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Performance of artificial intelligence using oral and maxillofacial CBCT images: A systematic review and meta-analysis



Background:

Artificial intelligence (AI) has the potential to enhance health care efficiency and diagnostic accuracy.


Aim:

The present study aimed to determine the current performance of AI using cone-beam computed tomography (CBCT) images for detection and segmentation.


Materials and methods:

A systematic search for scholarly articles written in English was conducted on June 24, 2021, in PubMed, Web of Science, and Google Scholar. Inclusion criteria were peer-reviewed articles that evaluated AI systems using CBCT images for detection and segmentation purposes and achieved reported outcomes in terms of precision and recall, accuracy, based on DICE index and Dice similarity coefficient (DSC). The Cochrane tool for assessing the risk of bias was used to evaluate the studies that were included in this meta-analysis. A random-effects model was used to calculate the pooled effect size.


Results:

Thirteen studies were included for review and analysis. The pooled performance that measures the included AI models is 0.85 (95%CI: 0.73,0.92) for DICE index/DSC, 0.88 (0.77,0.94) for precision, 0.93 (0.84, 0.97) for recall, and 0.83 (0.68, 0.91) for accuracy percentage.


Conclusion:

Some limitations are identified in our meta-analysis such as heterogenicity of studies, risk of bias and lack of ground truth. The application of AI for detection and segmentation using CBCT images is comparable to services offered by trained dentists and can potentially expedite and enhance the interpretive process. Implementing AI into clinical dentistry can analyze a large number of CBCT studies and flag the ones with significant findings, thus increasing efficiency. The study protocol was registered in PROSPERO, the international registry for systematic reviews (ID number CRD42021285095).


Keywords:

Artificial intelligence; cone-beam computed tomography; dentistry; machine learning.



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