Evidence map›Paper›PMID 35838977›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2022

A Deep Learning Approach to Identify Chest Computed Tomography Features for Prediction of SARS-CoV-2 Infection Outcomes.

Amirhossein Sahebkar, Mitra Abbasifard, Samira Chaibakhsh, Paul C Guest, Mohamad Amin Pourhoseingholi, Amir Vahedian-Azimi, Prashant Kesharwani, Tannaz Jamialahmadi

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Article in Methods in molecular biology (Clifton, N.J.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

8 authors.

Amirhossein SahebkarApplied Biomedical Research Center, Mashhad University of Medical Sciences, Mashhad, Iran. amir_saheb2000@yahoo.com.
Mitra AbbasifardImmunology of Infectious Diseases Research Center, Research Institute of Basic Medical Sciences, Rafsanjan University of Medical Sciences, Rafsanjan, Iran. abbasifardm@yandex.com.
Samira ChaibakhshEye Research Center, The five Senses Institute, Rassoul Akram Hospital, Iran University of Medical Sciences, Tehran, Iran.
Paul C GuestLaboratory of Neuroproteomics, Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil.
Mohamad Amin PourhoseingholiGastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Amir Vahedian-AzimiTrauma Research Center, Nursing Faculty, Baqiyatallah University of Medical Sciences, Tehran, Iran.
Prashant KesharwaniDepartment of Pharmaceutics, School of Pharmaceutical Education and Research, Jamia Hamdard, New Delhi, India.
Tannaz JamialahmadiSurgical Oncology Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

There is still an urgent need to develop effective treatments to help minimize the cases of severe COVID-19. A number of tools have now been developed and applied to address these issues, such as the use of non-contrast chest computed tomography (CT) for evaluation and grading of the associated lung damage. Here we used a deep learning approach for predicting the outcome of 1078 patients admitted into the Baqiyatallah Hospital in Tehran, Iran, suffering from COVID-19 infections in the first wave of the pandemic. These were classified into two groups of non-severe and severe cases according to features on their CT scans with accuracies of approximately 0.90. We suggest that incorporation of molecular and/or clinical features, such as multiplex immunoassay or laboratory findings, will increase accuracy and sensitivity of the model for COVID-19 -related predictions.

Indexed as

COVID-19Deep LearningHumansIranLungRetrospective StudiesSARS-CoV-2Tomography, X-Ray ComputedChest CTComputed tomographyCOVID-19Deep learningDiffuse opacitiesLesion distributionSARS-CoV-2

Identifiers

PMID35838977

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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.