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A Commentary on <p><b>Alam M K, Alftaikhah S A A, Issrani R</b> <b>et al</b>.</p> <p>Applications of artificial intelligence in the utilisation of imaging modalities in dentistry: a systematic review and meta-analysis of in-vitro studies. <i>Heliyon</i> 2024; <a href="https://doi.org/10.1016/j.heliyon.2024.e24221">https://doi.org/10.1016/j.heliyon.2024.e24221</a>.</p> Design <p>A systematic review of in vitro studies utilising artificial intelligence (AI) in dental imaging. Searches were carried out across multiple databases: CINAHL, Cochrane Library, Embase, Google Scholar, IEEE Xplore, PubMed/MEDLINE, Scopus, and Web of Science as well as hand-searching references from eligible articles.</p> Study selection <p>Studies were eligible if i) classed as in vitro, defined as simulations or laboratory tests outside a living organism, ii) studied the performance of AI techniques and iii) involved analysis of dental imaging. Studies not in English or with insufficient data were excluded.</p> Data analysis <p>An adapted version of the CONSORT bias tool was used for the assessment of studies. Outcome measures were extracted including: odds ratios, true positive rate, true negative rate, positive predictive value, and negative predictive value. A meta-analysis using a fixed-effects model assessed accuracy with a 95% confidence interval. Heterogeneity and overall effect tests were applied to evaluate the reliability of the meta-analysis.</p> Results <p>After screening, nine studies were identified, eight of which focused on Cone Beam Computed Tomography (CBCT) imaging. Endpoints included caries detection, segmentation tasks and virtual 3D model creation. Across the nine studies, and when pooled in meta-analysis, AI performance was shown to be superior to reference standards.</p> Conclusions <p>This systematic review of in-vitro studies highlights the potential of AI to improve the speed or quality of dental imaging tasks. However clinical studies are required to ensure evidence from laboratory studies can translate into clinical practice.</p>

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AI for dental imaging: impressive in vitro, but what about in practice?

  • 期刊论文

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  • 发表日期：

    2025年12月4日

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  • Thomas Evans,
  • Henry David Jeffry Hogg

摘要

A Commentary on

Alam M K, Alftaikhah S A A, Issrani R et al.

Applications of artificial intelligence in the utilisation of imaging modalities in dentistry: a systematic review and meta-analysis of in-vitro studies. Heliyon 2024; https://doi.org/10.1016/j.heliyon.2024.e24221.

Design

A systematic review of in vitro studies utilising artificial intelligence (AI) in dental imaging. Searches were carried out across multiple databases: CINAHL, Cochrane Library, Embase, Google Scholar, IEEE Xplore, PubMed/MEDLINE, Scopus, and Web of Science as well as hand-searching references from eligible articles.

Study selection

Studies were eligible if i) classed as in vitro, defined as simulations or laboratory tests outside a living organism, ii) studied the performance of AI techniques and iii) involved analysis of dental imaging. Studies not in English or with insufficient data were excluded.

Data analysis

An adapted version of the CONSORT bias tool was used for the assessment of studies. Outcome measures were extracted including: odds ratios, true positive rate, true negative rate, positive predictive value, and negative predictive value. A meta-analysis using a fixed-effects model assessed accuracy with a 95% confidence interval. Heterogeneity and overall effect tests were applied to evaluate the reliability of the meta-analysis.

Results

After screening, nine studies were identified, eight of which focused on Cone Beam Computed Tomography (CBCT) imaging. Endpoints included caries detection, segmentation tasks and virtual 3D model creation. Across the nine studies, and when pooled in meta-analysis, AI performance was shown to be superior to reference standards.

Conclusions

This systematic review of in-vitro studies highlights the potential of AI to improve the speed or quality of dental imaging tasks. However clinical studies are required to ensure evidence from laboratory studies can translate into clinical practice.