Evidence map›Paper›PMID 36660100›Full record

ArticleIEEE open journal of engineering in medicine and biology2022

Diabetic Foot Ulcer Ischemia and Infection Classification Using EfficientNet Deep Learning Models.

Ziyang Liu, Josvin John, Emmanuel Agu

Open access · goldAbstract read
In one paragraph

Article in IEEE open journal of engineering in medicine and biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 2 pooled it
5.7field-weighted citation impact, top 3% 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

13 citing papers in PubMed, 2 syntheses or guidelines pooled it, 46 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. Article
  6. Review
  7. Article
  8. Review
  9. Article
  10. Review
  11. Article
  12. Article
  13. 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

3 authors at 1 institution in 1 country.

Ziyang LiuComputer Science DepartmentWorcester Polytechnic Institute Worcester MA 01609 USA.ORCID https://orcid.org/0000-0001-5419-1250
Josvin JohnComputer Science DepartmentWorcester Polytechnic Institute Worcester MA 01609 USA.ORCID https://orcid.org/0000-0001-7576-0213
Emmanuel AguComputer Science DepartmentWorcester Polytechnic Institute Worcester MA 01609 USA.ORCID https://orcid.org/0000-0002-3361-4952
Worcester Polytechnic Institute · US

Funding

Smartphone-based wound infection screener and care recommender by combining thermal images and photographs using deep learning methodsR01EB031910 · NIBIB · WORCESTER POLYTECHNIC INSTITUTE · PI AGU, EMMANUEL, GIATSIDIS, GIORGIO · 2022 to 2025
$2.4M
SCH:Smartphone Wound Image Parameter Analysis and Decision Support in Mobile EnvR01EB025801 · NIBIB · WORCESTER POLYTECHNIC INSTITUTE · PI AGU, EMMANUEL · 2018 to 2021
$1.6M
NIBIB NIH HHS R01 EB025801NIBIB NIH HHS R01 EB031910
6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

Deep LearningDiabetic Foot UlcersEfficientNetInfectionIschemia

Identifiers

PMID36660100
PMCPMC9842228
OpenAlexW4312864984

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.