"Artificial intelligence-driven infection risk prediction in ventriculoperitoneal shunting: a novel approach for normal pressure hydrocephalus treatment".

Abstract

Idiopathic normal pressure hydrocephalus (iNPH) affects approximately 1.5% of the population, with a higher prevalence in men than women. Ventriculoperitoneal shunting (VPS) is the standard treatment for iNPH, but it poses a notable risk of infection, occurring in 8-10% of cases. Recent advancements in non-invasive diagnostic techniques, such as superb microvascular ultrasound (SMI), have demonstrated potential in evaluating cerebrospinal fluid (CSF) flow within VPS systems. A single-center feasibility study involving 19 asymptomatic patients with VPS systems showed that SMI reliably detected CSF flow in the proximal catheter in all patients and in the distal catheter in 89.5%, while reductions in optic nerve sheath diameter (ONSD) indicated lowered intracranial pressure after shunt activation. These findings suggest that SMI could serve as a safer alternative to invasive methods for assessing shunt function. Additionally, artificial intelligence (AI)-based approaches are being explored to reduce infection risk and enhance shunt efficacy. An artificial neural network (ANN) model achieved an 83.1% accuracy in predicting infection risk, surpassing traditional logistic regression models. However, the study's limitations, including its retrospective design, small sample size, and single-center nature, underscore the need for larger multi-center studies to confirm the generalizability of these findings. Further research is essential to validate the effectiveness of these innovations and their potential to improve patient outcomes in hydrocephalus management.