Journal of oral biology and craniofacial research · 2025
Diagnostic efficiency of digital photography and AI-assisted image interpretation in dental caries examination: An umbrella review.
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Madan Kumar P D, Sivakumar Sasidharan, Rajeshwari S, Lavanya C, Ranganathan K
Аннотация
BACKGROUND: Dental caries, a prevalent oral health issue impacting all demographics globally, necessitates precise detection and evaluation for effective intervention and preventive strategies. Advances in scientific research and technology have facilitated the development of innovative applications that utilize Artificial Intelligence (AI) within the medical sciences. AI offers promising improvements in risk assessment and caries prediction. A comprehensive body of evidence assessing the diagnostic accuracy of AI for identifying Dental caries via digital intraoral photographs remains insufficient.
AIM AND OBJECTIVES: Thus, the aim is to consolidate evidence regarding the efficacy of photographic examination and subsequent image analysis, demonstrating that these methods offer comparable accuracy to traditional visual inspection in the diagnosis of Dental caries.
METHODOLOGY: Eight systematic reviews were obtained from Medline, Web of Science, Scopus, and EMBASE up to June 2024. These articles were screened at the title, abstract, and full-text levels, with the risk of bias evaluated using the Joanna Briggs Institute (JBI) assessment tool.
RESULTS: AI-based dental caries detection from intraoral photographs showed sensitivities ranging from 67 % to 96 % and specificities from 75 % to 99.2 % with AUC values reported between 0.74 and 0.987 across modalities. The overall risk of bias was minimal.
CONCLUSION: Therefore, AI-assisted interpretation of digital photography can significantly aid in the early identification of Dental caries, thereby enhancing patient outcomes.
AIM AND OBJECTIVES: Thus, the aim is to consolidate evidence regarding the efficacy of photographic examination and subsequent image analysis, demonstrating that these methods offer comparable accuracy to traditional visual inspection in the diagnosis of Dental caries.
METHODOLOGY: Eight systematic reviews were obtained from Medline, Web of Science, Scopus, and EMBASE up to June 2024. These articles were screened at the title, abstract, and full-text levels, with the risk of bias evaluated using the Joanna Briggs Institute (JBI) assessment tool.
RESULTS: AI-based dental caries detection from intraoral photographs showed sensitivities ranging from 67 % to 96 % and specificities from 75 % to 99.2 % with AUC values reported between 0.74 and 0.987 across modalities. The overall risk of bias was minimal.
CONCLUSION: Therefore, AI-assisted interpretation of digital photography can significantly aid in the early identification of Dental caries, thereby enhancing patient outcomes.