Artificial Intelligence in Pediatric Endodontics: Development of Consensus Guidelines for Working Length Determination Using a Delphi Method
DOI:
https://doi.org/10.48165/ajm.2026.9.02.17Keywords:
Artificial intelligence, Modified Delphi, Consensus, Pediatric endodontics, Primary teeth, Working length determination, Electronic apex locator, Digital dentistryAbstract
Background: Artificial intelligence (AI) is emerging in endodontics as a promising adjunct, with its widespread applications in radiographic interpretation, anatomical landmark identification, and working length determination. Although preliminary evidence suggests that AI may enhance diagnostic accuracy and clinical efficiency, its application in primary teeth remains limited owing to the unique anatomical characteristics associated with physiological root resorption. Currently, no evidence-based clinical recommendations or consensus guidelines exist regarding the use of AI for working length determination in pediatric endodontics. Aim: To develop expert consensus recommendations on the role of artificial intelligence in working length determination of primary teeth using a modified Delphi methodology. Materials and Methods: Three-round modified Delphi study was carried out with an international multidisciplinary panel of experts in the fields of pediatric dentistry, endodontics, dental radiology, artificial intelligence, and digital dentistry. The consensus statements have been formulated after a thorough examination of the literature at hand and they have been refined after rounds of anonymous questionnaires. Participants rated each statement on a 9-point Likert scale. Consensus was predefined as a median agreement score of ≥7, an interquartile range (IQR) ≤2, and at least 75% agreement among panel members. The level of consensus was assessed using descriptive statistics such as median scores, IQRs and percentage agreement. Results: From 42 invited experts, 35 participated in Round 1, whereas 32 completed all three Delphi rounds. The initial 58 statements were refined to 60, of which 56 (93.3%) achieved final consensus. Expert agreement progressively increased across rounds (Kendall’s W: 0.48, 0.67, and 0.81), with high internal consistency (Cronbach’s α = 0.91). The strongest consensus supported AI can be used as a clinical decision-support tool, its integration with electronic apex locators. It also suggested pediatric-specific validation before clinical implementation, appropriate ethical and regulatory oversight, and incorporation of AI education into dental training. Conclusions: As AI continues to transform pediatric endodontic dental diagnostics, expert consensus is essential for safe, ethical, and evidence-informed adaptation. This Delphi study generates recommendations which are anticipated to serve as an important foundation for future clinical guidelines, validation studies, and the development of AI-assisted technologies for working length determination in primary teeth.
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