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 sensor-based 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.