Evidence map›Paper›PMID 40481023›Full record

ArticleScientific reports2025

UANV: UNet-based attention network for thoracolumbar vertebral compression fracture angle measurement.

Yurim Lee, Joohyun Kim, Kyu-Chong Lee, Sungjae An, Yongwon Cho, Kyung-Sik Ahn, Junseok W Hur

Abstract read
In one paragraph

Article in Scientific reports, 2025. 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
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Yurim Lee *Department of Brain and Cognitive Engineering, Korea University, Seoul, 02841, Republic of Korea.
Joohyun Kim *Department of Neurosurgery, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea.
Kyu-Chong LeeDepartment of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea.
Sungjae AnDepartment of Neurosurgery, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea.
Yongwon ChoDepartment of Computer Science and Engineering, Soonchunhyang University, Asan, Republic of Korea. dragon1won@sch.ac.kr.
Kyung-Sik AhnDepartment of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea. glassesik@korea.ac.kr.
Junseok W HurDepartment of Neurosurgery, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea. hurjune@gmail.com.

Funding

Ministry of Education of the Republic of Korea, the National Research Foundation of Korea RS-2023-00239603Ministry of Education of the Republic of Korea, the National Research Foundation of Korea RS-2023-00262309Ministry of Health Office for Healthcare Transformation HR22C1302Soonchunhyang University Research Fund 20240556
6 · The paper itself

Abstract

Kyphosis is a prevalent spinal condition where the spine curves in the sagittal plane, resulting in spine deformities. Curvature estimation provides a powerful index to assess the deformation severity of scoliosis. In current clinical diagnosis, the standard curvature estimation method for quantitatively assessing the curvature is performed by measuring the vertebral angle, which is the angle between two lines, drawn perpendicular to the upper and lower endplates of the involved vertebra. However, manual Cobb angle measurement requires considerable time and effort, along with associated problems such as interobserver and intraobserver variations. Hence, in this study, we propose UNet-based Attention Network for Thoracolumbar Vertebral Compression Fracture Angle (UANV), a vertebra angle measuring model using lateral spinal X-ray based on a deep convolutional neural network (CNN). Specifically, we considered the detailed shape of each vertebral body with an attention mechanism and then recorded each edge of each vertebra to calculate vertebrae angles.

Indexed as

Fractures, CompressionLumbar VertebraeNeural Networks, ComputerSpinal FracturesThoracic VertebraeFemaleHumansKyphosisMaleRadiographyCobb angle measurementComputer-aided diagnosisDeep learningScoliosisVertebral angle

Identifiers

PMID40481023
PMCPMC12144114

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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.