Evidence map›Paper›PMID 35919502›Full record

ArticleContrast media & molecular imaging2022

Three-Dimensional Reconstruction of a CT Image under Deep Learning Algorithm to Evaluate the Application of Percutaneous Kyphoplasty in Osteoporotic Thoracolumbar Compression Fractures.

Jiameng Li, Zhong Xiang, Jiaqing Zhou, Meng Zhang

Open access · hybridAbstract read
In one paragraph

Article in Contrast media & molecular imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.5field-weighted citation impact, top 43% of its field
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

4 citing papers in PubMed, 6 citations in OpenAlex.

  1. Review
  2. Article
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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

4 authors at 1 institution in 1 country.

Jiameng LiDepartment of Spine Surgery, The Fourth Hospital of Changsha, Changsha 412002, Hunan, China.ORCID 0000-0002-7169-691X
Zhong XiangDepartment of Spine Surgery, The Fourth Hospital of Changsha, Changsha 412002, Hunan, China.ORCID 0000-0001-8890-7464
Jiaqing ZhouDepartment of Spine Surgery, The Fourth Hospital of Changsha, Changsha 412002, Hunan, China.ORCID 0000-0002-1269-8691
Meng ZhangDepartment of Spine Surgery, The Fourth Hospital of Changsha, Changsha 412002, Hunan, China.ORCID 0000-0002-5125-2342
Fourth Hospital of Changsha · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In order to investigate the therapeutic evaluation of percutaneous kyphoplasty (PKP) for the treatment of osteoporotic thoracolumbar compression fractures by three-dimensional (3D) reconstruction of computed tomography (CT) based on the deep learning V-Net network, the traditional V-Net was optimized first and a new and improved V-Net was proposed. The introduced U-Net, V-Net, and convolutional neural network (CNN) were compared in this study. Then, 106 patients with osteoporotic thoracolumbar compression fractures were enrolled, and 128 centrums were divided into the test group with 53 cases of PKP and the control group with 53 cases of percutaneous vertebroplasty (PVP) according to different surgical protocols. All patients underwent CT scan based on the improved V-Net, and data of centrum measurement indicators, pain score, and therapeutic evaluation results of the modified Macnab were collected. The Dice coefficient of the improved V-Net was observably higher than that of U-Net, V-Net, and CNN, while the Hausdorff distance was lower than that of U-Net, V-Net, and CNN (

Indexed as

Deep LearningFractures, CompressionKyphoplastyKyphosisOsteoporotic FracturesSpinal FracturesVertebroplastyHumansImaging, Three-DimensionalTomography, X-Ray ComputedTreatment Outcome

Identifiers

PMID35919502
PMCPMC9290755
OpenAlexW4289781752

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.