Evidence map›Paper›PMID 40691597›Full record

ReviewJournal of orthopaedic surgery and research2025

Comprehensive workflow with optical navigation in minimally invasive transforaminal lumbar interbody fusion: a retrospective study.

Kangheng Niu, Zhenzhen Tao, Le Cheng, Zhong Wei, Hui Kang, Tanjun Wei, Biwang Huang, Feng Xu, Chengjie Xiong

Abstract readReview
In one paragraph

Review in Journal of orthopaedic surgery and research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. X2P-Net: Context-Aware 2D/3D Vertebra Localization.Bioengineering (Basel, Switzerland) · 2026
    Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Kangheng Niu *Department of Orthopedics, General Hospital of Central Theater Command of the PLA of China, Wuhan, 430070, Hubei, China.
Zhenzhen Tao *Department of Orthopedics, General Hospital of Central Theater Command of the PLA of China, Wuhan, 430070, Hubei, China.
Le ChengDepartment of Orthopedics, General Hospital of Central Theater Command of the PLA of China, Wuhan, 430070, Hubei, China.
Zhong WeiDepartment of Orthopedics, General Hospital of Central Theater Command of the PLA of China, Wuhan, 430070, Hubei, China.
Hui KangDepartment of Orthopedics, General Hospital of Central Theater Command of the PLA of China, Wuhan, 430070, Hubei, China.
Tanjun WeiDepartment of Orthopedics, General Hospital of Central Theater Command of the PLA of China, Wuhan, 430070, Hubei, China.
Biwang HuangDepartment of Orthopedics, General Hospital of Central Theater Command of the PLA of China, Wuhan, 430070, Hubei, China. biwanghuang@163.com.
Feng XuDepartment of Orthopedics, General Hospital of Central Theater Command of the PLA of China, Wuhan, 430070, Hubei, China. fengxu1969@163.com.
Chengjie XiongDepartment of Orthopedics, General Hospital of Central Theater Command of the PLA of China, Wuhan, 430070, Hubei, China. cjxiong1110@gmail.com.

Funding

Wuhan Knowledge Innovation Special Project 2023020201010177Yuying Plan Project of the General Hospital of Central Theater Command of the People's Liberation Army of China ZZYZD202103
6 · The paper itself

Abstract

backgroundDegenerative lumbar diseases (LDD) are increasingly common among the elderly due to extended life expectancy. Minimally invasive transforaminal lumbar interbody fusion (MIS-TLIF) is a preferred surgical technique for LDD, offering advantages over traditional methods, although it presents technical challenges. In this study, we compared the clinical outcomes and radiological accuracy between optical navigation-assisted MIS-TLIF (OMIS-TLIF) and conventional C-arm navigation-assisted MIS-TLIF (CMIS-TLIF) for the treatment of LDD.

methodsA retrospective analysis was conducted on 90 patients who underwent single-level lumbar fusion between December 2021 and April 2023. Patients were divided into two groups: 36 underwent OMIS-TLIF, and 54 underwent CMIS-TLIF. The surgical duration, blood loss, hospital stay, complication rates, and clinical outcomes were assessed. Radiological parameters, such as pedicle screw placement accuracy and facet joint violation (FJV) rates, were also evaluated. The learning curves for surgical time were analyzed using the Cumulative Sum Control Chart method.

resultsOMIS-TLIF demonstrated a significantly shorter total operation time (125.25 ± 19.51 min) compared with CMIS-TLIF (136.41 ± 20.39 min, P < 0.05). OMIS-TLIF also achieved higher accuracy in pedicle screw placement (99.31% vs. 96.76%, P < 0.05) and lower FJV rates (91.67% vs. 81.48%, P < 0.05). Significantly lower pain scores (VAS) and improved functional outcomes (ODI) was observed at the 1-week follow-up (P < 0.05), although no significant differences were noted at the 3-month and latest follow-ups. The learning curve for OMIS-TLIF showed proficiency after 14 cases, whereas CMIS-TLIF required 27 cases to achieve similar proficiency.

conclusionThe optical navigation system enhances the precision and efficiency of MIS-TLIF. OMIS-TLIF demonstrated superior clinical outcomes compared with CMIS-TLIF, with improved accuracy of instrument placement, a shorter surgery times and learning curve. These findings suggest that OMIS-TLIF offers significant advantages for the treatment of LDD.

Indexed as

Intervertebral Disc DegenerationLumbar VertebraeMinimally Invasive Surgical ProceduresSpinal FusionSurgery, Computer-AssistedWorkflowAgedFemaleHumansLearning CurveMaleMiddle AgedOperative TimeRetrospective StudiesTreatment OutcomeComprehensive workflow navigationMIS-TLIFOptical navigationSurgical accuracy and efficiency

Identifiers

PMID40691597
PMCPMC12281698

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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 OpenQuestion graph.