Evidence map›Paper›PMID 36573241›Full record

ReviewPeerJ2022

Future stem cell analysis: progress and challenges towards state-of-the art approaches in automated cells analysis.

Nurul Syahira Mohamad Zamani, Wan Mimi Diyana Wan Zaki, Zariyantey Abd Hamid, Aqilah Baseri Huddin

Abstract readReview
In one paragraph

Review in PeerJ, 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. 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

4 authors.

Nurul Syahira Mohamad ZamaniFaculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Department of Electrical, Electronic and Systems Engineering, UKM Bangi, Selangor, Malaysia.
Wan Mimi Diyana Wan ZakiFaculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Department of Electrical, Electronic and Systems Engineering, UKM Bangi, Selangor, Malaysia.ORCID 0000-0001-5808-4348
Zariyantey Abd HamidFaculty of Health Sciences, Universiti Kebangsaan Malaysia, Biomedical Science Programme and Centre for Diagnostic, Therapeutic and Investigative Science, Kuala Lumpur, W. P. Kuala Lumpur, Malaysia.
Aqilah Baseri HuddinFaculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Department of Electrical, Electronic and Systems Engineering, UKM Bangi, Selangor, Malaysia.ORCID 0000-0002-7047-1612

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and Aims: A microscopic image has been used in cell analysis for cell type identification and classification, cell counting and cell size measurement. Most previous research works are tedious, including detailed understanding and time-consuming. The scientists and researchers are seeking modern and automatic cell analysis approaches in line with the current in-demand technology. Objectives: This article provides a brief overview of a general cell and specific stem cell analysis approaches from the history of cell discovery up to the state-of-the-art approaches. Methodology: A content description of the literature study has been surveyed from specific manuscript databases using three review methods: manuscript identification, screening, and inclusion. This review methodology is based on Prism guidelines in searching for originality and novelty in studies concerning cell analysis. Results: By analysing generic cell and specific stem cell analysis approaches, current technology offers tremendous potential in assisting medical experts in performing cell analysis using a method that is less laborious, cost-effective, and reduces error rates. Conclusion: This review uncovers potential research gaps concerning generic cell and specific stem cell analysis. Thus, it could be a reference for developing automated cells analysis approaches using current technology such as artificial intelligence and deep learning.

Indexed as

Artificial IntelligenceArtificial intelligenceImage processingMachine learningMicroscopic images

Identifiers

PMID36573241
PMCPMC9789697

What Socratic 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 Socratic graph.