MUMBAI, India, July 7 -- Intellectual Property India has published a patent application (202641079321 A) filed by Sabah Afroze; Dr. Tamil Selvi; and Dr. Parisa Beham on June 27, 2026, for Adaptive Digital Twin-Based Oncological Monitoring System Using Sequential Histopathology Imaging.
Inventors include Sabah Afroze; Dr. Tamil Selvi; and Dr. Parisa Beham.
The application for the patent was published on July 03, 2026, under issue no. 27/2026.
Abstract: The present invention relates to an Adaptive Digital Twin-Based Oncological Monitoring System Using Sequential Histopathology Imaging, designed to provide continuous, patient-specific monitoring of cancer progression, treatment response, recurrence risk, and disease evolution through advanced computational pathology and digital twin technologies. Cancer is a highly dynamic disease characterized by temporal changes in tumor morphology, cellular architecture, molecular signaling pathways, and microenvironment interactions. Conventional pathology assessments are often limited to isolated tissue examinations and may not adequately capture longitudinal disease evolution. The proposed invention establishes a continuously evolving digital twin of a cancer patient by integrating sequential histopathological images, molecular biomarkers, genomic profiles, treatment history, radiological findings, and clinical outcomes into a unified computational framework. Advanced image processing algorithms extract quantitative pathological features including nuclear morphology, mitotic activity, tumor heterogeneity, stromal remodeling, angiogenesis, immune cell infiltration, and tissue architecture changes from serial biopsy and surgical specimens. Machine learning and deep learning models analyze temporal feature evolution to simulate disease trajectories and predict future oncological events. The adaptive digital twin continuously updates itself with newly acquired patient data, enabling real-time monitoring of treatment effectiveness, progression patterns, metastatic potential, and recurrence likelihood. The system further incorporates predictive analytics, explainable artificial intelligence, cloud-based healthcare integration, and clinical decision support functionalities. Automated alerts notify healthcare providers when significant deviations from expected treatment responses are detected. The invention enhances precision oncology by enabling personalized monitoring strategies, supporting evidence-based treatment modifications, improving prognostic accuracy, reducing diagnostic uncertainty, and facilitating early intervention. Consequently, the invention provides an intelligent, scalable, and non-invasive platform for comprehensive oncological monitoring and long-term cancer management across diverse clinical settings.
Disclaimer: Curated by HT Syndication.
