Biologically Inspired Oral Inflammatory Load For Periodontal Applications
| dc.contributor.advisor | Ghafar-Zadeh, Ebrahim | |
| dc.contributor.author | Soheili, Fatemeh | |
| dc.date.accessioned | 2025-04-10T10:59:32Z | |
| dc.date.available | 2025-04-10T10:59:32Z | |
| dc.date.copyright | 2025-01-13 | |
| dc.date.issued | 2025-04-10 | |
| dc.date.updated | 2025-04-10T10:59:32Z | |
| dc.degree.discipline | Biology | |
| dc.degree.level | Doctoral | |
| dc.degree.name | PhD - Doctor of Philosophy | |
| dc.description.abstract | Periodontal disease is a prevalent inflammatory condition affecting the supporting structures of teeth, including the gums, periodontal ligament, and alveolar bone. It is one of the leading causes of tooth loss in adults and has been linked to various systemic health issues, such as cardiovascular disease, diabetes, and respiratory conditions. Early detection and accurate diagnosis are critical for preventing disease progression and mitigating its broader health impacts. However, current diagnostic methods are labor-intensive, time-consuming, and require specialized clinical expertise. This dissertation presents an innovative approach that integrates bio-inspired cell isolation techniques with artificial intelligence (AI) to enhance the detection and quantification of oral inflammatory markers, specifically oral polymorphonuclear neutrophils (oPMNs), for periodontal diagnostics. Our method involves saliva sampling using a standardized 10 ml oral rinse collected from both healthy and periodontal subjects. We optimized and characterized a novel technique for isolating oPMNs from other cellular components in saliva, leveraging their unique adhesion properties. Hydrophilic materials were coated on surfaces to selectively capture and isolate oPMNs. The isolated cells were then analyzed using an AI server developed and trained specifically to detect and quantify oPMNs in both healthy and clinical samples. The AI model categorized the results into five distinct levels of periodontal disease, providing a detailed assessment of disease severity. The results demonstrated the effectiveness of the proposed method in isolating and quantifying oPMNs with high accuracy. Clinical validation on samples from a diverse cohort, ranging from healthy individuals to those with severe periodontitis, confirmed the method's functionality. The oral inflammatory load (OIL) assessment showed an error rate of less than 5% compared to standard laboratory-based methods, underscoring its precision and reliability. The integration of AI in this context significantly reduces the diagnostic workload for healthcare professionals while enhancing the accuracy and speed of assessments. The method offers a practical, scalable solution for periodontal disease diagnostics, enabling earlier and more precise identification of disease states. Beyond its clinical applications, the system has the potential to be developed into a point-of-care diagnostic tool, providing immediate results without the need for specialized laboratory equipment. This innovation could be particularly impactful in resource-limited settings, where access to advanced medical facilities is restricted. In conclusion, the combination of bio-inspired isolation techniques and AI-assisted analysis represents a significant advancement in periodontal diagnostics. This approach not only improves current diagnostic practices but also opens new possibilities for the future of periodontal healthcare, where rapid, accurate, and accessible diagnostics become the standard. Through this work, we aim to contribute to the development of innovative, patient-centered solutions for periodontal disease management, ultimately promoting better oral and systemic health outcomes. | |
| dc.identifier.uri | https://hdl.handle.net/10315/42888 | |
| dc.language | en | |
| dc.rights | Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests. | |
| dc.subject.keywords | Periodontal disease | |
| dc.subject.keywords | Diagnosis | |
| dc.subject.keywords | Oral neutrophil | |
| dc.subject.keywords | White blood cells | |
| dc.title | Biologically Inspired Oral Inflammatory Load For Periodontal Applications | |
| dc.type | Electronic Thesis or Dissertation |
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