Evidence map›Paper›PMID 38469158›Full record

ArticleFrontiers in artificial intelligence2024

OralImmunoAnalyser: a software tool for immunohistochemical assessment of oral leukoplakia using image segmentation and classification models.

Zakaria A Al-Tarawneh, Maite Pena-Cristóbal, Eva Cernadas, José Manuel Suarez-Peñaranda, Manuel Fernández-Delgado, Almoutaz Mbaidin, Mercedes Gallas-Torreira, Pilar Gándara-Vila

Open access · goldAbstract read
In one paragraph

Article in Frontiers in artificial intelligence, 2024. 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
1.6field-weighted citation impact, top 19% 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

1 citing paper in PubMed, 4 citations in OpenAlex.

  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 at 3 institutions in 2 countries.

Zakaria A Al-Tarawneh *Computer Science Department, Mutah University, Karak, Jordan.
Maite Pena-Cristóbal *Oral Medicine, Oral Surgery and Implantology Unit, MedOralRes Group of University of Santiago, Santiago de Compostela, Spain.
Eva CernadasCentro Singular de Investigación en Tecnoloxías Intelixentes da USC, Universidade de Santiago de Compostela (USC), Santiago de Compostela, Spain.
José Manuel Suarez-PeñarandaPathological Anatomy Service, University Hospital Complex of Santiago (CHUS), Santiago de Compostela, Spain.
Manuel Fernández-DelgadoCentro Singular de Investigación en Tecnoloxías Intelixentes da USC, Universidade de Santiago de Compostela (USC), Santiago de Compostela, Spain.
Almoutaz MbaidinComputer Science Department, Mutah University, Karak, Jordan.
Mercedes Gallas-TorreiraOral Medicine, Oral Surgery and Implantology Unit, MedOralRes Group of University of Santiago, Santiago de Compostela, Spain.
Pilar Gándara-VilaOral Medicine, Oral Surgery and Implantology Unit, MedOralRes Group of University of Santiago, Santiago de Compostela, Spain.
Universidade de Santiago de Compostela · ESCenter for Research in Molecular Medicine and Chronic Diseases · ESMutah University · JO

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Oral cancer ranks sixteenth amongst types of cancer by number of deaths. Many oral cancers are developed from potentially malignant disorders such as oral leukoplakia, whose most frequent predictor is the presence of epithelial dysplasia. Immunohistochemical staining using cell proliferation biomarkers such as ki67 is a complementary technique to improve the diagnosis and prognosis of oral leukoplakia. The cell counting of these images was traditionally done manually, which is time-consuming and not very reproducible due to intra- and inter-observer variability. The software presently available is not suitable for this task. This article presents the OralImmunoAnalyser software (registered by the University of Santiago de Compostela-USC), which combines automatic image processing with a friendly graphical user interface that allows investigators to oversee and easily correct the automatically recognized cells before quantification. OralImmunoAnalyser is able to count the number of cells in three staining levels and each epithelial layer. Operating in the daily work of the Odontology Faculty, it registered a sensitivity of 64.4% and specificity of 93% for automatic cell detection, with an accuracy of 79.8% for cell classification. Although expert supervision is needed before quantification, OIA reduces the expert analysis time by 56.5% compared to manual counting, avoiding mistakes because the user can check the cells counted. Hence, the SUS questionnaire reported a mean score of 80.9, which means that the system was perceived from good to excellent. OralImmunoAnalyser is accurate, trustworthy, and easy to use in daily practice in biomedical labs. The software, for Windows and Linux, with the images used in this study, can be downloaded from https://citius.usc.es/transferencia/software/oralimmunoanalyser for research purposes upon acceptance.

Indexed as

image processingimmunohistochemical imagemachine learningoral canceroral potentially malignant disorderssoftware

Identifiers

PMID38469158
PMCPMC10925674
OpenAlexW4392161902

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.