Evidence map›Paper›PMID 35371289›Full record

ArticleComputational and mathematical methods in medicine2022

Fully Convolutional Neural Network Deep Learning Model Fully in Patients with Type 2 Diabetes Complicated with Peripheral Neuropathy by High-Frequency Ultrasound Image.

Xiaoqiang Liu, Hongyan Zhou, Zhaoyun Wang, Xiaoli Liu, Xin Li, Chen Nie, Yang Li

RetractedOpen access · hybridAbstract readRetracted Publication
In one paragraph

Article in Computational and mathematical methods in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.1field-weighted citation impact, top 22% 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

3 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors at 1 institution in 1 country.

Xiaoqiang LiuDepartment of Ultrasound, The Second Hospital of Dalian Medical University, Dalian City, 116027 Liaoning Province, China.ORCID https://orcid.org/0000-0003-4601-8057
Hongyan ZhouDepartment of Ultrasound, The Second Hospital of Dalian Medical University, Dalian City, 116027 Liaoning Province, China.ORCID https://orcid.org/0000-0002-3320-0346
Zhaoyun WangDepartment of Wound Repair, The Second Hospital of Dalian Medical University, Dalian City, 116027 Liaoning Province, China.ORCID https://orcid.org/0000-0001-6039-4054
Xiaoli LiuDepartment of Respiratory, The Second Hospital of Dalian Medical University, Dalian City, 116027 Liaoning Province, China.ORCID https://orcid.org/0000-0002-2278-776X
Xin LiDepartment of Radiology, The Second Hospital of Dalian Medical University, Dalian City, 116027 Liaoning Province, China.ORCID https://orcid.org/0000-0002-5142-7026
Chen NieDepartment of Neurology, The Second Hospital of Dalian Medical University, Dalian City, 116027 Liaoning Province, China.ORCID https://orcid.org/0000-0002-2553-7707
Yang LiDepartment of Ultrasound, The Second Hospital of Dalian Medical University, Dalian City, 116027 Liaoning Province, China.ORCID https://orcid.org/0000-0002-3670-5630
Dalian Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study was aimed at exploring the diagnostic value of high-frequency ultrasound imaging based on a fully convolutional neural network (FCN) for peripheral neuropathy in patients with type 2 diabetes (T2D). A total of 70 patients with T2D mellitus were selected and divided into a lesion group (

Indexed as

Deep LearningDiabetes Mellitus, Type 2Peripheral Nervous System DiseasesHumansNeural Networks, ComputerUltrasonography

Identifiers

PMID35371289
PMCPMC8970954
OpenAlexW4220894176

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.