Evidence map›Paper›PMID 38495082›Full record

ReviewFrontiers in oncology2023

A narrative review on the application of artificial intelligence in renal ultrasound.

Tong Xu, Xian-Ya Zhang, Na Yang, Fan Jiang, Gong-Quan Chen, Xiao-Fang Pan, Yue-Xiang Peng, Xin-Wu Cui

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
4.6field-weighted citation impact, top 5% of its field
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

9 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Review
  4. Article
  5. Article
  6. Review
  7. Review
  8. Artificial Intelligence to Diagnose Complications of Diabetes.Journal of diabetes science and technology · 2025
    Review
  9. 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

8 authors at 6 institutions in 1 country.

Tong XuDepartment of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Xian-Ya ZhangDepartment of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Na YangDepartment of Ultrasound, Affiliated Hospital of Jilin Medical College, Jilin, China.
Fan JiangDepartment of Medical Ultrasound, The Second Hospital of Anhui Medical University, Hefei, China.
Gong-Quan ChenDepartment of Medical Ultrasound, Minda Hospital of Hubei Minzu University, Enshi, China.
Xiao-Fang PanHealth Medical Department, Dalian Municipal Central Hospital, Dalian, China.
Yue-Xiang PengDepartment of Ultrasound, Wuhan Third Hospital, Tongren Hospital of Wuhan University, Wuhan, China.
Xin-Wu CuiDepartment of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Tongji Hospital · CNAnhui Medical University · CNDalian Municipal Central Hospital · CNJilin Medical University · CNMinda Hospital · CNTongren Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Kidney disease is a serious public health problem and various kidney diseases could progress to end-stage renal disease. The many complications of end-stage renal disease. have a significant impact on the physical and mental health of patients. Ultrasound can be the test of choice for evaluating the kidney and perirenal tissue as it is real-time, available and non-radioactive. To overcome substantial interobserver variability in renal ultrasound interpretation, artificial intelligence (AI) has the potential to be a new method to help radiologists make clinical decisions. This review introduces the applications of AI in renal ultrasound, including automatic segmentation of the kidney, measurement of the renal volume, prediction of the kidney function, diagnosis of the kidney diseases. The advantages and disadvantages of the applications will also be presented clinicians to conduct research. Additionally, the challenges and future perspectives of AI are discussed.

Indexed as

artificial intelligencedeep learningkidneymachine learningultrasound

Identifiers

PMID38495082
PMCPMC10943690
OpenAlexW4392352743

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.