procedures, the existence of an endoscopic triangular working zone was confirmed. The implementation of a standardized predictive formula for incision positioning and interportal distance enhances surgical efficiency and methodological precision.

Indexed as

Lumbar disc herniationMachine learningUnilateral biportal endoscopy

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.