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

NCT07254039 | Not Yet Recruiting

• 1 other identifier: SALIENCE2025
• Study Type: observational
• Target: 200
• Locations: 0 countries
• Sites: N/A

Brief 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

Keywords

Saliva Diagnostics; Electrochemical Sensor; AI-Assisted Diagnostics; Biomarkers; Periodontal diseases

Outcome Measures

Primary Outcomes (1)

• Sensitivity and Specificity of the AI-Assisted Electrochemical Saliva Sensor for Detecting Periodontitis

  This outcome assesses the diagnostic accuracy of the AI-assisted electrochemical sensor platform by calculating sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and the area under the receiver operating characteristic curve (AUC). The platform analyzes electrochemical signal patterns in saliva samples, and an AI-based classification model predicts whether participants have periodontitis. Clinical periodontal status (periodontitis vs. periodontal health) will be established using a full-mouth clinical examination according to the 2017 World Workshop classification criteria. Diagnostic accuracy metrics from the sensor platform will be compared against this clinical gold standard.

  Within 24 months after study start (end of data collection and analysis).

Secondary Outcomes (3)

• Within-Run Repeatability of Electrochemical Saliva Sensor Signals (Coefficient of Variation)

  Measured throughout the 24-month study period.

• Correlation Between Sensor Outputs and Biochemical Reference Analyses

  24 month

• System Usability Scale (SUS) Score for Clinical Use of the AI-Assisted Saliva Sensor Platform

  24 month

Study Arms (2)

Periodontitis Group

Adults diagnosed with periodontitis based on clinical criteria.

Healthy Control Group

Adults with no clinical signs of periodontal disease.

Eligibility Criteria

• Age: 18 Years - 80 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Adult participants (≥18 years) will be recruited from an affiliated dental clinic. The study population consists of two groups: (1) patients clinically diagnosed with periodontitis according to the 2018 AAP/EFP classification, and (2) periodontally healthy adults with no signs of periodontal disease. All participants must be able to provide informed consent and a saliva sample according to study protocol.

You may qualify if:

• Adults ≥18 years.
• Able and willing to provide written informed consent.
• Ability to provide an unstimulated whole saliva sample per protocol (no food, drink, gum, toothbrushing, or smoking within 60 minutes prior to sampling).
• Periodontitis group: Clinical diagnosis of periodontitis according to 2018 AAP/EFP criteria (e.g., interdental CAL ≥3 mm at ≥2 non-adjacent teeth with radiographic bone loss; probing pocket depth ≥4 mm in ≥2 teeth).
• Healthy control group: No clinical signs of periodontal disease (no probing depths \>3 mm, bleeding on probing \<10%, and no radiographic bone loss).

You may not qualify if:

• Systemic antibiotics or systemic anti-inflammatory/immunosuppressive therapy within the past 3 months.
• Periodontal therapy (scaling/root planing or surgery) within the past 6 months.
• Current acute oral infection or abscess.
• Systemic conditions known to markedly alter saliva composition/flow (e.g., Sjögren's syndrome, prior head-and-neck radiation, ongoing chemotherapy, uncontrolled diabetes).
• Use of strongly xerogenic medications not on a stable dose ≥4 weeks, or clinically significant hyposalivation preventing sampling.
• Inability to comply with sampling procedures (e.g., cannot abstain from food/drink/tobacco for 60 minutes prior to sampling).
• Pregnancy or lactation.

Sponsors & Collaborators

• Ostergotland County Council, Sweden: lead
• Malmö University: collaborator
• Linkoeping University: collaborator

Biospecimen

Retention: SAMPLES WITHOUT DNA

Unstimulated whole saliva samples will be collected from adult participants with periodontitis and from healthy controls. The samples will be used for electrochemical and biochemical analyses of salivary biomarkers related to inflammation, oxidative stress, and microbial activity. No DNA or genetic material will be extracted. Samples will be stored under controlled conditions until analysis is completed.

MeSH Terms

Conditions

Periodontal Diseases; Disease

Condition Hierarchy (Ancestors)

Mouth Diseases; Stomatognathic Diseases; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Central Study Contacts

Shariel Sayardoust, DDS., PhD

CONTACT

+46736564648; shariel.sayardoust@regionostergotland.se

Magnus Falk, PhD

CONTACT

+460703857476; magnus.falk@mau.se

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Associate Professor

Study Record Dates

• First Submitted: November 15, 2025
• First Posted: November 28, 2025
• Study Start: December 1, 2025
• Primary Completion (Estimated): December 31, 2027
• Study Completion (Estimated): June 30, 2028
• Last Updated: November 28, 2025

Record last verified: 2025-10

Data Sharing

• IPD Sharing: Will not share