Evidence map›Paper›PMID 39781051›Full record

ArticleBiomedical engineering letters2025

Enhanced diagnosis of pes planus and pes cavus using deep learning-based segmentation of weight-bearing lateral foot radiographs: a comparative observer study.

Seung Min Ryu, Keewon Shin, Soo Wung Shin, Sun Ho Lee, Su Min Seo, Seung Hong Koh, Seung-Ah Ryu, Ki-Hong Kim, Jeong Hwan Ko, Chang Hyun Doh and 2 more

Abstract read
In one paragraph

Article in Biomedical engineering letters, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

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

12 authors.

Seung Min Ryu *Department of Orthopedic Surgery, Seoul Medical Center, Seoul, Republic of Korea.ORCID https://orcid.org/0000-0001-6946-1428
Keewon Shin *Department of Artificial Intelligence Research Center, Korea University College of Medicine, Seoul, Republic of Korea.
Soo Wung Shin *Department of Computer Science and Engineering, Seoul National University, Seoul, Korea.
Sun Ho LeeDepartment of Orthopedic Surgery, Chonnam National University Hospital, Gwangju, Republic of Korea.
Su Min SeoDepartment of Anesthesiology and Pain Medicine, Seoul Medical Center, Seoul, Republic of Korea.
Seung Hong KohDepartment of Anesthesiology and Pain Medicine, Seoul Medical Center, Seoul, Republic of Korea.
Seung-Ah RyuDepartment of Anesthesiology and Pain Medicine, Seoul Medical Center, Seoul, Republic of Korea.
Ki-Hong KimDepartment of Orthopedic Surgery, Seoul Medical Center, Seoul, Republic of Korea.
Jeong Hwan KoDepartment of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
Chang Hyun DohDepartment of Orthopedic Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
Young Rak ChoiDepartment of Orthopedic Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
Namkug KimDepartment of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.ORCID 0000-0002-3438-2217

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A weight-bearing lateral radiograph (WBLR) of the foot is a gold standard for diagnosing adult-acquired flatfoot deformity. However, it is difficult to measure the major axis of bones in WBLR without using auxiliary lines. Herein, we develop semantic segmentation with a deep learning model (DLm) on the WBLR of the foot for enhanced diagnosis of pes planus and pes cavus. We used 300 consecutive WBLRs from young Korean males. The semantic segmentation model was developed based on U Supplementary Information: The online version contains supplementary material available at 10.1007/s13534-024-00439-3.

Indexed as

Artificial intelligenceComputer-assisted diagnosisDeep learningOrthopaedicsSegmentationX-rays

Identifiers

PMID39781051
PMCPMC11704119

What OpenQuestion holds

Textmetadata
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.