Introduction
Apert syndrome is an autosomal dominant malformation syndrome, accounting for 4.5% of all craniosynostoses. Raised intracranial pressure (ICP) in Apert syndrome has a multifactorial aetiology, with an incidence of up to 45% if left untreated [1, 2]. Raised ICP can be determined clinically, with non-invasive and invasive methods. In this study, we want to assess whether the use of CT scans is reliable in identifying changes in ICP.
Method
Pre and postoperative CT scans for 13 Apert syndrome patients who had posterior vault expansion were assessed and graded for severity of intracranial pressure (ICP). The grading system used was departmental specific and the assessment was carried out by a single clinician on different brain structures. This process was repeated on the same patients, using the same CT scans, 4 months later to determine consistency and repeatability. The relationship between the pre and postoperative scans was explored using the chi squared test. Intra-observer variability was assessed using Kappa statistics [SS1].
Results
There was no statistically significant difference between the pre and postoperative CT scan grading. Across instances, only one assessed structure had a p-value <0.05. The Kappa interobserver reliability test did not identify a strong agreement in the comparison of the two instances of data analysis.
Conclusions
Assessment of CT scans is not a reliable method to determine changes in intracranial pressure in Apert syndrome patients who have had a posterior vault expansion.
Many children born with ear microtia undergo reconstructive surgery for both aesthetic and functional purposes. This surgery is a delicate procedure that requires the surgeon to carve a "scaffold" for a new ear, typically from the patient's own rib cartilage. This is an unnecessarily invasive procedure, and reconstruction relies on the skill of the surgeon to accurately construct a scaffold that best suits the patient based on limited data. Work in stem-cell technologies and bioprinting present an opportunity to change this procedure by providing the opportunity to "bioprint" a personalised cartilage scaffold in a lab. To do so, however, a 3D model of the desired cartilage shape is first required. In this paper we optimise the standard convolutional mesh autoencoder framework such that, given only the soft tissue surface of an unaffected ear, it can accurately predict the shape of the underlying cartilage. To prevent predicted cartilage meshes from intersecting with, and protruding through, the soft tissue ear mesh, we develop a novel intersectionbased loss function. These combined efforts present a means of designing personalised ear cartilage scaffold for use in reconstructive ear surgery.
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