Multiple Machine Learning Approaches for Morphometric Parameters in Prediction of Hydrocephalus

Abstract

Background: The diagnosis of hydrocephalus is mainly based on imaging findings. However, the significance of many imaging indicators may change, especially in some degenerative diseases, and even lead to misdiagnosis. Methods: This study explored the effectiveness of commonly used morphological parameters and typical radiographic findings in hydrocephalus diagnosis. The patients’ imaging data were divided into three groups, including the hydrocephalus group, the symptomatic group, and the normal control group. The diagnostic validity and weight of various parameters were compared between groups by multiple machine learning methods. Results: Our results demonstrated that Evans’ ratio is the most valuable diagnostic indicator compared to the hydrocephalus group and the normal control group. But frontal horns’ ratio is more useful in diagnosing patients with symptoms. Meanwhile, the sign of disproportionately enlarged subarachnoid space and third ventricle enlargement could be effective diagnostic indicators in all situations. Conclusion: Both morphometric parameters and radiological features were essential in diagnosing hydrocephalus, but the weights are different in different situations. The machine learning approaches can be applied to optimize the diagnosis of other diseases and consistently update the clinical diagnostic criteria.

Early Shunting Promotes Sustained Improvement of Patients with Idiopathic Normal Pressure Hydrocephalus: A Multicenter Retrospective Study

“…Furthermore, early screening and diagnosis of iNPH are critical to the patient's prognosis. In the absence of a known secondary cause of hydrocephalus, iNPH is a diagnosis of exclusion and requires careful consideration of the neurodegenerative causes of dementia, especially neurodegenerative dementias that also involve signi cant early gait impairment, such as dementia with Lewy bodies (DLB), and progressive supranuclear palsy (PSP) [31]. Since the ventricles are enlarged with age, it is not uncommon to suspect iNPH in these elderly patients with dementia.…”

A predictive model in patients with chronic hydrocephalus following aneurysmal subarachnoid hemorrhage: a retrospective cohort study

“…Analyzing neuroimages for structural changes related to hydrocephalus can be accomplished through morphometric analysis. The study focused on traditional radiographic characteristics such as enlarged subarachnoid space, dilated temporal horns, and dilated third ventricle ( 11 ). In our research, various commonly used radiographic parameters were collected and compared, including frontal horn length, lateral length, Evans’ ratio, sellar bone distortion, empty sella [defined as at least 66% pituitary height loss ( 5 )], cerebrospinal fluid (CSF) extravasation ( 12 ), third ventricle width ( 13 ), callosal angle, and tentorial angle ( 14 , 15 ).…”