(87 days)
Not Found
Yes
The document explicitly states that the contours are generated by "deep-learning algorithms" and that the device "should be installed on a specialized server supporting deep learning processing." Deep learning is a subset of machine learning.
No
Explanation: The device is intended to assist trained radiation oncology professionals in generating contours of organs at risk for radiation therapy treatment planning. It is an adjunct tool and does not provide therapy or directly treat conditions.
No.
Explanation: The device is explicitly stated as "not intended to be used for decision making or to detect lesions," which are characteristic functions of a diagnostic device. Instead, it is an "adjunct tool" to assist in radiation therapy treatment planning by providing initial contours.
Yes
The device is described as "standalone software" and its function is to process existing CT images to generate contours. While it requires a server and integration with a treatment planning system, these are described as the environment and tools for its operation, not components of the device itself. The core functionality is purely software-based image processing and contour generation.
Based on the provided information, this device is not an IVD (In Vitro Diagnostic).
Here's why:
- IVD Definition: In Vitro Diagnostics are tests performed on samples taken from the human body (like blood, urine, or tissue) to detect diseases, conditions, or infections. They are used to provide information for diagnosis, monitoring, or screening.
- Device Function: EFAI HNSeg is a software device that processes medical images (CT scans) to assist in radiation therapy planning. It does not analyze biological samples from the patient.
- Intended Use: The intended use is to assist trained radiation oncology professionals in contouring organs at risk on CT images for radiation therapy treatment planning. This is a step in the treatment process, not a diagnostic test performed on a biological sample.
Therefore, EFAI HNSeg falls under the category of a medical device, specifically a software medical device used in medical imaging and treatment planning, but it does not meet the definition of an In Vitro Diagnostic.
No
The provided text does not contain any explicit statement that the FDA has reviewed and approved or cleared a Predetermined Change Control Plan (PCCP) for this specific device.
Intended Use / Indications for Use
EFAI HNSeg is a software device intended to assist trained radiation oncology professionals, including, but not limited to, radiation oncologists, medical physicists, and dosimetrists, during their clinical workflows of radiation therapy treatment planning by providing initial contours of organs at risk in the head and neck region on non-contrast CT images. EFAI HNSeg is intended to be used on adult patients only.
The contours are generated by deep-learning algorithms and then transferred to radiation therapy treatment planning systems. EFAI HNSeg must be used in conjunction with a DICOM-compliant treatment planning system to review and edit results generated. EFAI HNSeg is not intended to be used for decision making or to detect lesions.
EFAI HNSeg is an adjunct tool and is not intended to replace a clinician's judgment and manual contouring of the normal organs on CT. Clinicians must not use the software generated output alone without review as the primary interpretation.
Product codes
QKB
Device Description
EFAI RTSuite CT HN-Segmentation System, herein referred to as EFAI HNSeg, is a standalone software that is designed to be used by trained radiation oncology professionals to automatically delineate head-and-neck organs-at-risk (OARs) on CT images. This auto-contouring of OARs is intended to facilitate radiation therapy workflows.
The device 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. 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 results. Once data is routed to EFAI HNSeg, the data will be processed and no user interaction is required, nor provided.
The deployment environment is recommended to be in a local network with an existing hospitalgrade IT system in place. EFAI HNSeg should be installed on a specialized server supporting deep learning processing. The configurations are only being operated by the manufacturer:
- Local network setting of input and output destinations;
- Presentation of labels and their color;
- Processed image management and output (RTSTRUCT) file management.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes
Input Imaging Modality
Non-contrast CT
Anatomical Site
Head and neck region
Indicated Patient Age Range
Adult patients only
Intended User / Care Setting
Trained radiation oncology professionals, including, but not limited to, radiation oncologists, medical physicists, and dosimetrists. The deployment environment is recommended to be in a local network with an existing hospital-grade IT system in place.
