Sensitivity and Specificity of Different Diagnostic Methods in Occlusal Caries Detection of Permanent Teeth among Paediatric Patients

IF 0.1 Q4 DENTISTRY, ORAL SURGERY & MEDICINE
I. Mokhtar, A. Venkiteswaran, M.Y.P. Mohd Yusof
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引用次数: 0

Abstract

Dental caries is a commonly progressive disease that proceeds through various degrees of severity that a dentist can detect. The aims of the in vivo study were to assess the accuracy of the individual model (near-infrared light transillumination [NILT] device, visual and radiographic examinations) in detecting occlusal caries, and to evaluate the performance of visual and NILT device combination for occlusal caries detection in deciding the treatment options. Fifty-two non-cavitated occlusal surfaces from 16 patients were assessed with three different diagnostic devices in random order. Identified lesions were prepared and validated. Logistic regression analysis was performed for each method. The sensitivity and specificity values for each method and the combined models were statistically measured using RStudio version 0.97.551. At the enamel level, visual detection was the most sensitive method (0.88), while NILT was the most specific (0.93). NILT scored the highest for sensitivity (0.93) at the dentine level and visual detection scored the highest for specificity (0.88). Visual detection + NILT model was significantly better (p = 0.04) compared to visual detection or NILT alone (df = 1). The visual-NILT combination is a superior model in detecting occlusal caries on permanent teeth. The model provided surplus value in caries detection hence improving the treatment decision-making in occlusal surfaces.
不同诊断方法对儿童恒牙龋病检测的敏感性和特异性
龋齿是一种常见的进行性疾病,其严重程度各不相同,牙医都能检测到。体内研究的目的是评估个体模型(近红外光透射[NILT]装置,视觉和放射检查)检测咬合龋齿的准确性,并评估视觉和NILT装置组合检测咬合龋齿的性能,以决定治疗方案。对16例患者的52个非空化咬合面采用三种不同的诊断设备随机排序进行评估。准备并验证已确定的病变。对每种方法进行Logistic回归分析。采用RStudio 0.97.551版本统计各方法及组合模型的敏感性和特异性值。在牙釉质水平,目测是最敏感的方法(0.88),而NILT是最特异的方法(0.93)。在牙本质水平上,NILT的灵敏度最高(0.93),视觉检测的特异性最高(0.88)。视觉检测+ NILT模型明显优于单纯视觉检测或NILT模型(p = 0.04) (df = 1)。视觉-NILT联合检测恒牙合龋是较好的模型。该模型为龋病检测提供了剩余价值,从而改善了牙合面治疗决策。
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来源期刊
Archives of Orofacial Science
Archives of Orofacial Science DENTISTRY, ORAL SURGERY & MEDICINE-
CiteScore
0.30
自引率
50.00%
发文量
27
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