Tariq Asmhan, Nakhi Fatmah Bin, Salah Fatema, Eltayeb Gabass, Abdulla Ghada Jassem, Najim Noor, Khedr Salma Ahmed, Elkerdasy Sara, Al-Rawi Natheer, Alkawas Sausan, Mohammed Marwan, Shetty Shishir Ram
Department of Oral and Craniofacial Health Sciences, College of Dental Medicine, University of Sharjah, Sharjah, United Arab Emirates.
Imaging Sci Dent. 2023 Sep;53(3):193-198. doi: 10.5624/isd.20230092. Epub 2023 Aug 2.
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.
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.
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.
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.
人工智能（AI）有望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治疗和良好预后至关重要。本研究旨在通过影像学分析评估人工智能在诊断牙周骨丧失方面的有效性。
对5个数据库（PubMed、ScienceDirect、Scopus、健康与医学数据库、牙科学与口腔科学数据库）进行文献检索。使用特定的关键词组合来获取文章。采用PRISMA指南筛选符合条件的文章。分析研究设计、样本量、人工智能软件类型以及每项符合条件研究的结果。使用CASP诊断研究清单评估证据强度得分。
根据PRISMA指南，有7篇文章符合综述要求。在这7项符合条件的研究中，4项具有较高的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）。在已报道的研究中，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%。
基于人工智能利用X线片检测牙周骨丧失是一种有效的方法。然而，在将该方法引入常规牙科实践之前，还需要进行更多的临床研究。
