MUMBAI, India, Jan. 2 -- Intellectual Property India has published a patent application (202541123319 A) filed by Dayananda Sagar Academy Of Technology And Management; Dr Shivaprasad Ashok Chikop; Aruna Kanki; Bathineni Pranathi; Halvi Sai Vineela; and Neha R, Bengaluru, Karnataka, on Dec. 7, 2025, for 'non-invasive blood group detection system using fingerprint analysis and deep learning.'
Inventor(s) include Dayananda Sagar Academy Technology And Management; Dr Shivaprasad Ashok Chikop; Aruna Kanki; Bathineni Pranathi; Halvi Sai Vineela; and Neha R.
The application for the patent was published on Jan. 2, under issue no. 01/2026.
According to the abstract released by the Intellectual Property India: "The present invention relates to a non-invasive blood group detection system utilizing fingerprint image analysis and deep learning techniques to eliminate the need for blood sample collection and laboratory testing. The system comprises a fingerprint acquisition module for capturing images through scanners or cameras, an advanced preprocessing module implementing CLAHE enhancement, noise reduction, and normalization to improve ridge pattern clarity, a Convolutional Neural Network (CNN) architecture with multiple convolutional, pooling, dropout, and dense layers for automatic feature extraction and blood group classification, and a real-time web-based interface enabling instant image upload, rapid processing, and immediate display of predicted blood group with confidence scores. The CNN model is trained on labeled fingerprint datasets using data augmentation and supervised learning to classify fingerprints into eight blood group categories: A+, A-, B+, B-, AB+, AB-, O+, and O-. The invention provides a rapid, portable, cost-effective, and user-friendly alternative to traditional serological methods, suitable for emergency medical scenarios, remote healthcare facilities, disaster management, forensic applications, and resource-constrained environments. The system achieves high accuracy through optimized preprocessing pipelines and deep learning architectures while maintaining computational efficiency for real-time inference, representing a significant advancement in non-invasive medical diagnostics and biometric analysis applications."
Disclaimer: Curated by HT Syndication.
