Evidence map›Paper›PMID 35893314›Full record

ArticleJournal of personalized medicine2022

Ensemble Deep-Learning-Based Prognostic and Prediction for Recurrence of Sporadic Odontogenic Keratocysts on Hematoxylin and Eosin Stained Pathological Images of Incisional Biopsies.

Roopa S Rao, Divya Biligere Shivanna, Surendra Lakshminarayana, Kirti Shankar Mahadevpur, Yaser Ali Alhazmi, Mohammed Mousa H Bakri, Hazar S Alharbi, Khalid J Alzahrani, Khalaf F Alsharif, Hamsa Jameel Banjer and 4 more

Erratum issuedOpen access · goldAbstract read
In one paragraph

Article in Journal of personalized medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
3.2field-weighted citation impact, top 8% 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

11 citing papers in PubMed, 19 citations in OpenAlex.

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  7. Intelligent biofilm detection with ensemble of deep learning networks.Medicina oral, patologia oral y cirugia bucal · 2025
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

14 authors at 6 institutions in 3 countries.

Roopa S RaoDepartment of Oral Pathology and Microbiology, Faculty of Dental Sciences, Ramaiah University of Applied Sciences, Bengaluru 560054, India.
Divya Biligere ShivannaDepartment of Computer Science and Engineering, Faculty of Engineering and Technology, Ramaiah University of Applied Sciences, Bengaluru 560054, India.
Surendra LakshminarayanaDepartment of Oral Pathology and Microbiology, Faculty of Dental Sciences, Ramaiah University of Applied Sciences, Bengaluru 560054, India.
Kirti Shankar MahadevpurDepartment of Computer Science and Engineering, Faculty of Engineering and Technology, Ramaiah University of Applied Sciences, Bengaluru 560054, India.
Yaser Ali AlhazmiDivision of Oral and Maxillofacial Surgery, Department of Oral and Maxillofacial Surgery and Diagnostic Sciences, College of Dentistry, Jazan University, Jazan 45412, Saudi Arabia.
Mohammed Mousa H BakriDivision of Oral and Maxillofacial Surgery, Department of Oral and Maxillofacial Surgery and Diagnostic Sciences, College of Dentistry, Jazan University, Jazan 45412, Saudi Arabia.ORCID 0000-0002-5775-2938
Hazar S AlharbiDepartment of Basic Dental Science, College of Dentistry, Princess Nourah Bint Abdulrahman University, Riyadh 11564, Saudi Arabia.
Khalid J AlzahraniDepartment of Clinical Laboratories Sciences, College of Applied Medical Sciences, Taif University, 11099, Taif 21944, Saudi Arabia.ORCID 0000-0002-6688-0106
Khalaf F AlsharifDepartment of Clinical Laboratories Sciences, College of Applied Medical Sciences, Taif University, 11099, Taif 21944, Saudi Arabia.ORCID 0000-0002-8156-2069
Hamsa Jameel BanjerDepartment of Clinical Laboratories Sciences, College of Applied Medical Sciences, Taif University, 11099, Taif 21944, Saudi Arabia.
Mrim M AlnfiaiDepartment of Information Technology, College of Computers and Information Technology, Taif University, 11099, Taif 21944, Saudi Arabia.
Rodolfo RedaDepartment of Oral and Maxillo Facial Sciences, Sapienza University of Rome, 00161 Rome, Italy.ORCID 0000-0003-1532-6524
Shankargouda PatilDepartment of Maxillofacial Surgery and Diagnostic Sciences, Division of Oral Pathology, College of Dentistry, Jazan University, Jazan 45412, Saudi Arabia.ORCID 0000-0001-7246-5497
Luca TestarelliDepartment of Oral and Maxillo Facial Sciences, Sapienza University of Rome, 00161 Rome, Italy.ORCID 0000-0003-3904-3000
M S Ramaiah University of Applied Sciences · INTaif University · SAJazan University · SASapienza University of Rome · ITPrincess Nourah bint Abdulrahman University · SASaveetha University · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

(1) Background: Odontogenic keratocysts (OKCs) are enigmatic developmental cysts that deserve special attention due to their heterogeneous appearance in histopathological characteristics and high recurrence rate. Despite several nomenclatures for classification, clinicians still confront challenges in its diagnosis and predicting its recurrence. This paper proposes an ensemble deep-learning-based prognostic and prediction algorithm, for the recurrence of sporadic odontogenic keratocysts, on hematoxylin and eosin stained pathological images of incisional biopsies before treatment. (2) Materials and Methods: In this study, we applied a deep-learning algorithm to an ensemble approach integrated with DenseNet-121, Inception-V3, and Inception-Resnet-V3 classifiers. Around 1660 hematoxylin and eosin stained pathologically annotated digital images of OKC-diagnosed (60) patients were supplied to train and predict recurrent OKCs. (3) Results: The presence of SEH (

Indexed as

deep learningmachine learningmicroscopyodontogenic keratocystsoralpathologyprognosissporadic form

Identifiers

PMID35893314
PMCPMC9332803
OpenAlexW4288034278

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.