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AI-Assisted Saliva Diagnostics Using an Electrochemical Sensor Platform for Periodontitis Detection (SALIENCE)

NCT07254039 · Status: NOT_YET_RECRUITING · Type: OBSERVATIONAL · Enrollment: 200

Last updated 2025-11-28

No results posted yet for this study

Summary

This observational study aims to develop and validate a novel, AI-assisted electrochemical sensor platform for saliva-based diagnostics in periodontitis. Periodontitis is a chronic inflammatory disease affecting the gums and supporting tissues of the teeth. Despite its high global prevalence, early diagnosis remains challenging because the disease often progresses silently until irreversible damage has occurred.

Saliva offers a promising, non-invasive diagnostic medium that reflects both oral and systemic health. However, its biological complexity and variability have limited its clinical use. This project addresses these challenges by combining advanced electrochemical sensing with artificial intelligence (AI) and synthetic data generation to improve diagnostic precision and reliability.

The study involves the collection of saliva samples from adult participants with diagnosed periodontitis and from healthy controls. The samples will be analyzed using a modular sensor platform equipped with multiple electrodes that detect electrochemical signals from a wide range of salivary biomarkers. The sensor data will then be processed using machine learning models trained on both real and synthetic data to classify disease states.

The main goals are to:

Evaluate the performance of the electrochemical sensor array for saliva analysis.

Develop and validate AI-based algorithms for detecting and differentiating between healthy and diseased samples.

Generate feasibility data supporting future clinical implementation of saliva-based diagnostics for periodontitis.

This interdisciplinary project combines expertise in clinical dentistry, biomedical engineering, and computer science. It is conducted in collaboration between Linköping University and Malmö University, with patient sampling carried out at an affiliated dental clinic.

The study is expected to result in a working proof-of-concept device that enables real-time, non-invasive detection of periodontitis at the point of care. By enabling earlier diagnosis and more personalized treatment, this technology may transform periodontal care and serve as a foundation for future saliva-based diagnostics targeting other oral and systemic diseases.

Conditions

  • Periodontal Diseases
  • Biomarkers (D23.050.301)
  • Diagnostic
  • Saliva

Sponsors & Collaborators

  • Malmö University

    collaborator OTHER

  • Linkoeping University

    collaborator OTHER_GOV

  • Ostergotland County Council, Sweden

    lead OTHER

Eligibility

Min Age
18 Years
Max Age
80 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2025-12-01
Primary Completion
2027-12-31
Completion
2028-06-30

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07254039 on ClinicalTrials.gov