Evidence map›Paper›PMID 40772999›Full record

ReviewLa Radiologia medica2025

Addressing fractures that are hard to diagnose on imaging: Radiomics or deep learning?

Junlin Xu, Xiaobo Wen, Yingchun Shao, Qing Liu, Sha Zhou, Li Jiyixuan, Dan Wang, Ying Yang, Han Li, Linyuan Xue and 3 more

Erratum issuedAbstract readReview
PubMed Publisher
In one paragraph

Review in La Radiologia medica, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. 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

5 · Who and what money

Authors and funding

13 authors.

Junlin Xu *The Affiliated Hospital of Qingdao University, Qingdao Medical College, Qingdao University, Qingdao, 266071, China.
Xiaobo Wen *The Affiliated Hospital of Qingdao University, Qingdao Medical College, Qingdao University, Qingdao, 266071, China.
Yingchun Shao *Department of Pharmacy, Qingdao Municipal Hospital, Qingdao, 266000, China.
Qing LiuThe Affiliated Hospital of Qingdao University, Qingdao Medical College, Qingdao University, Qingdao, 266071, China.
Sha ZhouThe Affiliated Hospital of Qingdao University, Qingdao Medical College, Qingdao University, Qingdao, 266071, China.
Li JiyixuanThe Affiliated Hospital of Qingdao University, Qingdao Medical College, Qingdao University, Qingdao, 266071, China.
Dan WangThe Affiliated Hospital of Qingdao University, Qingdao Medical College, Qingdao University, Qingdao, 266071, China.
Ying YangThe Affiliated Hospital of Qingdao University, Qingdao Medical College, Qingdao University, Qingdao, 266071, China.
Han LiDepartment of Radiotherapy, The Third Affiliated Hospital of Kunming Medical University, Kunming, 650500, China.
Linyuan XueThe Affiliated Hospital of Qingdao University, Qingdao Medical College, Qingdao University, Qingdao, 266071, China.
Kunyue XingInstitute of Health Informatics, University College London, 222 Euston Rd, London, NW1 2DA, UK. kunyue.xing.24@ucl.ac.uk.
Xiaolin WuThe Affiliated Hospital of Qingdao University, Qingdao Medical College, Qingdao University, Qingdao, 266071, China. fyqs01@qdu.edu.cn.
Dongming XingThe Affiliated Hospital of Qingdao University, Qingdao Medical College, Qingdao University, Qingdao, 266071, China. xdm_tsinghua@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Fractures and their complications are recognized as major public health problems. Especially for occult fractures that are difficult to judge radiologically, timely and accurate diagnosis is particularly important for the treatment and prognosis of patients. In recent years, the successful application of radiomics and deep learning in medical diagnosis has shown great potential for providing more timely and accurate diagnostic methods for occult fractures. This review provides an introduction to radiomics and deep learning, summarizes their respective characteristics in detecting occult fractures, and subsequently conducts a detailed analysis on the potential value and future prospects of integrating these two techniques to develop an enhanced approach for prompt and precise detection of occult fractures.

Indexed as

Deep LearningFractures, BoneHumansRadiomicsBoneDeep learningOccult fractureRadiomics

Identifiers

PMID40772999

What Socratic 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 Socratic graph.