PDF(Pubmed)
Abstract：
BACKGROUND: Trigonocephaly occurs due to the premature fusion of the metopic suture, leading to a triangular forehead and hypotelorism. This condition often requires surgical correction for morphological and functional indications. Metopic ridges also originate from premature metopic closure but are only associated with mid-frontal bulging; their surgical correction is rarely required. Differential diagnosis between these two conditions can be challenging, especially in minor trigonocephaly.
METHODS: Two hundred seven scans of patients with trigonocephaly (90), metopic rigdes (27), and controls (90) were collected. Geometric morphometrics were used to quantify skull and orbital morphology as well as the interfrontal angle and the cephalic index. An innovative method was developed to automatically compute the frontal curvature along the metopic suture. Different machine-learning algorithms were tested to assess the predictive power of morphological data in terms of classification.
RESULTS: We showed that control patients, trigonocephaly and metopic rigdes have distinctive skull and orbital shapes. The 3D frontal curvature enabled a clear discrimination between groups (sensitivity and specificity > 92%). Furthermore, we reached an accuracy of 100% in group discrimination when combining 6 univariate measures.
CONCLUSIONS: Two diagnostic tools were proposed and demonstrated to be successful in assisting differential diagnosis for patients with trigonocephaly or metopic ridges. Further clinical assessments are required to validate the practical clinical relevance of these tools.
METHODS: Two hundred seven scans of patients with trigonocephaly (90), metopic rigdes (27), and controls (90) were collected. Geometric morphometrics were used to quantify skull and orbital morphology as well as the interfrontal angle and the cephalic index. An innovative method was developed to automatically compute the frontal curvature along the metopic suture. Different machine-learning algorithms were tested to assess the predictive power of morphological data in terms of classification.
RESULTS: We showed that control patients, trigonocephaly and metopic rigdes have distinctive skull and orbital shapes. The 3D frontal curvature enabled a clear discrimination between groups (sensitivity and specificity > 92%). Furthermore, we reached an accuracy of 100% in group discrimination when combining 6 univariate measures.
CONCLUSIONS: Two diagnostic tools were proposed and demonstrated to be successful in assisting differential diagnosis for patients with trigonocephaly or metopic ridges. Further clinical assessments are required to validate the practical clinical relevance of these tools.
摘要：
背景：由于异位缝合线的过早融合而发生三头畸形，导致三角形的前额和下等。这种情况通常需要针对形态和功能适应症进行手术矫正。异位脊也起源于过早的异位闭合，但仅与中额叶隆起有关；很少需要手术矫正。这两种情况之间的鉴别诊断可能具有挑战性，尤其是在小三角头。
方法：对三头畸形患者进行了27次扫描（90），变位rigdes(27)，和对照组（90）被收集。几何形态计量学用于量化颅骨和眼眶形态以及额间角和头部指数。开发了一种创新的方法来自动计算沿着异位缝合线的正面曲率。测试了不同的机器学习算法，以评估形态数据在分类方面的预测能力。
结果：我们显示对照组患者，三角头颅和异位脊具有独特的头骨和眼眶形状。3D额叶曲率能够在组间进行清晰的区分(灵敏度和特异性>92%)。此外，当组合6项单变量测量时,我们在组辨别中的准确率达到100%.
结论：提出了两种诊断工具，并证明其在帮助患有三角头脊或异位脊的患者的鉴别诊断方面是成功的。需要进一步的临床评估来验证这些工具的实际临床相关性。
方法：对三头畸形患者进行了27次扫描（90），变位rigdes(27)，和对照组（90）被收集。几何形态计量学用于量化颅骨和眼眶形态以及额间角和头部指数。开发了一种创新的方法来自动计算沿着异位缝合线的正面曲率。测试了不同的机器学习算法，以评估形态数据在分类方面的预测能力。
结果：我们显示对照组患者，三角头颅和异位脊具有独特的头骨和眼眶形状。3D额叶曲率能够在组间进行清晰的区分(灵敏度和特异性>92%)。此外，当组合6项单变量测量时,我们在组辨别中的准确率达到100%.
结论：提出了两种诊断工具，并证明其在帮助患有三角头脊或异位脊的患者的鉴别诊断方面是成功的。需要进一步的临床评估来验证这些工具的实际临床相关性。
