Clinical Oral Investigations· 2026Q1
Development and external validation of an AI pipeline for automated estimation of a radiographic DMFT (rDMFT) score from panoramic radiographs: a feasibility study
- 0citations
- Q1SCImago
- 2026year
Short summary
An AI pipeline can estimate a radiographic DMFT (rDMFT) score from panoramic X-rays with an ICC of 0.84, comparable to agreement between two human dentists (ICC 0.88), but shows higher error rates and unreliability for decayed teeth.
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Key points
- An AI pipeline achieved an ICC of 0.84 for estimating radiographic DMFT (rDMFT) scores from panoramic X-rays.
- The pipeline's performance was comparable to, but slightly below, the agreement between two human dentists (ICC 0.88).
- The AI pipeline accurately detected missing teeth but showed lower agreement for decayed teeth (r 0.34) and over-called missing teeth in 15% of cases.
- The pipeline is considered a research building block and not suitable for clinical decision-making or triage.
AI-generated from the title and abstract; the full text is not read.
Abstract
OBJECTIVES: To assess the feasibility of estimating a radiographic Decayed-Missing-Filled-Teeth score (rDMFT), a proxy for the clinical DMFT index, from a single panoramic radiograph with a position-aware deep-learning pipeline. MATERIALS AND METHODS: A tooth-status detector and a tooth-numbering detector (32 FDI positions), trained on DENTEX 2023, were combined in a position-aware read-out (one tooth per FDI position, third molars excluded, missing teeth = 28 - occupied positions) and validated on 100 panoramic radiographs from an independent hospital against a two-dentist, radiograph-based reference (intraclass correlation coefficient, ICC; mean absolute error, MAE; Bland-Altman analysis). RESULTS: Against the reference, the pipeline reached an ICC of 0.84 (95% CI 0.74 to 0.90) and an MAE of 2.38, below the between-dentist agreement in point estimate (ICC 0.88, MAE 2.13), with larger errors against each dentist alone (MAE 3.00 and 2.73) and a bias of + 1.14 units from caries over-detection; agreement was highest for filled (r 0.90) and lowest for decayed teeth (r 0.34; dentists 0.16). In the 26 radiographs with missing teeth the missing count was exact in 42% (62% within ± 1 tooth), and missing teeth were over-called in 15 of 74 complete dentitions; tooth loss was no longer overlooked (26 of 26 detected), but the overall ICC was 0.84 versus 0.90 without numbering. CONCLUSIONS: Automated rDMFT estimation is feasible, but agreement was lower in point estimate than between two dentists, with larger errors against each dentist. The missing component now responds to actual tooth loss but remains imprecise and is over-called in some complete dentitions; the decayed component is unreliable. The pipeline is a research building block, not a decision-support or triage tool. CLINICAL RELEVANCE: rDMFT is a radiographic proxy, not a clinical measure, and the pipeline is not proposed for clinical use; any application would first require validation in unselected cohorts against clinically determined reference standards.
The authors' abstract, as published at the source. Clinical Oral Investigations, 2026 · DOI ↗
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Field: Oral Surgery
Oral SurgeryDentistry