Issue 14, 2024

Periodontal disease and emerging point-of-care technologies for its diagnosis

Abstract

Periodontal disease (PD), a chronic inflammatory disorder that damages the tooth and its supporting components, is a common global oral health problem. Understanding the intricacies of these disorders, from gingivitis to severe PD, is critical for efficient treatment, diagnosis, and prevention in dental care. Periodontal biosensors and biomarkers are critical in improving oral health diagnostic skills. Clinicians may accomplish early identification, tailored therapy, and efficient tracking of periodontal diseases by using these technologies, ushering in a new age of accurate oral healthcare. Traditional periodontitis diagnostic methods frequently rely on physical probing and visual examinations, necessitating the development of point-of-care (POC) devices. As periodontal disorders necessitate more precise and rapid diagnosis, incorporating novel innovations in biosensors and biomarkers becomes increasingly crucial. These innovations improve our capacity to diagnose, monitor, and adapt periodontal therapies, bringing in the next phase of customized and effective dental healthcare. The review discusses the characteristics and stages of PD, clinical treatment techniques, prominent biomarkers and infection-associated factors that may be employed to determine PD, biomedical sensing, and POC appliances that have been created so far to diagnose stages of PD and its progression profile, as well as predicting future developments in this field.

Graphical abstract: Periodontal disease and emerging point-of-care technologies for its diagnosis

Article information

Article type
Critical Review
Submitted
05 apr 2024
Accepted
05 iyn 2024
First published
07 iyn 2024
This article is Open Access
Creative Commons BY license

Lab Chip, 2024,24, 3326-3346

Periodontal disease and emerging point-of-care technologies for its diagnosis

J. Korgaonkar, A. Y. Tarman, H. Ceylan Koydemir and S. S. Chukkapalli, Lab Chip, 2024, 24, 3326 DOI: 10.1039/D4LC00295D

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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