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Ratings and Reviews 0 Ratings
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What is dcmage?
dcmage is a versatile DICOM workbench that facilitates metadata modification, study de-identification, and volumetric data analysis. It operates seamlessly on native Mac applications as well as within web browsers, allowing users to process DICOM studies locally, which promotes both privacy and efficiency.
The tag editor feature equips users with the ability to search for any DICOM tag using its number, keyword, or value, effectively covering nested sequences and private tags, while inline checks quickly highlight invalid value representations. Any modifications are saved to a new file, preserving the integrity of the original data.
For de-identification, dcmage utilizes DICOM PS3.15 profiles in line with HIPAA Safe Harbor standards and allows users to set custom rules, ensuring consistent UID remapping throughout various studies. Furthermore, its on-device OCR functionality can detect text within images, enabling users to review and obscure sensitive information as necessary.
The viewer component provides multiple viewing angles, such as axial, sagittal, and coronal MPR, complete with synchronized crosshairs and tools for adjusting window/level settings, zooming, and taking precise measurements.
An audit trail feature systematically records every edit, anonymization, and export using a SHA-256 hash-chained methodology, which can be exported for documentation, simplifying the tracking of changes.
Designed specifically for research, data management, and technical assessments, dcmage emphasizes its function as a robust tool rather than a medical device, giving researchers and professionals the ability to manage DICOM data with heightened control and security. Its comprehensive feature set further enhances the efficiency and reliability of DICOM data handling in various professional environments.
What is AnalyticDiD?
Safeguarding sensitive information, particularly personally identifiable information (PII), through strategies such as pseudonymization and anonymization is crucial for secondary applications like comparative effectiveness research, policy assessments, and studies within the life sciences. As organizations gather large amounts of business data to discern patterns, gauge customer preferences, and stimulate innovation, the significance of this process grows. Adherence to legal frameworks like HIPAA and GDPR requires that data be stripped of identifying details; however, many current de-identification solutions focus primarily on eliminating personal identifiers, which can compromise the usability of the data. Transforming PII into formats that preclude identification while ensuring privacy is essential, and effectively utilizing data anonymization and pseudonymization techniques is key to this endeavor. This approach facilitates thorough data analysis without violating privacy laws. It is crucial to carefully choose suitable methods and privacy models from the broad domains of data de-identification and statistics to achieve effective data de-identification. Additionally, ongoing assessment of the evolving landscape of privacy standards is necessary to maintain compliance. Ultimately, achieving an equilibrium between data usability and the protection of privacy is essential for ethical data management practices.
Media
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Integrations Supported
Additional information not provided
Integrations Supported
Additional information not provided
API Availability
API Availability
Pricing Information
Pricing not provided
Free Version
Pricing Information
Pricing not provided
Supported Platforms
SaaS
Mac
On-Prem
Supported Platforms
SaaS
Customer Service / Support
Web-Based Support
Customer Service / Support
Standard Support
Web-Based Support
Training Options
Documentation Hub
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Company Facts
Organization Name
dcmage
Company Website
dcmage.com
Company Facts
Organization Name
Fasoo
Company Location
United States
Company Website
en.fasoo.com/products/analyticdid/
Categories and Features
DICOM Viewers
Not specified
Categories and Features
Data Anonymization
Not specified
Data Masking
Not specified