Advanced Algorithms for Treatment Management Applications (AATMA)
Overview
Advanced Algorithms for Treatment Management Applications (AATMA) by Elekta Solutions AB is a medical image processing library. It is designed to generate derived data sets that serve as input for radiation therapy treatment planning systems or other intermediate pre-treatment planning applications. AATMA operates without a direct user interface, relying on its application programming interface (API) for integration with other devices. The data sets produced by AATMA, particularly those from its auto-segmentation algorithm, must be thoroughly reviewed and validated by a qualified healthcare professional before clinical use. The auto-segmentation capability is powered by machine-learning convolutional neural networks and utilizes pre-trained models to automatically segment image sets. Functioning as a computational engine, AATMA processes data without storing input, output, or logs, ensuring data privacy and efficient operation. Its development adheres to regulatory standards, including FDA 510(k) clearance, to enhance the precision and efficiency of cancer treatment planning.
Reviewed by Pouyan Golshani, MD — Interventional Radiologist
Key Features
- Medical image processing library
- Produces derived data sets for radiation therapy planning
- Auto-segmentation algorithm
- Based on machine-learning convolutional neural networks
- Includes pre-trained models for image segmentation
- Accessed via Application Programming Interface (API)
- Functions as a computational engine (does not store data)
- Supports review and validation by healthcare professionals
- Enhances precision in treatment planning
- Aids in adaptive therapy planning
Use Cases
- Input for radiation therapy treatment planning systems
- Intermediate pre-treatment planning applications
- Automated segmentation of image sets (e.g., Head & Neck, Male Pelvis)
- Improving clinical workflows in radiation oncology
- Enhancing adaptive radiotherapy planning
What Physicians Need to Know
AATMAu2122 streamlines the initial auto-segmentation of medical images, a critical step in radiation therapy planning. Physicians should leverage its API-driven capabilities to integrate seamlessly with existing treatment planning systems, enhancing efficiency in contouring. It's crucial to remember that AATMAu2122 functions as a computational engine, and the derived datasets must always be reviewed and validated by a qualified healthcare professional prior to clinical use to ensure patient safety and treatment accuracy.
AATMAu2122 is designed as an API-accessed library, making it highly suitable for integration into various radiation therapy treatment planning systems and pre-treatment planning applications. Its output in standard DICOM formats facilitates interoperability. Developers can also explore integration with Elekta's broader ecosystem, including their FHIR API for MOSAIQ, to create comprehensive AI-powered healthcare applications that leverage both image processing and EHR data.
Details
| Category | Developer Tools & APIs, Oncology AI, Radiology & Imaging AI |
| Pricing | Contact for pricing |
| Deployment | Integrated via API into existing systems; Elekta also offers cloud-based solutions built on Microsoft Azure for broader product portfolio. |
| Compliance | |
| BAA Available | Unknown AI-estimated |
| HIPAA Compliant | Unknown AI-estimated |
| FDA Status |
1 AI-estimated AATMA™ received U.S. FDA 510(k) clearance (K212218) as a Class II medical device under regulation 21 CFR 892.2050 (Medical Image Management And Processing System), with product codes QKB and LLZ. |
| Integrations | |
| EHR | Not specified |
| Specialties | Oncology, Radiology |
What the Web Says
Advanced Algorithms for Treatment Management Applications (AATMA) by Elekta is a medical image processing library designed to produce derived datasets for radiation therapy treatment planning systems. It aims to improve the quality and efficiency of radiotherapy by automating various treatment planning processes, such as auto-segmentation and dose prediction. While AATMA itself doesn't have a user interface, it's accessed via an API by other applications and requires human review and editing of its generated results. The use of AI in radiotherapy, in general, has shown promise in reducing planning time, increasing prediction accuracy, and enhancing patient care.
Overall: PositiveStrengths
- Automates various treatment planning processes, potentially saving time and increasing efficiency.
- Aims to improve treatment planning quality and accuracy.
- Can generate contours and treatment plans, with dose calculations.
- Designed to be integrated with existing radiation therapy treatment planning systems.
- Performance testing for Head & Neck and Male Pelvis models demonstrated substantial equivalence to predicate devices.
- AI methods in radiotherapy can reduce time and increase prediction accuracy, performing better than other methods in dose prediction, treatment design, and dose delivery.
Limitations
- AATMA does not provide a user interface and requires other software for access and review.
- Requires appropriate software to review and edit results generated by the auto-segmentation algorithm, indicating it's not a fully autonomous solution.
- Some automated plans may still require manual adjustment.
- Training of AI algorithms with inferior plans can result in inferior plans for future patients.
- General challenges with AI in healthcare include data privacy, security, ethics, and regulatory barriers.
- Computational time can be higher for advanced algorithms, requiring advanced hardware.
Based on reviews from: Indeed.com, G2, PubMed, accessdata.fda.gov, Capterra, DigitalCommons@TMC, Imaging Technology News, AAPM, Frontiers, MDPI, Colorado Associates in Medical Physics
Last updated: 2026-07-18
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