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额眶部隆起和三角头畸形的新诊断标准:一种 3D 几何方法。

New diagnostic criteria for metopic ridges and trigonocephaly: a 3D geometric approach.

机构信息

Service de chirurgie maxillofaciale et chirurgie plastique, Hôpital Necker - Enfants malades, Assistance Publique - Hôpitaux de Paris, CRMR CRANIOST, Faculté de Médecine, Université Paris Cité, Paris, France.

Institut de Biomécanique Humaine Georges Charpak, Arts et Métiers Institute of Technology, Paris, France.

出版信息

Orphanet J Rare Dis. 2024 May 18;19(1):204. doi: 10.1186/s13023-024-03197-8.

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.

CONCLUSION

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

三角头畸形是由于额骨正中缝过早融合导致的,表现为三角形的额头和两眼内距过近。这种情况通常需要手术矫正以改善形态和功能。额骨嵴也起源于额缝过早闭合,但仅与额部正中突出有关,很少需要手术矫正。这两种情况的鉴别诊断具有挑战性,尤其是轻度三角头畸形。

方法

收集了 207 例三角头畸形(90 例)、额骨嵴(27 例)和对照组(90 例)患者的 207 个头颅 CT 扫描图像。使用几何形态测量学来量化颅骨和眶部形态,以及额间角和头指数。开发了一种新的方法来自动计算额缝上的额部曲率。测试了不同的机器学习算法,以评估形态学数据在分类方面的预测能力。

结果

我们表明,对照组、三角头畸形和额骨嵴患者的颅骨和眶部形态具有显著差异。三维额部曲率可以清晰地区分各组(敏感性和特异性均>92%)。此外,当结合 6 个单变量测量值时,我们在组间鉴别中达到了 100%的准确率。

结论

提出并验证了两种诊断工具,用于辅助三角头畸形或额骨嵴患者的鉴别诊断。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。

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