Ai-Driven Dense Neural Network and IOMT for Hydrocephalus Diagnosis and Management in Healthcare

In the field of medicine, a general hydrocephalus
a condition marked by aberrant accumulation of cerebrospinal
fluid in the brain—present major diagnosis and management
challenges. Preventing major complications including cognitive
decline and motor dysfunction depends absolutely on early
identification and continuous monitoring. There is great need
for innovative technologies free of any invasive procedures since
conventional diagnosis methods rely on invasive procedures and
are prone to delays in intervention. Using a Dense Neural
Network (DNN), this work addresses these challenges by
combining artificial intelligence (AI) with the Internet of
Medical Things (IoMT). Devices enabled by IoMT can
constantly gather patient data including imaging results,
physiological parameters, and intracranial pressure. The DNN
is concurrently managing these multimodal inputs to generate
fast and accurate diagnosis results. MRI images and sensorbased
real-time data comprised the dataset used for proposed
system testing. Comprising 3,500 patient records taken from
several medical institutions, the dataset consisted in The
diagnosis of hydrocephalus reveals the impressive performance
of the system, which reached an accuracy of 98.7%, a sensitivity
of 97.9%, and a specificity of 99.2%. Comparatively to more
conventional methods, predictive analytics also proved able to
identify critical thresholds for timely surgical intervention, so
reducing the rate of complications.
Keywords - Hydrocephalus, Dense Neural Network, Internet
of Medical Things, AI in healthcare, Real-time monitoring.