Evidence map›Paper›PMID 27454518›Full record

ArticlePloS one2016

A Low-Cost iPhone-Assisted Augmented Reality Solution for the Localization of Intracranial Lesions.

YuanZheng Hou, LiChao Ma, RuYuan Zhu, XiaoLei Chen, Jun Zhang

Abstract read
In one paragraph

Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 1 pooled it
–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

23 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  13. A Tool-free Neuronavigation Method based on Single-view Hand Tracking.Computer methods in biomechanics and biomedical engineering. Imaging & visualization · 2023
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  16. Review
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  18. MARIN: an open-source mobile augmented reality interactive neuronavigation system.International journal of computer assisted radiology and surgery · 2020
    Article
  19. Implementation of augmented reality support in spine surgery.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 · 2019
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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

5 authors.

YuanZheng HouDepartment of Neurosurgery, PLA General Hospital Hainan Branch, Sanya, Hainan, China.
LiChao MaDepartment of Geriatric Endocrinology, PLA General Hospital, Beijing, China.
RuYuan ZhuDepartment of Neurosurgery, PLA General Hospital, Beijng, China.
XiaoLei ChenDepartment of Neurosurgery, PLA General Hospital, Beijng, China.
Jun ZhangDepartment of Neurosurgery, PLA General Hospital, Beijng, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPrecise location of intracranial lesions before surgery is important, but occasionally difficult. Modern navigation systems are very helpful, but expensive. A low-cost solution that could locate brain lesions and their surface projections in augmented reality would be beneficial. We used an iPhone to partially achieve this goal, and evaluated its accuracy and feasibility in a clinical neurosurgery setting. METHODOLOGY/PRINCIPAL

findingsWe located brain lesions in 35 patients, and using an iPhone, we depicted the lesion's surface projection onto the skin of the head. To assess the accuracy of this method, we pasted computed tomography (CT) markers surrounding the depicted lesion boundaries on the skin onto 15 patients. CT scans were then performed with or without contrast enhancement. The deviations (D) between the CT markers and the actual lesion boundaries were measured. We found that 97.7% of the markers displayed a high accuracy level (D ≤ 5mm). In the remaining 20 patients, we compared our iPhone-based method with a frameless neuronavigation system. Four check points were chosen on the skin surrounding the depicted lesion boundaries, to assess the deviations between the two methods. The integrated offset was calculated according to the deviations at the four check points. We found that for the supratentorial lesions, the medial offset between these two methods was 2.90 mm and the maximum offset was 4.2 mm. CONCLUSIONS/SIGNIFICANCE: This low-cost, image-based, iPhone-assisted, augmented reality solution is technically feasible, and helpful for the localization of some intracranial lesions, especially shallow supratentorial intracranial lesions of moderate size.

Indexed as

SmartphoneAdolescentAdultAgedAlgorithmsBrainChildChild, PreschoolFemaleHumansImage Processing, Computer-AssistedMagnetic Resonance ImagingMaleMiddle AgedModels, StatisticalNeuronavigation

Identifiers

PMID27454518
PMCPMC4959690

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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.