2025 Asian Conference on Communication and Networks (ASIANComNet 2025)

Currently, data from Hitachi machines' Digital Imaging and Communications in Medicine (DICOM) reports is used manually, which is time-consuming and error-prone. This limits the effectiveness of the analyses needed to manage radiation doses and improve medical practices. This paper therefore proposes an automated solution for the rapid and reliable extraction of key information, including CTDI (Computed Tomography Dose Index) and PDL (Product of Dose Length) indices, to facilitate the determination of Diagnostic Reference Levels (DRL). The methodology combines several steps: collecting real data from Picture Archiving and Communication System (PACS) reports; studying the structure of DICOM reports; developing an extraction algorithm in Python; and visualising the results via a web interface. The solution's architecture is based on the Django framework for data processing and Angular for the interactive presentation of results. The results obtained demonstrate the effectiveness of the platform in automating the extraction and analysis of DICOM data, significantly reducing processing time while providing healthcare professionals with clear, intuitive visualisation. This solution represents a significant advance in radiation dose management, transitioning from manual processes to an automated system that is faster and aligns with international radiation protection recommendations.

DICOM, DICOM SR, DRL