Background: Evaluation of surgical treatment for craniosynostosis is typically based on subjective visual assessment or simple clinical metrics of cranial shape that are prone to interobserver variability. Three-dimensional photography provides cheap and noninvasive information to assess surgical outcomes, but there are no clinical tools to analyze it. The authors aim to objectively and automatically quantify head shape from three-dimensional photography. Methods: The authors present an automatic method to quantify intuitive metrics of local head shape from three-dimensional photography using a normative statistical head shape model built from 201 subjects. The authors use these metrics together with a machine learning classifier to distinguish between patients with (n = 266) and without (n = 201) craniosynostosis (aged 0 to 6 years). The authors also use their algorithms to quantify objectively local surgical head shape improvements on 18 patients with presurgical and postsurgical three-dimensional photographs. Results: The authors’ methods detected craniosynostosis automatically with 94.74 percent sensitivity and 96.02 percent specificity. Within the data set of patients with craniosynostosis, the authors identified correctly the fused sutures with 99.51 percent sensitivity and 99.13 percent specificity. When the authors compared quantitatively the presurgical and postsurgical head shapes of patients with craniosynostosis, they obtained a significant reduction of head shape abnormalities (p < 0.05), in agreement with the treatment approach and the clinical observations. Conclusions: Quantitative head shape analysis and three-dimensional photography provide an accurate and objective tool to screen for head shape abnormalities at low cost and avoiding imaging with radiation and/or sedation. The authors’ automatic quantitative framework allows for the evaluation of surgical outcomes and has the potential to detect relapses. CLINICAL QUESTION/LEVEL OF EVIDENCE: Diagnostic, I.

No abstract

Reported are the cases of three hydrocephalic patients who developed a clinically heterogenous entity with an allergic rejection of their silicone ventriculoperitoneal shunts. All of the patients had an original presentation indicative of a shunt infection, but laboratory analysis revealed sterile cerebrospinal fluid in all three cases. The typical course included recurrent skin breakdowns over the shunt tract, subsequent infections and development of fungating granulomas. Treatment, with successful resolution of the symptoms, included changing the shunt material from silicone to polyurethane, with immunosuppression in one patient and removal of the shunt altogether in the other two patients. The roles of the immune system and silicone in the pathophysiology of this condition are discussed.

Metopic craniosynostosis is a condition caused by the premature fusion of the metopic cranial suture. If untreated, it can result into brain growth restriction, increased intra-cranial pressure, visual impairment, and cognitive delay. Fronto-orbital advancement is the widely accepted surgical approach to correct cranial shape abnormalities in patients with metopic craniosynostosis, but the outcome of the surgery remains very dependent on the expertise of the surgeon because of the lack of objective and personalized cranial shape metrics to target during the intervention. We propose in this paper a locally affine diffeomorphic surface registration framework to create an optimal interventional plan personalized to each patient. Our method calculates the optimal surgical plan by minimizing cranial shape abnormalities, which are quantified using objective metrics based on a normative model of cranial shapes built from 198 healthy cases. It is guided by clinical osteotomy templates for fronto-orbital advancement, and it automatically calculates how much and in which direction each bone piece needs to be translated, rotated, and/or bent. Our locally affine framework models separately the transformation of each bone piece while ensuring the consistency of the global transformation. We used our method to calculate the optimal surgical plan for 23 patients, obtaining a significant reduction of malformations (p < 0.001) between 40.38% and 50.85% in the simulated outcome of the surgery using different osteotomy templates. In addition, malformation values were within healthy ranges (p > 0.01).

We describe the clinicopathologic features of an Epstein-Barr virus (EBV)-associated smooth muscle tumor arising in the basal ganglia of a 10-year-old human immunodeficiency virus (HIV)-positive child. Only a few cases of intracranial smooth muscle tumors are reported in the literature and virtually all of these have been extra-axial, involving the dura or sinuses in HIV+ adults. Our case underscores the need to include an EBV-associated smooth muscle tumor in the differential diagnosis when evaluating intracranial mass lesions in immunodeficient children.

Twenty-eight currently or recently employed adults with concealable impairments from a community in the United States completed semi-structured interviews to capture workers' perceptions of their internal and social experiences that contribute to their identity management decisions. Disability identity management strategies included effortful behaviors, such as avoiding relevant situations and using specific language to describe impairments. Participants suggested intraindividual factors (disability salience, disability strain) and environmental factors (disability stigma, ineffective social support) as primary reasons for their identity management decisions. The findings align with existing research on stigmatized identity management and consequences for well-being, together informing a proposed theoretical model. Conclusions invite policy change at national and organizational levels to account for intraindividual and environmental factors that may introduce barriers to disability disclosure and effective accommodation practices.

No abstract

3D photography offers non-invasive, radiation-free, and anesthetic-free evaluation of craniofacial morphology. However, intracranial volume (ICV) quantification is not possible with current non-invasive imaging systems in order to evaluate brain development in children with cranial pathology. The aim of this study is to develop an automated, radiation-free framework to estimate ICV. Pairs of computed tomography (CT) images and 3D photographs were aligned using registration. We used the real ICV calculated from the CTs and the head volumes from their corresponding 3D photographs to create a regression model. Then, a template 3D photograph was selected as a reference from the data, and a set of landmarks defining the cranial vault were detected automatically on that template. Given the 3D photograph of a new patient, it was registered to the template to estimate the cranial vault area. After obtaining the head volume, the regression model was then used to estimate the ICV. Experiments showed that our volume regression model predicted ICV from head volumes with an average error of 5.81 ± 3.07% and a correlation (R2) of 0.96. We also demonstrated that our automated framework quantified ICV from 3D photography with an average error of 7.02 ± 7.76%, a correlation (R2) of 0.94, and an average estimation error for the position of the cranial base landmarks of 11.39 ± 4.3mm.