Asmhan Tariq, Fatmah Bin Nakhi, Fatema Salah, Gabass Eltayeb, Ghada Jassem Abdulla, Noor Najim, Salma Ahmed Khedr, Sara Elkerdasy, Natheer Al-Rawi, Sausan Alkawas, Marwan Mohammed, Shishir Ram Shetty
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引用次数: 0
Abstract
Purpose: Artificial intelligence (AI) is poised to play a major role in medical diagnostics. Periodontal disease is one of the most common oral diseases. The early diagnosis of periodontal disease is essential for effective treatment and a favorable prognosis. This study aimed to assess the effectiveness of AI in diagnosing periodontal bone loss through radiographic analysis.
Materials and methods: A literature search involving 5 databases (PubMed, ScienceDirect, Scopus, Health and Medical Collection, Dentistry and Oral Sciences) was carried out. A specific combination of keywords was used to obtain the articles. The PRISMA guidelines were used to filter eligible articles. The study design, sample size, type of AI software, and the results of each eligible study were analyzed. The CASP diagnostic study checklist was used to evaluate the evidence strength score.
Results: Seven articles were eligible for review according to the PRISMA guidelines. Out of the 7 eligible studies, 4 had strong CASP evidence strength scores (7-8/9). The remaining studies had intermediate CASP evidence strength scores (3.5-6.5/9). The highest area under the curve among the reported studies was 94%, the highest F1 score was 91%, and the highest specificity and sensitivity were 98.1% and 94%, respectively.
Conclusion: AI-based detection of periodontal bone loss using radiographs is an efficient method. However, more clinical studies need to be conducted before this method is introduced into routine dental practice.
目的:人工智能(AI)将在医学诊断中发挥重要作用。牙周病是最常见的口腔疾病之一。牙周病的早期诊断对于有效的治疗和良好的预后至关重要。本研究旨在通过放射学分析评估人工智能在诊断牙周骨丢失方面的有效性。材料和方法:文献检索涉及5个数据库(PubMed、ScienceDirect、Scopus、Health and Medical Collection、Dentistry and Oral Sciences)。使用特定的关键字组合来获得这些文章。PRISMA指南用于筛选符合条件的文章。对研究设计、样本量、人工智能软件类型以及每项合格研究的结果进行了分析。CASP诊断研究检查表用于评估证据强度评分。结果:根据PRISMA指南,有7篇文章符合审查条件。在7项符合条件的研究中,4项具有较强的CASP证据强度得分(7-8/9)。其余研究的CASP证据强度得分为中等(3.5-6.5/9)。报告研究中曲线下面积最高为94%,F1得分最高为91%,特异性和敏感性最高分别为98.1%和94%。结论:基于AI的牙周骨丢失X线片检测是一种有效的方法。然而,在将这种方法引入常规牙科实践之前,还需要进行更多的临床研究。