The Possibility of Intracranial Hypertension in Patients with Autism Spectrum Disorder Using Computed Tomography.
Shuichi YamadaIchiro NakagawaFumihiko NishimuraYasushi MotoyamaYoung-Soo ParkHiroyuki NakasePublished in: Journal of clinical medicine (2020)
Although intracranial pressure is considered to be normal in children with autism spectrum disorder (ASD), we aimed to assess whether such children may have increased intracranial pressure using noninvasive computed tomography (CT). Head CT scans of children with ASD (109 cases, male 91 and female 18, average age 4.3 years) and of children with typical development (60 cases, male 35 and female 25, average age 4.5 years) were acquired. The images were processed to map the shape of the inner skull surface. We predicted that a complex skull shape, based on a marked digital impression, would be indicative of chronically increased intracranial pressure. The data of the scans were extracted and processed to automatically establish inner and outer cranial circumferences. The circularity (reflecting inner skull shape and area) and C-ratio (ratio of inner/outer circumference) were determined and statistically analyzed. The circularity and C-ratio were significantly lower in children with ASD than in children with typical development. A lower circularity was associated with a more complex shape of the inner skull surface, which indicated the presence of intracranial hypertension. Our study suggests that children with ASD may be at a risk for chronic intracranial hypertension. Our technique incorporating the circularity and C-ratio is a useful noninvasive method for screening such patients and could impact future investigations of ASD.
Keyphrases
- computed tomography
- autism spectrum disorder
- young adults
- blood pressure
- optic nerve
- positron emission tomography
- attention deficit hyperactivity disorder
- newly diagnosed
- dual energy
- ejection fraction
- body mass index
- intellectual disability
- machine learning
- prognostic factors
- physical activity
- image quality
- artificial intelligence
- patient reported outcomes
- big data
- patient reported
- magnetic resonance
- body weight