Efficiency and accuracy of artificial intelligence in the radiographic detection of periodontal bone loss: A systematic review.
Alveolar Bone Loss
Artificial Intelligence
Diagnosis
Periodontitis
Radiography, Dental
Journal
Imaging science in dentistry
ISSN: 2233-7822
Titre abrégé: Imaging Sci Dent
Pays: Korea (South)
ID NLM: 101559249
Informations de publication
Date de publication:
Sep 2023
Sep 2023
Historique:
received:
26
04
2023
revised:
18
06
2023
accepted:
23
06
2023
medline:
6
10
2023
pubmed:
6
10
2023
entrez:
6
10
2023
Statut:
ppublish
Résumé
Artificial intelligence (AI) is poised to play a major role in medical diagnostics. Periodontal disease is one of the most common oral diseases. The early diagnosis of periodontal disease is essential for effective treatment and a favorable prognosis. This study aimed to assess the effectiveness of AI in diagnosing periodontal bone loss through radiographic analysis. A literature search involving 5 databases (PubMed, ScienceDirect, Scopus, Health and Medical Collection, Dentistry and Oral Sciences) was carried out. A specific combination of keywords was used to obtain the articles. The PRISMA guidelines were used to filter eligible articles. The study design, sample size, type of AI software, and the results of each eligible study were analyzed. The CASP diagnostic study checklist was used to evaluate the evidence strength score. Seven articles were eligible for review according to the PRISMA guidelines. Out of the 7 eligible studies, 4 had strong CASP evidence strength scores (7-8/9). The remaining studies had intermediate CASP evidence strength scores (3.5-6.5/9). The highest area under the curve among the reported studies was 94%, the highest F1 score was 91%, and the highest specificity and sensitivity were 98.1% and 94%, respectively. AI-based detection of periodontal bone loss using radiographs is an efficient method. However, more clinical studies need to be conducted before this method is introduced into routine dental practice.
Identifiants
pubmed: 37799746
doi: 10.5624/isd.20230092
pmc: PMC10548158
doi:
Banques de données
figshare
['10.6084/m9.figshare.19586008']
Types de publication
Journal Article
Langues
eng
Pagination
193-198Informations de copyright
Copyright © 2023 by Korean Academy of Oral and Maxillofacial Radiology.
Déclaration de conflit d'intérêts
Conflicts of Interest: None.
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