Vortkamp Paulina, Rues Stefan, Räther Sven, Eberhard Lydia, Rößler Alexander, Bouffleur Frederic, Kühle Reinald, Schwindling Franz Sebastian, Rammelsberg Peter, Roser Christoph J, Herpel Christopher
Department of Prosthodontics, University of Heidelberg, Heidelberg, Germany.
Department of Oral and Maxillofacial Surgery, University of Heidelberg, Heidelberg, Germany.
J Oral Rehabil. 2025 Sep;52(9):1369-1376. doi: 10.1111/joor.14002. Epub 2025 May 4.
Since 1949, occlusal contact area (OCA) has been recognised as an indicator of masticatory performance (MP), but it required individual and laborious analog measurement. Today, a digital workflow could provide automatic quantification.
To find optimal parameters for a digital evaluation in terms of maximising correlation between OCA and MP.
MP was measured clinically by comminution of standardised test food by 41 participants (mean age = 29, n female = 28), including 18 patients with dentofacial deformities and 23 healthy subjects with normal occlusion. OCA was measured in the laboratory. After impression taking, gypsum casts were digitised (D2000, 3shape) and aligned in maximum intercuspidation (Geomagic Design X2022, 3D Systems). The maxilla was enlarged with surface offsets of 100, 150, 200, 250, 300, 350, 400, 800 and 2000 μm to simulate different interocclusal distances. OCA was identified at the mandible surface by intersection with the respective enlarged maxilla scan (3D surface area). OCA projection onto the occlusal plane (2D area) was also computed, resulting in a total of 18 different OCA evaluations per patient.
MP was lower in patients with malocclusion than in individuals with normal occlusion (X50 mean 5.35 vs. 4.62). The 18 mean OCAs ranged from 11 to 852 mm. In subjects with normal occlusion, strong correlations were shown for interocclusal distances between 150 and 300 μm combined with 3D projection (r < -0.7). For patients with malocclusion, no significant correlations were identified.
OCA appears a suitable indicator of MP in individuals with normal occlusion, but not in patients with malocclusion, where other factors seem more determinant.
自1949年以来，咬合接触面积（OCA）一直被视为咀嚼性能（MP）的一个指标，但它需要进行个体且费力的模拟测量。如今，数字化工作流程可以实现自动量化。
为了找到数字评估的最佳参数，以最大化OCA与MP之间的相关性。
41名参与者（平均年龄=29岁，女性28名），包括18名牙颌面畸形患者和23名咬合正常的健康受试者，通过粉碎标准化测试食物对咀嚼性能进行临床测量。在实验室中测量OCA。取模后，将石膏模型数字化（D2000，3shape）并在最大牙尖交错位对齐（Geomagic Design X2022，3D Systems）。通过100、150、200、250、300、350、400、800和2000μm的表面偏移量扩大上颌，以模拟不同的咬合间隙。通过与相应扩大的上颌扫描（3D表面积）相交，在下颌表面识别OCA。还计算了OCA在咬合平面上的投影（2D面积），每位患者总共得到18种不同的OCA评估。
错牙合患者的咀嚼性能低于咬合正常者（X50平均值5.35对4.62）。18个平均OCA范围为11至852mm。在咬合正常的受试者中，150至300μm的咬合间隙与3D投影显示出强相关性（r < -0.7）。对于错牙合患者，未发现显著相关性。
OCA似乎是咬合正常个体咀嚼性能的合适指标，但对错牙合患者则不然，在错牙合患者中其他因素似乎更具决定性。
