MUMBAI, India, Aug. 12 -- Intellectual Property India has published a patent application (202621066127 A) filed by Mrs. Nita Jayesh Mahale; Punam A. Patil; Rajshri Ingle; Mrs. Megha Swapnil Mane; Geeta Rahul Darunte; and Dr. Deepali Rajendra Sale on May 26, 2026, for Deep Learning-Based System For Automated Diagnosis Of Cardiovascular Disease Using Multisource Medical Data Integration.
Inventors include Mrs. Nita Jayesh Mahale; Punam A. Patil; Rajshri Ingle; Mrs. Megha Swapnil Mane; Geeta Rahul Darunte; and Dr. Deepali Rajendra Sale.
The application for the patent was published on August 07, 2026, under issue no. 32/2026.
Abstract: The present invention discloses a deep learning-based system for automated diagnosis of cardiovascular disease using multisource medical data integration. The proposed framework is designed to collect and analyze heterogeneous healthcare data obtained from ECG sensors, wearable IoT devices, cardiac imaging systems, laboratory reports, and electronic health records. The acquired medical information undergoes preprocessing operations including noise removal, normalization, segmentation, feature extraction, and dimensionality reduction to generate a structured multimodal dataset for intelligent healthcare analysis. The invention employs a hybrid deep learning architecture incorporating Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and Transformer-based models to extract spatial, temporal, and contextual healthcare features from various medical modalities. An adaptive multimodal fusion mechanism integrates the extracted features to perform cardiovascular disease classification, risk prediction, severity assessment, and anomaly detection with improved diagnostic accuracy. The system further includes cloud-edge deployment support, secure healthcare data transmission, encrypted storage, and explainable AI-based clinical decision support functionalities for generating automated diagnosis reports and predictive alerts. The proposed invention enhances early disease detection, reduces diagnostic errors, supports remote patient monitoring, and enables scalable deployment in hospitals, telemedicine systems, wearable healthcare devices, and smart healthcare infrastructures while ensuring secure and reliable healthcare data management.
Disclaimer: Curated by HT Syndication.
