Evidence map›Paper›PMID 37563659›Full record

ArticleBMC oral health2023

Detection of the pathological exposure of pulp using an artificial intelligence tool: a multicentric study over periapical radiographs.

A Altukroni, A Alsaeedi, C Gonzalez-Losada, J H Lee, M Alabudh, M Mirah, S El-Amri, O Ezz El-Deen

Open access · goldAbstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC oral health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 2 pooled it
3.3field-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

8 citing papers in PubMed, 2 syntheses or guidelines pooled it, 13 citations in OpenAlex.

  1. Pooled it
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  5. Review
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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 6 institutions in 5 countries.

A AltukroniMinistry of Health, Medina, Saudi Arabia. research-manager@swc-dent.com.
A AlsaeediDepartment of Computer Science, College of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia.
C Gonzalez-LosadaSchool of Dentistry, Complutense University of Madrid, Madrid, Spain.
J H LeeDepartment of Periodontology, College of Dentistry and Institute of Oral Bioscience, Jeonbuk National University, Jeonju, Korea.
M AlabudhMinistry of Health, Medina, Saudi Arabia.
M MirahDepartment of Dental Materials, Taibah University, Medina, Saudi Arabia.
S El-AmriMinistry of Health, Ankara, Turkey.
O Ezz El-DeenMinistry of Health, Cairo, Egypt.
Ministry of Health · SATaibah University · SAJeonbuk National University · KRMinistry of Health · TRMinistry of Health and Population · EGUniversidad Complutense de Madrid · ES

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIntroducing artificial intelligence (AI) into the medical field proved beneficial in automating tasks and streamlining the practitioners' lives. Hence, this study was conducted to design and evaluate an AI tool called Make Sure Caries Detector and Classifier (MSc) for detecting pathological exposure of pulp on digital periapical radiographs and to compare its performance with dentists.

methodsThis study was a diagnostic, multi-centric study, with 3461 digital periapical radiographs from three countries and seven centers. MSc was built using Yolov5-x model, and it was used for exposed and unexposed pulp detection. The dataset was split into a train, validate, and test dataset; the ratio was 8-1-1 to prevent overfitting. 345 images with 752 labels were randomly allocated to test MSc. The performance metrics used to test MSc performance included mean average precision (mAP), precision, F1 score, recall, and area under receiver operating characteristic curve (AUC). The metrics used to compare the performance with that of 10 certified dentists were: right diagnosis exposed (RDE), right diagnosis not exposed (RDNE), false diagnosis exposed (FDE), false diagnosis not exposed (FDNE), missed diagnosis (MD), and over diagnosis (OD).

resultsMSc achieved a performance of more than 90% in all metrics examined: an average precision of 0.928, recall of 0.918, F1-score of 0.922, and AUC of 0.956 (P<.05). The results showed a higher mean of 1.94 for all right (correct) diagnosis parameters in MSc group, while a higher mean of 0.64 for all wrong diagnosis parameters in the dentists group (P<.05).

conclusionsThe designed MSc tool proved itself reliable in the detection and differentiating between exposed and unexposed pulp in the internally validated model. It also showed a better performance for the detection of exposed and unexposed pulp when compared to the 10 dentists' consensus.

Indexed as

Artificial IntelligenceDental PulpHumansRadiographyROC CurveArtificial IntelligenceCariesDeep LearningPeriapical RadiographPulp ExposureYolov5x

Identifiers

PMID37563659
PMCPMC10416487
OpenAlexW4385725574

What OpenQuestion holds

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LicenceCC BY
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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.