Evidence map›Paper›PMID 42368307›Full record

ArticleGlobal health & medicine2026

Automated radiographic shoulder balance assessment in scoliosis

Longhao Yang, Fangzheng Xu, Qingzhi Xiang, Jianwen Fu, Xiao Xia, Fuping Li, Shaobo Cheng, Yifei Qin, Yan Yu

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PubMed Full text at PMC Publisher
In one paragraph

Article in Global health & medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Longhao YangDivision of Spine, Department of Orthopaedics, Tongji Hospital, Tongji University School of Medicine, Tongji University, Shanghai, China.
Fangzheng XuDivision of Spine, Department of Orthopaedics, Tongji Hospital, Tongji University School of Medicine, Tongji University, Shanghai, China.
Qingzhi XiangDivision of Spine, Department of Orthopaedics, Tongji Hospital, Tongji University School of Medicine, Tongji University, Shanghai, China.
Jianwen FuSpinextech Medical Technology Co., Ltd., Shanghai, China.
Xiao XiaDivision of Spine, Department of Orthopaedics, Tongji Hospital, Tongji University School of Medicine, Tongji University, Shanghai, China.
Fuping LiDivision of Spine, Department of Orthopaedics, Tongji Hospital, Tongji University School of Medicine, Tongji University, Shanghai, China.
Shaobo ChengDivision of Spine, Department of Orthopaedics, Tongji Hospital, Tongji University School of Medicine, Tongji University, Shanghai, China.
Yifei QinDivision of Spine, Department of Orthopaedics, Tongji Hospital, Tongji University School of Medicine, Tongji University, Shanghai, China.
Yan YuDivision of Spine, Department of Orthopaedics, Tongji Hospital, Tongji University School of Medicine, Tongji University, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The objective of this study was to develop an automated deep learning-based method for the assessment of shoulder balance in adolescent idiopathic scoliosis (AIS) patients using X-ray images in order to provide a reliable and efficient alternative to manual measurements. A total of 940 AIS radiographs were screened; 937 cases were included in the model-development cohort after quality control and were annotated for precise identification and segmentation of the T1 vertebra, both clavicles, and both coracoids. A deep learning neural network was used to segment these structures. Landmarks were extracted based on morphological image processing, and shoulder balance parameters including the clavicle angle (CA), coracoid height difference (CHD), clavicle tilt angle difference (CTAD), radiological shoulder height (RSH), and T1 tilting angle (T1TA) were calculated. The accuracy of the automated measurements was validated using an external dataset (

Indexed as

adolescent idiopathic scoliosisautomated measurementneural networkX-ray

Identifiers

PMID42368307
PMCPMC13306868

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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.