Development and Validation of an AI-Assisted Electrochemical Sensor Platform for Saliva-Based Diagnostics in Periodontitis
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 发起方
- 入组人数
- 200
- 主要终点
- Sensitivity and Specificity of the AI-Assisted Electrochemical Saliva Sensor for Detecting Periodontitis
研究概览
简要总结
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.
详细描述
Background and Rationale Periodontitis is a chronic inflammatory disease that affects the supporting tissues of the teeth and is one of the most prevalent oral diseases worldwide. Despite its high prevalence, early diagnosis remains a major challenge, as current diagnostic tools rely on retrospective measures such as pocket depth, bleeding on probing, and radiographic bone loss. These indicators reflect past tissue destruction rather than current disease activity, leading to delayed diagnosis and treatment.
Saliva is an attractive diagnostic fluid for non-invasive, real-time disease monitoring because it contains a complex mixture of biomarkers that reflect both oral and systemic health. However, its biological variability, matrix effects, and susceptibility to contamination have limited its reliability as a diagnostic medium. To overcome these challenges, this project integrates electrochemical sensing with artificial intelligence (AI)-driven data interpretation and synthetic data generation to enable robust saliva-based diagnostics.
Study Objectives
The overall objective is to develop and validate an AI-assisted electrochemical sensor platform capable of detecting biochemical patterns in saliva associated with periodontitis. Specific aims are to:
Design and optimize a modular, multi-electrode sensor platform for saliva analysis.
研究设计
- 研究类型
- Observational
- 观察模型
- Case Control
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 80 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •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).
排除标准
- •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.
结局指标
主要结局
Sensitivity and Specificity of the AI-Assisted Electrochemical Saliva Sensor for Detecting Periodontitis
时间窗: Within 24 months after study start (end of data collection and analysis).
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.
次要结局
- 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)
研究者
Shariel Sayardoust
Associate Professor
Ostergotland County Council, Sweden