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. 2023 Jun;53(2):101-108.
doi: 10.5624/isd.20220224. Epub 2023 Mar 24.

Diagnostic performance of artificial intelligence using cone-beam computed tomography imaging of the oral and maxillofacial region: A scoping review and meta-analysis

Farida Abesi  1 , Mahla Maleki  2 , Mohammad Zamani  2
Affiliations

Diagnostic performance of artificial intelligence using cone-beam computed tomography imaging of the oral and maxillofacial region: A scoping review and meta-analysis

Farida Abesi et al. Imaging Sci Dent. 2023 Jun.

Abstract

Purpose: The aim of this study was to conduct a scoping review and meta-analysis to provide overall estimates of the recall and precision of artificial intelligence for detection and segmentation using oral and maxillofacial cone-beam computed tomography (CBCT) scans.

Materials and methods: A literature search was done in Embase, PubMed, and Scopus through October 31, 2022 to identify studies that reported the recall and precision values of artificial intelligence systems using oral and maxillofacial CBCT images for the automatic detection or segmentation of anatomical landmarks or pathological lesions. Recall (sensitivity) indicates the percentage of certain structures that are correctly detected. Precision (positive predictive value) indicates the percentage of accurately identified structures out of all detected structures. The performance values were extracted and pooled, and the estimates were presented with 95% confidence intervals (CIs).

Results: In total, 12 eligible studies were finally included. The overall pooled recall for artificial intelligence was 0.91 (95% CI: 0.87-0.94). In a subgroup analysis, the pooled recall was 0.88 (95% CI: 0.77-0.94) for detection and 0.92 (95% CI: 0.87-0.96) for segmentation. The overall pooled precision for artificial intelligence was 0.93 (95% CI: 0.88-0.95). A subgroup analysis showed that the pooled precision value was 0.90 (95% CI: 0.77-0.96) for detection and 0.94 (95% CI: 0.89-0.97) for segmentation.

Conclusion: Excellent performance was found for artificial intelligence using oral and maxillofacial CBCT images.

Keywords: Artificial Intelligence; Cone-Beam Computed Tomography; Dentistry; Predictive Value of Tests.

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Conflict of interest statement

Conflicts of Interest: None

Figures

Fig. 1
Fig. 1. Risk of bias assessment of the included studies according to the Prediction Model Risk of Bias Assessment Tool (PROBAST).
Fig. 2
Fig. 2. PRISMA flow diagram.
Fig. 3
Fig. 3. Recall values of artificial intelligence using oral and maxillofacial cone-beam computed tomography imaging.
Fig. 4
Fig. 4. Funnel plot assessing publication bias across studies assessing recall values of artificial intelligence using oral and maxillofacial cone-beam computed tomography imaging.
Fig. 5
Fig. 5. Recall values of artificial intelligence systems for detection and segmentation using oral and maxillofacial cone-beam computed tomography scans.
Fig. 6
Fig. 6. Precision values of artificial intelligence systems using oral and maxillofacial cone-beam computed tomography imaging.
Fig. 7
Fig. 7. Funnel plot assessing publication bias across studies assessing precision values of artificial intelligence using oral and maxillofacial cone-beam computed tomography imaging.
Fig. 8
Fig. 8. Precision values of artificial intelligence for detection and segmentation using oral and maxillofacial cone-beam computed tomography scans.
See this image and copyright information in PMC

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