Kaya Süheyla, Alkan Melisa
Department of Periodontology, Faculty of Dentistry, Istanbul University Cerrahpaşa, Istanbul, Türkiye.
Department of Orthodontics, Faculty of Dentistry, Istanbul Okan University, Istanbul, Türkiye.
BMC Oral Health. 2025 Jul 2;25(1):1024. doi: 10.1186/s12903-025-06390-8.
Gingival phenotype (GP) significantly influences periodontal health and treatment outcomes. Traditional methods for assessing GP, using gingival thickness (GT) alone, may lack sufficient accuracy for reliable GP classification. The present study aims to introduce and validate a novel non-invasive digital GP assessment measuring digital GT (dGT) and digital keratinized tissue width (dKTW). The primary objective is to assess the diagnostic performance of digital gingival phenotype (dGP) in distinguishing between thick and thin phenotypes.
This prospective, cross-sectional study was conducted at the Periodontology Department of Istanbul University-Cerrahpaşa, Turkey, from October to December 2024. Participants were included if they had all maxillary and mandibular anterior teeth present. Exclusion criteria included factors that could affect periodontal tissues, such as clinical attachment loss, systemic diseases (e.g., diabetes), gingival enlargement or recession, smoking, medications causing gingival hyperplasia, and melanin pigmentation. All subjects were screened for eligibility by S.K. prior to enrollment, with participants enrolled when S.K. was present at the periodontology department for preliminary examinations using a convenience sampling approach. KTW was assessed using clinical (cKTW), digital (dKTW), and rounded methods. cKTW and dKTW measured the distance between the gingival margin and mucogingival junction, while rounded KTW was calculated by rounding dKTW to the nearest whole number. GT was measured digitally in millimeters from the gingival margin level. GP was evaluated clinically (cGP) with a color-coded periodontal probe and digitally (dGP) by multiplying dKTW and dGT measurements. cKTW, dKTW, rounded KTW, dGT, and dGP are index tests, with cGP serving as the reference standard. The diagnostic accuracy of each method was evaluated using Receiver Operating Characteristic (ROC) analysis.
Out of 348 participants, 31 met the inclusion criteria. Since each participant's 12 teeth were evaluated, a total of 372 teeth were included in the study. The area under the curve (AUC) values and 95% confidence intervals (CI) for each method were as follows: dGT: 0.628 (95% CI: 0.570-0.687), cKTW: 0.730 (95% CI: 0.677-0.782), dKTW: 0.714 (95% CI: 0.661-0.767), rounded KTW: 0.710 (95% CI: 0.657-0.763), and dGP: 0.734 (95% CI: 0.683-0.785). The dGP model exhibited the highest diagnostic accuracy, while the dGT model showed the lowest.
The findings suggest that the digital gingival phenotype assessment provides superior diagnostic accuracy compared to other methods, achieving the highest AUC value. This demonstrates its efficacy in classifying GP and offers a reliable and accurate alternative to traditional clinical techniques for GP classification.
No trial registration was performed, as no invasive procedures were conducted in this study.
牙龈表型（GP）对牙周健康和治疗结果有显著影响。仅使用牙龈厚度（GT）来评估GP的传统方法，对于可靠的GP分类可能缺乏足够的准确性。本研究旨在引入并验证一种新型的非侵入性数字GP评估方法，测量数字牙龈厚度（dGT）和数字角化组织宽度（dKTW）。主要目的是评估数字牙龈表型（dGP）在区分厚型和薄型表型方面的诊断性能。
这项前瞻性横断面研究于2024年10月至12月在土耳其伊斯坦布尔大学-塞拉哈帕夏牙周病科进行。如果参与者上颌和下颌前牙全部存在，则纳入研究。排除标准包括可能影响牙周组织的因素，如临床附着丧失、全身性疾病（如糖尿病）、牙龈肿大或退缩、吸烟、导致牙龈增生的药物以及黑色素沉着。在入组前，所有受试者均由S.K.进行资格筛查，当S.K.在牙周病科进行初步检查时，采用便利抽样方法招募参与者。KTW采用临床（cKTW）、数字（dKTW）和四舍五入法进行评估。cKTW和dKTW测量牙龈边缘与膜龈联合之间的距离，而四舍五入后的KTW是通过将dKTW四舍五入到最接近的整数来计算的。GT以毫米为单位从牙龈边缘水平进行数字测量。GP通过使用颜色编码的牙周探针进行临床评估（cGP），并通过将dKTW和dGT测量值相乘进行数字评估（dGP）。cKTW、dKTW、四舍五入后的KTW、dGT和dGP为指标测试，以cGP作为参考标准。使用受试者工作特征（ROC）分析评估每种方法的诊断准确性。
在348名参与者中，31名符合纳入标准。由于对每位参与者的12颗牙齿进行了评估，因此本研究共纳入372颗牙齿。每种方法的曲线下面积（AUC）值和95%置信区间（CI）如下：dGT：0.628（95%CI：0.570-0.687），cKTW：0.730（95%CI：0.677-0.782），dKTW：0.714（95%CI：0.661-0.767），四舍五入后的KTW：0.710（95%CI：0.657-0.763），dGP：0.734（95%CI：0.683-0.7
