首页|Shenzhen University Reports Findings in Artificial Intelligence (Evaluating Toot h Segmentation Accuracy and Time Efficiency in CBCT Images using Artificial Inte lligence: A Systematic Review and Meta-analysis)
Shenzhen University Reports Findings in Artificial Intelligence (Evaluating Toot h Segmentation Accuracy and Time Efficiency in CBCT Images using Artificial Inte lligence: A Systematic Review and Meta-analysis)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Artificial Intelligenc e is the subject of a report. According to news originating from Shenzhen, Peopl e’s Republic of China, by NewsRx correspondents, research stated, “This systemat ic review and meta-analysis aimed to assess the current performance of artificia l intelligence (AI)- based methods for tooth segmentation in three-dimensional co ne-beam computed tomography (CBCT) images, with a focus on their accuracy and ef ficiency compared to those of manual segmentation techniques. The data analyzed in this review consisted of a wide range of research studies utilizing AI algori thms for tooth segmentation in CBCT images.” Our news journalists obtained a quote from the research from Shenzhen University , “Meta-analysis was performed, focusing on the evaluation of the segmentation r esults using the dice similarity coefficient (DSC). PubMed, Embase, Scopus, Web of Science, and IEEE Explore were comprehensively searched to identify relevant studies. Study selection The initial search yielded 5642 entries, and subsequent screening and selection processes led to the inclusion of 35 studies in the sys tematic review. Among the various segmentation methods employed, convolutional n eural networks, particularly the U-net model, are the most commonly utilized. Th e pooled effect of the DSC score for tooth segmentation was 0.95 (95% CI 0.94 to 0.96). Furthermore, seven papers provided insights into the time requ ired for segmentation, which ranged from 1.5 s to 3.4 min when utilizing AI tech niques. AI models demonstrated favorable accuracy in automatically segmenting te eth from CBCT images while reducing the time required for the process. Neverthel ess, correction methods for metal artifacts and tooth structure segmentation usi ng different imaging modalities should be addressed in future studies. AI algori thms have great potential for precise tooth measurements, orthodontic treatment planning, dental implant placement, and other dental procedures that require acc urate tooth delineation.”
ShenzhenPeople’s Republic of ChinaAs iaArtificial IntelligenceDentistryEmerging TechnologiesHealth and Medici neMachine Learning
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