EFAI RTSuite CT HN-Segmentation System
Overview
The EFAI RTSuite CT HN-Segmentation System is a standalone software device designed to assist trained radiation oncology professionals, including radiation oncologists, medical physicists, and dosimetrists, during their clinical workflows of radiation therapy treatment planning. It automatically delineates head-and-neck organs-at-risk (OARs) on non-contrast CT images. The system receives CT images in DICOM format as input and automatically generates the contours of OARs, which are stored in DICOM format and in RTSTRUCT modality. This auto-contouring of OARs is intended to facilitate radiation therapy workflows and help reduce the time of treatment planning. The device does not offer a user interface and must be used in conjunction with a DICOM-compliant treatment planning system to review and edit the generated results. It is an adjunct tool and is not intended to be used for decision-making, to detect lesions, or to replace a clinician’s judgment and manual contouring of normal organs on CT. Clinicians must not use the software-generated output alone without review as the primary interpretation.
Reviewed by Pouyan Golshani, MD — Interventional Radiologist
Key Features
- Automatic delineation of head-and-neck organs-at-risk (OARs) on CT images
- Facilitates radiation therapy workflows
- Receives CT images in DICOM format
- Generates contours in DICOM RTSTRUCT modality
- Utilizes deep-learning algorithms for contour generation
- Compatible with DICOM-compliant treatment planning systems for review and editing
- Supports multi-modality image registration (CT, MRI, PET)
- Intended for use by trained radiation oncology professionals
- Designed as an adjunct tool, not for primary diagnosis or decision-making
- Recommended for deployment in a local network with hospital-grade IT systems
Use Cases
- Assisting in radiation therapy treatment planning
- Automating the contouring of organs-at-risk (OARs)
- Improving efficiency in radiotherapy workflow
- Reducing the time required for treatment planning
- Providing initial contours for head and neck regions on non-contrast CT images
What Physicians Need to Know
This AI tool is designed to be a powerful assistant, not a replacement for clinical judgment. Always thoroughly review and, if necessary, manually adjust the AI-generated contours within your treatment planning system. Leverage its speed for routine cases to free up time for more complex planning and patient-specific considerations. While initially focused on head and neck OARs, be aware of its expanding capabilities to other anatomical regions and imaging modalities.
The EFAI RTSuite CT HN-Segmentation System operates as a backend service. Ensure your existing treatment planning system is DICOM 3.0 compliant for seamless input and output of image data and RT Structure Sets. Plan for dedicated server infrastructure within your local network to host the deep learning processing, with manufacturer support for initial setup and configuration.
Details
| Category | Oncology AI, Radiology & Imaging AI |
| Pricing | Contact for pricing |
| Deployment | Recommended to be deployed in a local network with an existing hospital-grade IT system, installed on a specialized server supporting deep learning processing. |
| Compliance | |
| BAA Available | Unknown AI-estimated |
| HIPAA Compliant | Unknown AI-estimated |
| FDA Status |
1 AI-estimated The EFAI RTSuite CT HN-Segmentation System (K220264) received FDA 510(k) clearance on April 28, 2022. It is classified as a Class II medical device under regulation 21 CFR 892.2050, Medical Image Management and Processing System, with product code QKB. |
| Integrations | |
| EHR | Not specified |
| Specialties | Oncology, Radiology |
What the Web Says
The EFAI RTSuite CT HN-Segmentation System is an AI-powered standalone software designed to automate the delineation of head-and-neck organs-at-risk (OARs) on CT images for radiation oncology treatment planning. This system aims to streamline radiation therapy workflows by reducing the time-consuming manual contouring process. It has received regulatory approvals from both Taiwan's TFDA and the US FDA.
Overall: PositiveStrengths
- Automates the delineation of head-and-neck organs-at-risk (OARs) on CT images.
- Facilitates radiation therapy workflows and reduces treatment planning time.
- Aims to improve contour consistency and accuracy, and decrease inter-observer variability.
- Utilizes deep-learning algorithms for contour generation.
- Integrates with DICOM-compliant treatment planning systems for review and editing.
- Has received TFDA and US FDA approvals.
Limitations
- Not intended for decision-making or lesion detection; it is an adjunct tool.
- Requires review and potential manual correction by trained radiation oncology professionals.
- The software does not offer a user interface and must be used with a DICOM-compliant treatment planning system.
- General challenges with AI auto-segmentation include the need for model validation, standardization, and external validation in diverse patient populations.
- No specific negative reviews from physicians, healthcare IT, tech reviewers, Reddit, G2, or Capterra were found for this particular product in the search results.
Based on reviews from: Healthcare+ B2B, accessdata.fda.gov, FDA.report, China Medical University Hospital, Ever Fortune.AI, PMC, TechNewsWorld
Last updated: 2026-07-19
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