Workflow Box
Overview
Mirada Medical’s Workflow Box is an advanced AI-automation platform designed to significantly enhance the efficiency and precision of cancer treatment planning, particularly within radiation oncology. Leveraging deep learning and multi-atlas contouring technologies, it automates the laborious task of delineating tumors and organs-at-risk (OARs) on various medical images, including CT, MR, and PET scans. The platform aims to reduce manual effort, save time, and improve the consistency and accuracy of contouring, which is critical for minimizing damage to healthy tissue during radiation therapy. Workflow Box also features robust data routing enhancements, ensuring flexible and seamless integration into existing radiotherapy workflows. It supports a ‘Zero-Click’ automation approach, allowing clinicians to focus on complex cases while the system handles routine tasks, thereby accelerating the treatment pathway for patients.
Reviewed by Pouyan Golshani, MD — Interventional Radiologist
Key Features
- AI-based autocontouring (Deep Learning Contouring - DLCExpertu2122)
- Zero-Clicku2122 automation platform
- Multi-atlas contouring
- Deformable image registration for re-contouring
- Intelligent data routing and integration
- Support for multiple modalities (CT, MR, PET, RTSTRUCT)
- Organ At Risk (OAR) contouring
- Tumor and target volume delineation
- Vendor-agnostic integration
- Workflow prioritization for critical cases
Use Cases
- Accelerating cancer treatment planning
- Automating OAR and target volume contouring in radiation oncology
- Improving consistency and accuracy in contouring
- Streamlining diagnostic imaging workflows
- Adaptive radiotherapy planning
- Reducing manual effort in image processing tasks
What Physicians Need to Know
Leverage Workflow Box for its significant time-saving potential in OAR and target volume contouring, particularly in high-volume radiation oncology settings. Always critically review the automatically generated contours using appropriate visualization software, as the tool is an aid and not a replacement for clinical judgment. While highly effective for many structures, be aware that small or highly variable anatomical regions may require more manual adjustment and further clinical validation. Utilize the workflow prioritization features to manage critical cases efficiently.
Workflow Box is designed for seamless integration into existing clinical environments. It is DICOM-compliant, allowing easy data exchange with PACS, scanners, and various treatment planning systems. The platform's flexibility also supports the integration of custom applications, enabling tailored solutions within your hospital network.
Details
| Category | Oncology AI, Radiology & Imaging AI |
| Pricing | Contact for pricing — Pricing information is not publicly available; contact Mirada Medical for a quote. |
| Deployment | Server-based application supporting Microsoft Windows 10 (64-bit) and Microsoft Windows Server 2016. Can be deployed as standalone workstation or thin client solution. |
| Compliance | |
| BAA Available | Unknown AI-estimated |
| HIPAA Compliant | Unknown AI-estimated |
| FDA Status |
1 AI-estimated FDA 510(k) clearance K181572 was granted on July 10, 2018, for Workflow Box. It is classified as a Class II Picture Archiving and Communications System (PACS) component, intended for routing image data and structures to automatic image processing workflows, including atlas-based, image registration-based re-contouring, and machine learning-based contouring. |
| Integrations | |
| EHR | Not specified |
| Specialties | Nuclear Medicine, Oncology, Radiology |
What the Web Says
Mirada Medical's Workflow Box is an automated contouring software primarily used in radiation therapy planning to delineate target volumes and organs at risk (OARs). It aims to save time for radiation oncologists and improve consistency in contouring. While it significantly reduces the time spent on initial delineation, physicians' review and corrections are still necessary, especially for smaller or more complex structures.
Overall: MixedStrengths
- Significant time-saving in delineation of target volumes and OARs, potentially reducing contouring time by about 40%.
- Improves consistency and reproducibility in delineation practices.
- Can contour a large number of structures (over 160) on CT and MRI images across various body regions.
- Supports routing of data to and from DICOM nodes within a hospital network, enabling automated workflows.
- Considered one of the first commercially available AI auto-contouring solutions.
- Produces acceptable quality work for many structures, with a high percentage of auto-delineations scored as clinically acceptable with or without corrections.
Limitations
- Remains insufficiently accurate for some lymph node volumes (e.g., IMN) and small structures in OAR delineation, requiring manual correction.
- Requires further clinical validation before widespread use in daily practice.
- Does not have a built-in editor or confirmed CBCT/PET support.
- Lacks a user interface for visualizing image data, requiring external software for review and editing.
- Not intended to automatically detect lesions.
- Evidence primarily consists of small, single-institutional studies, with a lack of transparency regarding development and algorithms.
Based on reviews from: PMC, RT Medical Systems, Artificial intelligence auto-contouring to aid radiotherapy treatment planning [GID-HTE10015] External Assessment Group report N - NICE, Enhancing Radiotherapy Workflow for Head and Neck Cancer with Artificial Intelligence: A Systematic Review - MDPI, accessdata.fda.gov, University of Groningen, ResearchGate, Apposite Capital, Innolitics, AAPM, Maastricht University, AIR Unimi
Last updated: 2026-07-18
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