Actionable companion · Assessment & reasoning

Learning with agency: Examination of new clinical dental caries in school children using real intra oral photos with artificial intelligence model YOLO-V8x

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not claims or recommendations attributed to the source authors. No independent peer review of this guide is asserted.

Evidence and context

Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Primary school, Children · stage unspecified. Health-professions learning must be distinguished from K–12 instruction.

Original authors’ abstract, reproduced under the linked source license:

Abstract Background Dental caries is a common chronic condition among school children, and in Indonesia, rates have been higher than 80%. Traditional diagnostic techniques tend to be long and dependent upon the availability of healthcare professionals and hence promote development of alternatives involving artificial intelligence (AI). This work aimed to evaluate the effectiveness of the application titled HI Bogi that embeds the model known as YOLOv8 in distinguishing dental caries among primary school pupils in Cimahi, Indonesia. Materials and methods A cross-sectional analytic model with prospectively collected data was adopted. A dataset consisting of 3,221 JPG photographs was created and labeled based on the International Caries Detection and Assessment System (ICDAS, D0 - D6) using Roboflow software. The photographs were resized to a size of 640 × 640 pixels and distributed into training (2,266 photographs), validation (635 photographs), and testing (320 photographs) sets. A YOLOv8x algorithm was used for application in deep learning tasks, and its performance was determined through mean Average Precision (mAP), Intersection over Union (IoU) measurements, and precision and recall metrics. Additionally, a Mann Whitney statistical test was carried out to contrast classification efficiency between the AI system and dental practitioners’ classification efficacy, while both methods’ diagnostic speed was tested. Results mAP was 45.8%, and precision was 72.6%, and recall was 41.1% for YOLOv8x model. Comparative testing across ICDAS categories did not vary significantly between dentists and AI (p > 0.05). Detection of caries (D1–D6) was observed to have a sensitivity ranged from 82.31% to 96.45% and specificity ranged from 77.10% to 99.33% on new data. Additionally, testing was completed approximately four times faster with AI compared to manual testing (p = 0.000). Conclusion The YOLOv8x model in the HI Bogi app demonstrated diagnostic performance equal to dentists and a drastically shortened examination time, validating its use in school-based dental health programs. But class-specific precision variation suggests further refinement is needed. Further studies ought to increase datasets and characterize advanced architecture, such as transformer-based architecture, to achieve higher specificity and detection rates in rare lesion categories. Keywords: Dental caries, ICDAS, School children, YOLO-V8x

A 90-day district pilot

1. Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
2. Days 16–30 · Design. Pilot one teacher-led unit with short guided turns. The tutor asks for an explanation and offers a graduated hint before any worked solution. Use concrete examples and an equivalent task without AI. Name an accountable owner and set the baseline, success threshold and budget before implementation.
3. Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
4. Days 61–90 · Decide. Use an age-appropriate independent post-task and a delayed parallel task. Score explanation quality and transfer, not the number of agent messages or completion rate alone. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Rina Putri Noer Fadilah; Rasmi Rikmasari; Saiful Akbar; Arlette Suzy Setiawan. Examination of new clinical dental caries in school children using real intra oral photos with artificial intelligence model YOLO-V8x. BMC Oral Health (2025-12-20). https://doi.org/10.1186/s12903-025-07486-x

Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.