Artificial intelligence models for diagnosing gingivitis and periodontal disease: A systematic review.

STATEMENT OF PROBLEM: Artificial intelligence (AI) models have been developed for periodontal applications, including diagnosing gingivitis and periodontal disease, but their accuracy and maturity of the technology remain unclear. PURPOSE: The purpose of this systematic review was to evaluate the performance of the AI models for detecting dental plaque and diagnosing gingivitis and periodontal disease. MATERIAL AND METHODS: A review was performed in 4 databases: MEDLINE/PubMed, World of Science, Cochrane, and Scopus. A manual search was also conducted. Studies were classified into 4 groups: detecting dental plaque, diagnosis of gingivitis, diagnosis of periodontal disease from intraoral images, and diagnosis of alveolar bone loss from periapical, bitewing, and panoramic radiographs. Two investigators evaluated the studies independently by applying the Joanna Briggs Institute critical appraisal. A third examiner was consulted to resolve any lack of consensus. RESULTS: Twenty-four articles were included: 2 studies developed AI models for detecting plaque, resulting in accuracy ranging from 73.6% to 99%; 7 studies assessed the ability to diagnose gingivitis from intraoral photographs reporting an accuracy between 74% and 78.20%; 1 study used fluorescent intraoral images to diagnose gingivitis reporting 67.7% to 73.72% accuracy; 3 studies assessed the ability to diagnose periodontal disease from intraoral photographs with an accuracy between 47% and 81%, and 11 studies evaluated the performance of AI models for detecting alveolar bone loss from radiographic images reporting an accuracy between 73.4% and 99%. CONCLUSIONS: AI models for periodontology applications are still in development but might provide a powerful diagnostic tool.

The Future of Dentistry: How AI is Transforming Dental Practices.

Artificial Intelligence (AI) builds upon the digital tools the dental profession has already adopted. It promises to transform the patient journey outlined above and support the quest of the dental community to improve oral health. In this review, we will detail how AI is used today in the clinic to improve patient care and experience. The review will start with the diagnostic capabilities of AI and then move to how it can be used to support treatment planning and patient communication. It will end with a discussion of how AI quantification helps dentists monitor longitudinal outcomes for patients and across the practice.

Evaluation of virulence potential of Aspergillus tubingensis and subsequent biochemical and enzymatic defense response of cotton.

Aspergillus tubingensis is a causative known pathogen of various important crops, worldwide. The existing study was aimed to examine the virulence potential of A. tubingensis on resistant (NIA-Sadori) and susceptible (CIM-573) cultivars of cotton. For this purpose, both cultivars were inoculated with pathogen and altered morphology of diseased leaves was observed with light and scanning electron microscope. Disease severity was measured and estimated to be 68.7 and 27.1% in susceptible and resistant cultivars, respectively. To understand and compare defense mechanism of resistant and susceptible cultivars, different biochemical and enzymatic changes were observed. After the infection of A. tubingensis, increase in the concentrations of sugar, total protein, proline, phenol, and phenylalanine ammonia lyase (PAL) was more prominent in resistant cultivar, than the susceptible one. Moreover, due to increased number of dead cells, significantly higher electrolyte leakage was detected in susceptible cultivar. Principal component analysis confirmed the effect of A. tubingensis on growth attributes and various physiological and biochemical activities of cotton. These findings help us to suggest a possible role of proline content, protein content, and PAL activity in resistance mechanism of Cotton plant.

AI-Driven Transformation of Dental Benefits.

2021 is shaping up to be the year of artificial intelligence (AI) for the dental industry. Not only are providers adopting AI at a rapid pace, payers are tapping this technology to automate their claims review operations and reduce friction in provider interactions. In part three of this six-part dental AI series, the authors offer their view from the frontlines of dental claims processing and the promising future impact of AI. Representing clinical and business viewpoints, the authors draw on experience working at and with some of the largest dental payers in the country. This article presents a forward-looking perspective on the potential of dental AI to improve payer-provider relations, streamline claims review, and ultimately provide an improved patient experience.

AI-Driven Growth of DSOs.

DSOs have been at the forefront of adopting technology that demonstrates value-add to their businesses. Hence, DSOs have been quick to identify artificial intelligence (AI) as a transformative means to support their business and clinical processes to benefit patient care. Specifically, AI can unlock clinical insights to inform practice affiliation and post-affiliation onboarding. Next, it can help support a standard of care across practices for compliance. And finally, AI technology can improve practice performance through the use of visualization tools to support dentists with patient treatment discussions and professional development of associates.