Evidence map›Paper›PMID 36832228›Full record

ArticleDiagnostics (Basel, Switzerland)2023

Differentiation of Benign and Malignant Thyroid Nodules with ANFIS by Using Genetic Algorithm and Proposing a Novel CAD-Based Risk Stratification System of Thyroid Nodules.

Ahmet Cankat Ozturk, Hilal Haznedar, Bulent Haznedar, Seyfettin Ilgan, Osman Erogul, Adem Kalinli

Open access · goldAbstract read
In one paragraph

Article in Diagnostics (Basel, Switzerland), 2023. 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
0.8field-weighted citation impact, top 26% 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

2 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. 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

6 authors at 5 institutions in 2 countries.

Ahmet Cankat OzturkInstitute of Natural Science, Department of Biomedical Engineering, TOBB University of Economics and Technology, 06560 Ankara, Türkiye.ORCID 0000-0002-7082-6479
Hilal HaznedarInstitute of Natural Science, Department of Computer Engineering, Erciyes University, 38280 Kayseri, Türkiye.
Bulent HaznedarDepartment of Computer Engineering, Gaziantep University, 27310 Gaziantep, Türkiye.ORCID 0000-0003-0692-9921
Seyfettin IlganDepartment of Nuclear Medicine, Ankara Guven Hospital, 06540 Ankara, Türkiye.
Osman ErogulInstitute of Natural Science, Department of Biomedical Engineering, TOBB University of Economics and Technology, 06560 Ankara, Türkiye.ORCID 0000-0002-4640-6570
Adem KalinliPresidency Office, Rectorate, Middle East Technical University, 06800 Ankara, Türkiye.
TOBB University of Economics and Technology · TRGaziantep University · TRGüven Hospital · TRInstitute of Natural Science · KPMiddle East Technical University · TR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The thyroid nodule risk stratification guidelines used in the literature are based on certain well-known sonographic features of nodules and are still subjective since the application of these characteristics strictly depends on the reading physician. These guidelines classify nodules according to the sub-features of limited sonographic signs. This study aims to overcome these limitations by examining the relationships of a wide range of ultrasound (US) signs in the differential diagnosis of nodules by using artificial intelligence methods. An innovative method based on training Adaptive-Network Based Fuzzy Inference Systems (ANFIS) by using Genetic Algorithm (GA) is used to differentiate malignant from benign thyroid nodules. The comparison of the results from the proposed method to the results from the commonly used derivative-based algorithms and Deep Neural Network (DNN) methods yielded that the proposed method is more successful in differentiating malignant from benign thyroid nodules. Furthermore, a novel computer aided diagnosis (CAD) based risk stratification system for the thyroid nodule's US classification that is not present in the literature is proposed.

Indexed as

ANFISclassificationdeep neural networkguidelinethyroidthyroid nodule

Identifiers

PMID36832228
PMCPMC9954959
OpenAlexW4320917605

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.