Evidence map›Paper›PMID 40958876›Full record

ReviewFrontiers in oncology2025

Application of ultrasound in combination with other methods in gynecological disease: artificial intelligence, surgery, and drugs.

Shaohua Shi, Chengcheng Dai, Demin Liu, Xingjie Liu

Abstract readReview
In one paragraph

Review in Frontiers in oncology, 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

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.

  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

4 authors.

Shaohua ShiDepartment of Ultrasound, Qilu Hospital of Shandong University Dezhou Hospital, Dezhou, China.
Chengcheng DaiDepartment of Ultrasound, Qilu Hospital of Shandong University Dezhou Hospital, Dezhou, China.
Demin LiuClinical Training Center, Qilu Hospital of Shandong University Dezhou Hospital, Dezhou, China.
Xingjie LiuClinical Training Center, Qilu Hospital of Shandong University Dezhou Hospital, Dezhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Gynecological diseases (GyD) are related to reproductive tissue disorders such as the cervix, vagina, fallopian tubes, and ovaries, which can affect fertility. Among these diseases, we can mention endometriosis, ovarian laziness, primary ovarian insufficiency, cancers related to these tissues, and even Asherman's disease. Considering the impact of these diseases on the population's youth, it is imperative to develop effective methods for diagnosing, treating, monitoring, and preventing their progression. In the past, ultrasound-based methods have been used for early diagnosis of GyD, including ovarian cancer. However, in today's era, it is essential to enhance the features of this method to ensure that patients are screened more effectively and their treatment responses can be tracked. In recent years, the spread of artificial intelligence has led to its application in various branches of medicine. Many studies have increased their efficiency by combining ultrasound and artificial intelligence methods. Additionally, the simultaneous use of ultrasound and surgery can help improve patient recovery and the success of the procedure. Additionally, various studies have utilized the combination of ultrasound-based methods and different drugs to treat GyD. In this manuscript, we will discuss the pathology of gynecological diseases, the use of ultrasound-based methods, and their combination with other methods.

Indexed as

artificial intelligencediagnosisgynecological diseasetreatmentultrasound

Identifiers

PMID40958876
PMCPMC12433888

What OpenQuestion holds

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Registered trials

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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.