Periodontitis is a complex, multifactorial chronic inflammatory condition that impacts the adjacent hard and soft tissues. Microorganisms, especially gram-negative anaerobic pathogens, are a causative factor for periodontal disease. Periodontitis is identified by observing deeper periodontal pockets, clinical attachment loss, and the reduction of alveolar bone, often in conjunction with these indicators. The condition can vary in severity and be classified as mild, moderate, or severe. Scaling and root planing, combined with mechanical debridement, may not adequately reduce the bacterial load; therefore, adding local or systemic antimicrobials is advised as an adjunctive treatment. Commonly utilized local drug delivery agents for patients suffering from periodontitis include tetracycline, metronidazole, minocycline, doxycycline, and chlorhexidine. This system targets the pockets and eliminates the pathogens. Metronidazole is a nitroimidazole compound used commonly against gram-negative anaerobes. Its mechanism lies in four basic steps through which bacterial cell death occurs. A 25% metronidazole gel is used widely in periodontitis patients. The effectiveness of metronidazole as a local drug delivery agent has been evaluated in numerous studies, which have shown improvements in clinical parameters. To achieve favorable clinical outcomes, the non-surgical treatment of peri-implantitis should involve the systemic or local administration of metronidazole. Thus, the role of metronidazole in the emergence of periodontal diseases and its therapeutic uses are investigated in this narrative review.
Artificial intelligence (AI) is changing each of the healthcare fields, including periodontology, through the improvement of every diagnosis, treatment plan, and the handling of all patients. AI-driven technologies such as machine learning, deep learning, and computer vision are incorporated into radiographic analysis, automated disease detection, and prognosis prediction. These improvements effectively enable the early detection of periodontal diseases and efficient classification of disease severity. In addition, they allow for specially personalized treatment approaches. AI makes automated periodontal charting, virtual patient monitoring, and decision support systems easier, which improve clinical outcomes and patient care. Despite its immense potential, many substantial difficulties remain, such as data privacy, algorithm reliability, and the meaningful need for clinical validation. This review indicates the revolutionary function of AI in many current dental works and explores all present uses, advantages, limits, and possibilities in periodontology.