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.

Prolonged treatment with open-label pirfenidone in Hermansky-Pudlak syndrome pulmonary fibrosis.

PURPOSE: Limited information is available regarding chronic treatment with pirfenidone, an anti-fibrotic drug. Effects of long-term open-label pirfenidone were evaluated in a small cohort with Hermansky-Pudlak syndrome (HPS), a rare autosomal recessive disorder with highly penetrant pulmonary fibrosis. RESULTS: Three patients with HPS pulmonary fibrosis treated with open-label pirfenidone and twenty-one historical controls randomized to placebo were studied at a single center. Mean duration of treatment with pirfenidone for 3 patients with HPS pulmonary fibrosis was 13.1 years. Annual changes in FVC and DLCO with pirfenidone treatment were 0.46 and - 0.93% predicted, respectively. In comparison, historical controls randomized to receive placebo experienced mean annual changes in FVC and DLCO of -4.4 and - 2.3% predicted, respectively. High-resolution computed tomography (HRCT) scans revealed improved ground glass opacities with development of minimal interstitial reticulations in 1 patient after 12.8 years of treatment with pirfenidone. Slowly progressive increase in bilateral interstitial fibrosis developed in a different patient, who received pirfenidone for 18.1 years and died at 73 years of age due to HPS pulmonary fibrosis. Another patient treated with pirfenidone for 8.4 years had attenuated ground glass opacification on HRCT scan and improved oxygenation; this patient died due to chronic complications from colitis, and not pulmonary fibrosis. Adverse effects were generally limited to mild gastrointestinal discomfort and transient elevations of alanine aminotransferase in one patient. CONCLUSIONS: Chronic treatment with pirfenidone may provide clinical benefit with few adverse effects for some patients with HPS pulmonary fibrosis. These results suggest that compassionate use of pirfenidone could be considered on a case-by-case basis for patients with HPS pulmonary fibrosis.