Evidence map›Paper›PMID 38541680›Full record

ArticleLife (Basel, Switzerland)2024

Deep Learning Super-Resolution Technique Based on Magnetic Resonance Imaging for Application of Image-Guided Diagnosis and Surgery of Trigeminal Neuralgia.

Jun Ho Hwang, Chang Kyu Park, Seok Bin Kang, Man Kyu Choi, Won Hee Lee

Open access · goldAbstract read
In one paragraph

Article in Life (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
1.2field-weighted citation impact, top 23% 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

1 citing paper in PubMed, 3 citations in OpenAlex.

  1. 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

5 authors at 4 institutions in 1 country.

Jun Ho HwangDepartment of Neurosurgery, Kyung Hee University Medical Center, Seoul 02447, Republic of Korea.ORCID 0000-0002-5075-376X
Chang Kyu ParkDepartment of Neurosurgery, Kyung Hee University Medical Center, Seoul 02447, Republic of Korea.
Seok Bin KangDepartment of Urology, National Police Hospital, Seoul 05715, Republic of Korea.
Man Kyu ChoiDepartment of Neurosurgery, Kyung Hee University Medical Center, Seoul 02447, Republic of Korea.
Won Hee LeeDepartment of Neurosurgery, School of Medicine, Inje University Busan Paik Hospital, 75 Bokji-ro, Busanjin-gu, Busan 47392, Republic of Korea.ORCID 0000-0002-3112-5354
Kyung Hee University · KRInje University Busan Paik Hospital · KRKyung Hee University Medical Center · KRNational Police Hospital · KR

Funding

Kyung Hee University 20220788
6 · The paper itself

Abstract

This study aimed to implement a deep learning-based super-resolution (SR) technique that can assist in the diagnosis and surgery of trigeminal neuralgia (TN) using magnetic resonance imaging (MRI). Experimental methods applied SR to MRI data examined using five techniques, including T2-weighted imaging (T2WI), T1-weighted imaging (T1WI), contrast-enhancement T1WI (CE-T1WI), T2WI turbo spin-echo series volume isotropic turbo spin-echo acquisition (VISTA), and proton density (PD), in patients diagnosed with TN. The image quality was evaluated using the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). High-quality reconstructed MRI images were assessed using the Leksell coordinate system in gamma knife radiosurgery (GKRS). The results showed that the PSNR and SSIM values achieved by SR were higher than those obtained by image postprocessing techniques, and the coordinates of the images reconstructed in the gamma plan showed no differences from those of the original images. Consequently, SR demonstrated remarkable effects in improving the image quality without discrepancies in the coordinate system, confirming its potential as a useful tool for the diagnosis and surgery of TN.

Indexed as

artificial intelligence (AI)deep learningmagnetic resonance imaging (MRI)super resolution (SR)trigeminal neuralgia (TN)

Identifiers

PMID38541680
PMCPMC10971250
OpenAlexW4392550055

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.