[Digital, DICOM, diagnostics-unity over chaos : Data communication in digital pathology].

Area of Science:

• Digital pathology
• Medical imaging
• Artificial intelligence in healthcare

Background:

• Digitization of histological specimens is crucial for advanced computer-assisted analysis and AI-driven diagnostics.
• Lack of standardization and interoperability between proprietary formats and clinical systems (PACS, LIS) hinders digital pathology adoption.
• The Digital Imaging and COmmunications in Medicine (DICOM) format offers a potential solution for integrating image data, metadata, and analysis results.

Purpose of the Study:

• To address the lack of standards in digital pathology.
• To demonstrate a practical implementation of DICOM-compliant workflows for whole slide images and AI results.
• To establish a foundation for standardized, transparent, and trustworthy digital pathology.

Main Methods:

• Literature review on digital and computer-assisted pathology, focusing on DICOM adoption.
• Development and implementation of an open-source, modular Docker pipeline.
• Demonstration of DICOM-compliant workflows for storing and visualizing whole slide images and AI results.

Main Results:

• A growing body of literature supports the use of DICOM in digital pathology.
• Successful practical implementation of DICOM-compliant workflows using an open-source pipeline.
• Demonstrated capability for storing and visualizing whole slide images and AI results in a standardized format.

Conclusions:

• DICOM is emerging as a key format for enabling interoperability in digital pathology.
• The developed Docker pipeline facilitates standardized, transparent, and trustworthy digital pathology.
• This work provides a basis for wider adoption of digital pathology solutions.