Description of the training set, sample size, data source, and annotation protocol
Not Found
Description of the test set, sample size, data source, and annotation protocol
To establish the contour performance of EFAI HNSeg, a non-inferiority standalone performance test was performed. This non-inferiority test compared the mean Dice coefficient of the automatically generated head and neck OAR contours for EFAI HNSeg against that of the predicate device, AccuContour™.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Performance of the EFAI HNSeg v1.0 has been evaluated and verified in accordance with software specifications and applicable performance standards through software verification and validation testing. Additionally, the software validation activities were performed in accordance with IEC 62304:2006/A1:2016 - Medical device software – Software life cycle processes, in addition to the FDA Guidance documents, "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices"(2005) and the recently published "Content of Premarket submissions for Devices Software Functions (11-04-2021), and "Content of Premarket Submission for Management of Cybersecurity in Medical Devices. "
To establish the contour performance of EFAI HNSeg, a non-inferiority standalone performance test was performed. This non-inferiority test compared the mean Dice coefficient of the automatically generated head and neck OAR contours for EFAI HNSeg against that of the predicate device, AccuContour™. The results demonstrate that the EFAI HNSeg device was noninferior to the predicate by at least a non-inferiority limit of 0.1 Dice, which was the largest difference that is clinically acceptable based on previous studies, and thus we conclude that equivalence has been demonstrated.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
Dice coefficient
Predicate Device(s)
Reference Device(s)
Not Found
Predetermined Change Control Plan (PCCP) - All Relevant Information
Not Found
§ 892.2050 Medical image management and processing system.
(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).
0
Image /page/0/Picture/0 description: The image shows the logo of the U.S. Food and Drug Administration (FDA). The logo consists of two parts: a symbol on the left and the FDA name and title on the right. The symbol on the left is a stylized representation of a human figure, while the text on the right reads "FDA U.S. FOOD & DRUG ADMINISTRATION" in blue letters.
Ever Fortune.AI Co., Ltd. % Ti-Hao Wang, MD Chief Technology Officer Rm. D. 8F. No. 573. Sec. 2 Taiwan Blvd., West Dist. Taichung City, 403020 Taiwan
Re: K220264
Trade/Device Name: EFAI RTSuite CT HN-Segmentation System Regulation Number: 21 CFR 892.2050 Regulation Name: Medical Image Management And Processing System Regulatory Class: Class II Product Code: QKB Dated: January 28, 2022 Received: January 31, 2022
Dear Ti-Hao Wang:
We have reviewed your Section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database located at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.
If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.
Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part
1
801); medical device reporting of medical device-related adverse events) (21 CFR 803) for devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.
For comprehensive regulatory information about medical devices and radiation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely,
Julie Sullivan, PhD Assistant Director Division of Radiological Health OHT7: Office of In Vitro Diagnostics and Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
2
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DEPARTMENT OF HEALTH AND HUMAN SERVICES Food and Drug Administration
Indications for Use
510(k) Number (if known)
Device Name EFAI HNSeg
Indications for Use (Describe)
EFAI HNSeg is a software device intended to assist trained radiation oncology professionals, including, but not limited to, radiation oncologists, medical physicists, and dosimetrists, during their clinical workflows of radiation therapy treatment planning by providing initial contours of organs at risk in the head and neck region on non-contrast CT images. EFAI HNSeg is intended to be used on adult patients only.
The contours are generated by deep-learning algorithms and then transferred to radiation therapy treatment planning systems. EFAI HNSeg must be used in conjunction with a DICOM-compliant treatment planning system to review and edit results generated. EFAI HNSeg is not intended to be used for decision making or to detect lesions.
EF AI HNSeg is an adjunct tool and is not intended to replace a clinician's judgment and manual contouring of the normal organs on CT. Clinicians must not use the software generated output alone without review as the primary interpretation.
| Type of Use (Select one or both, as applicable) | ||
|---|---|---|
| Prescription Use (Part 21 CFR 801 Subpart D) Over-The-Counter Use (21 CFR 801 Subpart C) | Prescription Use (Part 21 CFR 801 Subpart D) | Over-The-Counter Use (21 CFR 801 Subpart C) |
| Prescription Use (Part 21 CFR 801 Subpart D) | Over-The-Counter Use (21 CFR 801 Subpart C) |
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3
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Section 5. 510(k) Summary
1. General Information
| 510(k) Sponsor | Ever Fortune.AI Co., Ltd. |
|---|---|
| Address | Rm. D, 8F. No. 573, Sec. 2 Taiwan Blvd. |
| West Dist. | |
| Taichung City 403020 | |
| TAIWAN | |
| Applicant | Joseph Chang |
| Contact Information | 886-04-23213838 #216 |
| joseph.chang@everfortune.ai | |
| Correspondence Person | Ti-Hao Wang, MD |
| Contact Information | 886-04-23213838 #168 |
| tihao.wang@everfortune.ai | |
| Date Prepared | January 29, 2022 |
2. Proposed Device
| Proprietary Name | EFAI RTSuite CT HN-Segmentation System v1.0 |
|---|---|
| Common Name | EFAI HNSeg v1.0 |
| Classification Name | Picture Archiving and Communications System |
| Regulation Number | 21 CFR 892.2050 |
| Regulation Name | Medical Image Management and Processing System |
| Product Code | QKB |
| Regulatory Class | II |
3. Predicate Device
| Proprietary Name | AccuContour |
|---|---|
| Premarket Notification | K191928 |
| Classification Name | Picture Archiving and Communications System |
| Regulation Number | 21 CFR 892.2050 |
| Regulation Name | Medical Image Management and Processing System |
| Product Code | QKB |
| Regulatory Class | II |
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Image /page/4/Picture/1 description: The image contains a logo for a company called EVER FORTUNE.AI. The logo consists of a stylized figure of a person with a head made of interconnected dots, and the company name is written in a sans-serif font. The color scheme is primarily teal and light green. The logo appears to be for a technology company, possibly specializing in artificial intelligence.
