Clinical database connection with cervix radiomic tools

Abstract

The early detection and diagnosis of cervical cancer relies heavily on medical imaging techniques such as colposcopy and Pap smear tests. With the increasing volume of cervix images generated in clinical settings, an efficient, secure, and AIdriven database system is essential for managing and analyzing these images. This study proposes the design of a scalable and structured relational database for storing cervix images, clinical data and report integrating database tools with web/ mobil radiomic tool to enhance image lesion segmentation, classification, and predictive analytics. The proposed system incorporates a hybrid approach, combining relational database management for structured metadata with cloud-based storage for large-scale image datasets connected with radiomic tools. The system ensures compliance with medical data privacy regulations through access control, regular backups mechanisms, and audit logs. By enabling efficient data retrieval, AI-assisted diagnostics, and interoperability with electronic health records (EHR), this database framework aims to enhance cervical cancer screening programs, reduce diagnostic delays, and improve patient outcomes.

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