Evidence map›Paper›PMID 36097768›Full record

ArticleZhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences2022

Artificial intelligence diagnosis based on breast ultrasound imaging.

Zihan Lan, Yulan Peng

Abstract read
In one paragraph

Article in Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. [Deep learning models based on fused ultrasound images for the assessment of cystocele in women].Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2026
    Article
  2. Review
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

2 authors.

Zihan LanDepartment of Ultrasound, West China Hospital, Sichuan University, Chengdu 610000, China. 401386443@qq.com.
Yulan PengDepartment of Ultrasound, West China Hospital, Sichuan University, Chengdu 610000, China. yulanpeng@163.com.

Funding

the Chengdu Science and Technology Project 2017-CY02-00027-GX
6 · The paper itself

Abstract

Breast cancer has now become the leading cancer in women. The development of breast ultrasound artificial intelligence (AI) diagnostic technology is conducive to promoting the precise diagnosis and treatment of breast cancer and alleviating the heavy medical burden due to the unbalanced regional development in China. In recent years, on the basis of improving diagnostic efficiency, AI technology has been continuously combined with various clinical application scenarios, thereby providing more comprehensive and reliable evidence-based suggestions for clinical decision-making. Although AI diagnostic technologies based on conventional breast ultrasound gray-scale images and cutting-edge technologies such as three-dimensional (3D) imaging and elastography have been developed to some extent, there are still technical pain points, diffusion difficulties and ethical dilemmas in the development of AI diagnostic technologies for breast ultrasound.

Indexed as

Breast NeoplasmsElasticity Imaging TechniquesArtificial IntelligenceBreastFemaleHumansUltrasonography, Mammaryartificial intelligencebreast ultrasonographycomputer assistantmedical image fusionmultimodal imaging

Identifiers

PMID36097768
PMCPMC10950100

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.