Abstract

Introduction

To investigate protocols for volumetric measurement of alveolar defect in alveolar cleft cases, including innovations in Artificial Intelligence (AI).

Methods

Searches were conducted using PubMed, Embase, and Scopus databases, along with a hand search. Based on inclusion and exclusion criteria, 17 studies were selected. Additionally, discussions on the protocols included workflow and anatomical landmarks associated with the measurement.

Results

Thirty workflows were identified and categorized into virtual and 3D printing-based approaches. A 3D U-Net architecture was employed for segmentation and measurement using artificial intelligence. Various anatomical landmarks for defining alveolar cleft boundaries were described. The average volume of the alveolar cleft was 1.61 cm3.

Discussion

The majority of the studies were published within 3 years of the article search, indicating an increased desire to optimize the utilization of 3D imaging beyond simple assessments. Recent developments in AI have simplified complex imaging tasks; hence, volumetric assessments are expected to increase in the future.

Conclusion

The expert workflow with the most supporting evidence is manual tracing on the axial slice. Studies using AI are emerging and need to be explored. The anatomical landmarks advocated by this review are cementoenamel junction, anterior nasal spine, and continuity with the alveolar segments with adequate labio-palatal thickness as superior, inferior, and labio-palatal borders, respectively. Nonetheless, more studies are needed to help create a technical guideline.

Keywords: 3D imaging, Computed tomography (CT), Cone beam CT, Cleft lip and alveolus, Cleft palate.
Fulltext HTML PDF
1800
1801
1802
1803
1804