NDLI: Artificial intelligence for radiographic imaging detection of caries lesions: a systematic review

Content Provider	Springer Nature : BioMed Central
Author	Albano, Domenico Galiano, Vanessa Basile, Mariachiara Di Luca, Filippo Gitto, Salvatore Messina, Carmelo Cagetti, Maria Grazia Del Fabbro, Massimo Tartaglia, Gianluca Martino Sconfienza, Luca Maria
Abstract	Background The aim of this systematic review is to evaluate the diagnostic performance of Artificial Intelligence (AI) models designed for the detection of caries lesion (CL). Materials and methods An electronic literature search was conducted on PubMed, Web of Science, SCOPUS, LILACS and Embase databases for retrospective, prospective and cross-sectional studies published until January 2023, using the following keywords: artificial intelligence (AI), machine learning (ML), deep learning (DL), artificial neural networks (ANN), convolutional neural networks (CNN), deep convolutional neural networks (DCNN), radiology, detection, diagnosis and dental caries (DC). The quality assessment was performed using the guidelines of QUADAS-2. Results Twenty articles that met the selection criteria were evaluated. Five studies were performed on periapical radiographs, nine on bitewings, and six on orthopantomography. The number of imaging examinations included ranged from 15 to 2900. Four studies investigated ANN models, fifteen CNN models, and two DCNN models. Twelve were retrospective studies, six cross-sectional and two prospective. The following diagnostic performance was achieved in detecting CL: sensitivity from 0.44 to 0.86, specificity from 0.85 to 0.98, precision from 0.50 to 0.94, PPV (Positive Predictive Value) 0.86, NPV (Negative Predictive Value) 0.95, accuracy from 0.73 to 0.98, area under the curve (AUC) from 0.84 to 0.98, intersection over union of 0.3–0.4 and 0.78, Dice coefficient 0.66 and 0.88, F1-score from 0.64 to 0.92. According to the QUADAS-2 evaluation, most studies exhibited a low risk of bias. Conclusion AI-based models have demonstrated good diagnostic performance, potentially being an important aid in CL detection. Some limitations of these studies are related to the size and heterogeneity of the datasets. Future studies need to rely on comparable, large, and clinically meaningful datasets. Protocol PROSPERO identifier: CRD42023470708
Related Links	https://bmcoralhealth.biomedcentral.com/counter/pdf/10.1186/s12903-024-04046-7.pdf
Ending Page	10
Page Count	10
Starting Page	1
File Format	HTM / HTML
ISSN	14726831
DOI	10.1186/s12903-024-04046-7
Journal	BMC Oral Health
Issue Number	1
Volume Number	24
Language	English
Publisher	BioMed Central
Publisher Date	2024-02-24
Access Restriction	Open
Subject Keyword	Dentistry Oral and Maxillofacial Surgery Artificial intelligence Caries lesion Radiographic imaging Detection Diagnosis
Content Type	Text
Resource Type	Article
Subject	Dentistry
Journal Impact Factor	2.6/2023
5-Year Journal Impact Factor	3.2/2023

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

White spot lesions: diagnosis and treatment – a systematic review

Accuracy of artificial intelligence in caries detection: a systematic review and meta-analysis

Detection and diagnosis of the early caries lesion

Applications of artificial intelligence in the field of oral and maxillofacial pathology: a systematic review and meta-analysis

Diagnostic accuracy of artificial intelligence-assisted caries detection: a clinical evaluation

Dental Caries Diagnosis and Detection Using Neural Networks: A Systematic Review

Clinical SWIR and CP-OCT imaging of interproximal lesions

Segmentation of periapical lesions with automatic deep learning on panoramic radiographs: an artificial intelligence study

Correction to: Accuracy of artificial intelligence-based segmentation in maxillofacial structures: a systematic review

Artificial intelligence for radiographic imaging detection of caries lesions: a systematic review

Similar Documents

White spot lesions: diagnosis and treatment – a systematic review

Accuracy of artificial intelligence in caries detection: a systematic review and meta-analysis

Detection and diagnosis of the early caries lesion

Applications of artificial intelligence in the field of oral and maxillofacial pathology: a systematic review and meta-analysis

Diagnostic accuracy of artificial intelligence-assisted caries detection: a clinical evaluation

Dental Caries Diagnosis and Detection Using Neural Networks: A Systematic Review

Clinical SWIR and CP-OCT imaging of interproximal lesions

Segmentation of periapical lesions with automatic deep learning on panoramic radiographs: an artificial intelligence study

Correction to: Accuracy of artificial intelligence-based segmentation in maxillofacial structures: a systematic review

Artificial intelligence for radiographic imaging detection of caries lesions: a systematic review