Artificial Intelligence for Early Detection of Childhood Dental and Orthodontic Diseases

Summary

Motivation

Northern Ireland (NI) has the UK’s highest rate of childhood dental decay, with over 40% of children experiencing caries by age eight, disproportionately affecting socio-economically deprived communities.

Yet, caries and malocclusion screening remains labour-intensive, subjective, and inconsistently available across Trusts.

Artificial intelligence (AI) now enables direct interpretation of dental and craniofacial images, but existing models are either limited to adults, based on behavioural surveys, or trained in non-UK populations with little attention to transferability or fairness.

There is currently no validated, imaging-based AI model for detecting and grading dental or orthodontic pathologies in children that is suitable for clinical integration within NI’s Health and Social Care (HSC) system.

Developing such a model would address a major unmet need in early prevention and reduce inequality in oral-health outcomes.

Objective

To develop and validate multimodal deep-learning models for automated detection and severity grading of paediatric dental and orthodontic pathologies.

Outcomes

The PhD will

1. construct convolutional and transformer-based models trained on Chinese paediatric datasets comprising bitewing, periapical, cephalometric, cone-beam CT, and intraoral images;
2. implement cross-domain transfer learning and domain-adaptation pipelines for testing on NI datasets;
3. quantify performance (AUC, F1, sensitivity/specificity) and fairness across ethnic and imaging-system differences; and
4. develop interpretable visual explanations (Grad-CAM, SHAP) for clinical acceptance.

The research will yield:

• A validated AI framework for multimodal dental image analysis in children;
• Calibration protocols and bias-mitigation strategies enabling safe transfer of models between populations;
• An open-access benchmark dataset and codebase compliant with FAIR and GDPR standards; and
• Evidence for clinical translation through collaboration with HSC Trusts and iREACH Health facilities.

Collectively, the work will generate a deployable prototype capable of assisting clinicians in early detection of caries, periapical lesions, and orthodontic anomalies, ultimately informing NI’s population-level prevention programmes.

AccessNI clearance required

Please note, the successful candidate will be required to obtain AccessNI clearance prior to registration due to the nature of the project.

Essential criteria

Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.

We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.

In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.

• Sound understanding of subject area as evidenced by a comprehensive research proposal
• A comprehensive and articulate personal statement

Desirable Criteria

If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.

• First Class Honours (1st) Degree
• Completion of Masters at a level equivalent to commendation or distinction at Ulster
• Practice-based research experience and/or dissemination
• Experience using research methods or other approaches relevant to the subject domain
• Work experience relevant to the proposed project
• Publications record appropriate to career stage
• Experience of presentation of research findings

Equal Opportunities

The University is an equal opportunities employer and welcomes applicants from all sections of the community, particularly from those with disabilities.

Appointment will be made on merit.

Funding and eligibility

NOTE - This is a self-funded research project and applicants will be required to provide evidence of funds to support their tuition fees and living expenses.

Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.