Introduction Apert, Crouzon, and Pfeiffer syndromes are common genetic syndromes related to syndromic craniosynostosis (SC), whereby it is a congenital defect that occurs when the cranial growth is distorted. Identifying cranial angles associated with these 3 syndromes may assist the surgical team to focus on a specific cranial part during the intervention planning, thus optimizing surgical outcomes and reducing potential morbidity. Objective The aim of this study is to identify the cranial angles, which are associated with Apert, Crouzon, and Pfeiffer syndromes. Methods The cranial computed tomography scan images of 17 patients with SC and 22 control groups aged 0 to 12 years who were treated in the University Malaya Medical Centre were obtained, while 12 angular measurements were attained using the Mimics software. The angular data were then divided into 2 groups (patients aged 0 to 24 months and >24 months). This work proposes a 95% confidence interval (CI) for angular mean to detect the abnormality in patient's cranial growth for the SC syndromes. Results The 95% CI of angular mean for the control group was calculated and used as an indicator to confirm the abnormality in patient's cranial growth that is associated with the 3 syndromes. The results showed that there are different cranial angles associated with these 3 syndromes. Conclusions All cranial angles of the patients with these syndromes lie outside the 95% CI of angular mean of control group, indicating the reliability of the proposed CI in the identification of abnormality in the patient's cranial growth.
Single-linkage is one of the algorithms in agglomerative clustering technique that can be used to detect outliers. The single-linkage algorithm combines two clusters with the closest pair of observations. Then, the clusters are combined into larger clusters, until all the observations are formed in the same cluster. In this study, a single-linkage algorithm method that utilised a circular distance based on the City-block distance as the similarity distance is used. The performance of the method in detecting multiple outliers for a circular regression model is tested via simulation studies with three different outlier scenarios which are outliers in u-space only, v-space only and both uv-space. The performance is measured by calculating the "success" probability (pout), masking error (pmask) and swamping error (pswamp) for both outlier scenarios. It is found that the single linkage method performed well in detecting outliers for both outlier scenarios and applicable for circular regression model.
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