Author:
Li Xin,Zhao Dan,Xie Jinxuan,Wen Hao,Liu Chunhua,Li Yajie,Li Wenbin,Wang Songlin
Abstract
Abstract
Background
The development of deep learning (DL) algorithms for use in dentistry is an emerging trend. Periodontitis is one of the most prevalent oral diseases, which has a notable impact on the life quality of patients. Therefore, it is crucial to classify periodontitis accurately and efficiently. This systematic review aimed to identify the application of DL for the classification of periodontitis and assess the accuracy of this approach.
Methods
A literature search up to November 2023 was implemented through EMBASE, PubMed, Web of Science, Scopus, and Google Scholar databases. Inclusion and exclusion criteria were used to screen eligible studies, and the quality of the studies was evaluated by the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology with the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies) tool. Random-effects inverse-variance model was used to perform the meta-analysis of a diagnostic test, with which pooled sensitivity, specificity, positive likelihood ratio (LR), negative LR, and diagnostic odds ratio (DOR) were calculated, and a summary receiver operating characteristic (SROC) plot was constructed.
Results
Thirteen studies were included in the meta-analysis. After excluding an outlier, the pooled sensitivity, specificity, positive LR, negative LR and DOR were 0.88 (95%CI 0.82–0.92), 0.82 (95%CI 0.72–0.89), 4.9 (95%CI 3.2–7.5), 0.15 (95%CI 0.10–0.22) and 33 (95%CI 19–59), respectively. The area under the SROC was 0.92 (95%CI 0.89–0.94).
Conclusions
The accuracy of DL-based classification of periodontitis is high, and this approach could be employed in the future to reduce the workload of dental professionals and enhance the consistency of classification.
Funder
the Beijing Stomatological Hospital of Capital Medical University Young Scientist Program
the Beijing Municipal Government grant
the Beijing Municipal Science and Technology Commission
the Beijing Municipal Education Commission
the Innovation Research Team Project of Beijing Stomatological Hospital, Capital Medical University
the Chinese Research Unit of Tooth Development and Regeneration, Academy of Medical Sciences
the National Natural Science Foundation of China
the Beijing Advanced Innovation Center for Big Data-based Precision Medicine
the Beijing Municipal Government
the Beijing Municipal Colleges and Universities High Level Talents Introduction and Cultivate Project-Beijing Great Wall Scholar Program
the National Key Research and development Program
Publisher
Springer Science and Business Media LLC