Evidence map›Paper›PMID 38975154›Full record

ArticleHeliyon2024

A non-invasive AI-based system for precise grading of anosmia in COVID-19 using neuroimaging.

Hossam Magdy Balaha, Mayada Elgendy, Ahmed Alksas, Mohamed Shehata, Norah Saleh Alghamdi, Fatma Taher, Mohammed Ghazal, Mahitab Ghoneim, Eslam Hamed Abdou, Fatma Sherif and 6 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

16 authors.

Hossam Magdy BalahaDepartment of Bioengineering, J.B. Speed School of Engineering, University of Louisville, Louisville, KY 40292, USA.
Mayada ElgendyApplied Theoretical Physics Research Group, Physics Department, Faculty of Science, Mansoura University, Mansoura 35516, Egypt.
Ahmed AlksasDepartment of Bioengineering, J.B. Speed School of Engineering, University of Louisville, Louisville, KY 40292, USA.
Mohamed ShehataDepartment of Bioengineering, J.B. Speed School of Engineering, University of Louisville, Louisville, KY 40292, USA.
Norah Saleh AlghamdiDepartment of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia.
Fatma TaherThe College of Technological Innovation, Zayed University, Dubai, 19282, United Arab Emirates.
Mohammed GhazalElectrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates.
Mahitab GhoneimDepartment of Radiology, Faculty of Medicine, Mansoura University, Mansoura 35516, Egypt.
Eslam Hamed AbdouOtolaryngology Department, Faculty of Medicine, Mansoura University, Mansoura 35516, Egypt.
Fatma SherifDepartment of Radiology, Faculty of Medicine, Mansoura University, Mansoura 35516, Egypt.
Ahmed ElgarayhiApplied Theoretical Physics Research Group, Physics Department, Faculty of Science, Mansoura University, Mansoura 35516, Egypt.
Mohammed SallahApplied Theoretical Physics Research Group, Physics Department, Faculty of Science, Mansoura University, Mansoura 35516, Egypt.
Mohamed Abdelbadie SalemOtolaryngology Department, Faculty of Medicine, Mansoura University, Mansoura 35516, Egypt.
Elsharawy KamalOtolaryngology Department, Faculty of Medicine, Mansoura University, Mansoura 35516, Egypt.
Harpal SandhuDepartment of Bioengineering, University of Louisville, Louisville, KY 40292, USA.
Ayman El-BazDepartment of Bioengineering, J.B. Speed School of Engineering, University of Louisville, Louisville, KY 40292, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

COVID-19 (Coronavirus), an acute respiratory disorder, is caused by SARS-CoV-2 (coronavirus severe acute respiratory syndrome). The high prevalence of COVID-19 infection has drawn attention to a frequent illness symptom: olfactory and gustatory dysfunction. The primary purpose of this manuscript is to create a Computer-Assisted Diagnostic (CAD) system to determine whether a COVID-19 patient has normal, mild, or severe anosmia. To achieve this goal, we used fluid-attenuated inversion recovery (FLAIR) Magnetic Resonance Imaging (FLAIR-MRI) and Diffusion Tensor Imaging (DTI) to extract the appearance, morphological, and diffusivity markers from the olfactory nerve. The proposed system begins with the identification of the olfactory nerve, which is performed by a skilled expert or radiologist. It then proceeds to carry out the subsequent primary steps: (i) extract appearance markers (i.e.,

Indexed as

AnosmiaComputer aided design (CAD) diffusion tensor imaging (DTI)COVID-19Features selection (FS)Fluid-attenuated inversion recovery (FLAIR)Spherical harmonics (SH)Texture analysis

Identifiers

PMID38975154
PMCPMC11226840

What OpenQuestion holds

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LicenceCC BY-NC
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.