MUMBAI, India, Sept. 14 -- Intellectual Property India has published a patent application (202641108139 A) filed by Pragati Engineering College on September 09, 2026, for Ai-Based Smart Iv Drip Monitoring And Vein Infiltration Prevention System.
Inventors include Mrs. N. V. S. Sowjanya; Mr. Krishna Chaitanya Pidugu; Mr. K. Chakravarthy Bheri; and Mr. K. V. S. Akhil.
The application for the patent was published on September 11, 2026, under issue no. 37/2026.
Abstract: IV therapy is one of the most frequently employed medical treatments used to supply the body with liquids, medicines, nutrients and blood. The drawback is that traditional IV therapy monitoring requires frequent observation by a healthcare provider which is less efficient and hinders early detection of vein infiltrations, extravasations, abnormal flow rate of drip, no fluid in the bottle, air bubble in the line, blockage in line and dislodging of cannula which if left undiagnosed may cause tissue damages, leakages, infusion interruptions, patient’s distress, longer hospitalization duration and extra health expenses. Hence there is a need for intelligent, autonomous and continuous IV monitoring system capable of predicting potential issues beforehand. The proposed invention is an AI-Based Smart IV Drip Monitoring and Vein Infiltration Prevention System that effectively combines the Internet of Things (IoT), Artificial Intelligence (AI), cloud computing, edge computing and multiple biomedical sensors. It creates a unified healthcare platform for the intelligent and secure monitoring of IV drip using AI to predict issues beforehand. In this method, an ESP32 based edge controller is used to collect real time data from sensors such as drip rate sensors, IV fluid level sensors, skin temperature sensors, tissue swelling sensors, air bubble sensors, tube blockage sensors and cannula displacement sensors, preprocess them and transmit it to the AI prediction engine where the data is analyzed and abnormal infusion patterns are detected and the risk of vein infiltration is predicted using Machine Learning (ML) algorithms. The results are then uploaded to the cloud platform for monitoring and displaying in a mobile app and web interface for remote patients. The system keeps track of the medical history of the patient and provides an overview of their current state by maintaining real-time visualization and aiding the doctors to take decision for proper patient care by assessing risk and sending automated notifications. This system allows continuous prediction of potential complications of infusion therapy, reduces dependency on human monitoring, minimizes complications caused by infusions and improves the safety of the patient in various healthcare facilities like hospitals, ICUs, emergency departments, rehabilitation centres, homecare, thereby laying the foundation for next-generation smart intravenous therapy management systems.
Disclaimer: Curated by HT Syndication.