4. Device Description
EFAI RTSuite CT HN-Segmentation System, herein referred to as EFAI HNSeg, is a standalone software that is designed to be used by trained radiation oncology professionals to automatically delineate head-and-neck organs-at-risk (OARs) on CT images. This auto-contouring of OARs is intended to facilitate radiation therapy workflows.
The device 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. 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 results. Once data is routed to EFAI HNSeg, the data will be processed and no user interaction is required, nor provided.
The deployment environment is recommended to be in a local network with an existing hospitalgrade IT system in place. EFAI HNSeg should be installed on a specialized server supporting deep learning processing. The configurations are only being operated by the manufacturer:
- Local network setting of input and output destinations; ●
- Presentation of labels and their color; ●
- Processed image management and output (RTSTRUCT) file management. ●
5. Intended Use
EFAI HNSeg is a software device intended to assist trained radiation oncology professionals, including, but not limited to, radiation oncologists, medical physicists, and dosimetrists, during their clinical workflows of radiation therapy treatment planning by providing initial contours of organs at risk in the head and neck region on non-contrast CT images. EFAI HNSeg is intended to be used on adult patients only.
The contours are generated by deep-learning algorithms and then transferred to radiation therapy treatment planning systems. EFAI HNSeg must be used in conjunction with a DICOM-compliant treatment planning system to review and edit results generated. EFAI HNSeg is not intended to be used for decision making or to detect lesions.
EFAI HNSeg is an adjunct tool and is not intended to replace a clinician's judgment and manual contouring of the normal organs on CT. Clinicians must not use the software generated output alone without review as the primary interpretation.
6. Comparison of Technological Characteristics with Predicate Device
Table below provides a comparison of the intended use and key technological features of EFAI HNSeg with that of the Primary Predicate, AccuContour™ (K191928).
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| Company | Ever Fortune.AI Co., Ltd. (EFAI) | Xiamen Manteia Technology LTD. |
|---|---|---|
| Device Name | EFAI HNSeg | AccuContourTM |
| 510k Number | Pending | K191928 |
| Regulation No. | 21CFR 892.2050 | 21CFR 892.2050 |
| Classification | II | II |
| Product Code | QKB | QKB |
| Intended Use/Indication | ||
| for Use | EFAI HNSeg is a software device | |
| intended to assist trained radiation | ||
| oncology professionals, including, | ||
| but not limited to, radiation | ||
| oncologists, medical physicists, | ||
| and dosimetrists, during their | ||
| clinical workflows of radiation | ||
| therapy treatment planning by | ||
| providing initial contours of organs | ||
| at risk in the head and neck region | ||
| on non-contrast CT images. EFAI | ||
| HNSeg is intended to be used on | ||
| adult patients only. |
The contours are generated by
deep-learning algorithms and then
transferred to radiation therapy
treatment planning systems. EFAI
HNSeg must be used in conjunction
with a DICOM-compliant
treatment planning system to
review and edit results generated.
EFAI HNSeg is not intended to be
used for decision making or to
detect lesions.
EFAI HNSeg is an adjunct tool and
is not intended to replace a
clinician's judgment and manual
contouring of the normal organs on
CT. Clinicians must not use the
software generated output alone
without review as the primary
interpretation. | It is used by radiation oncology
department to register
multimodality images and segment
(non-contrast) CT images, to
generate needed information for
treatment planning, treatment
evaluation and treatment adaptation.
The product has two image process
functions:
(1) Deep learning contouring: it can
automatically contour the organ-at-
risk, including head and neck,
thorax, abdomen and pelvis (for
both male and female),
(2) Automatic Registration, and
(3) Manual Contour.
It also has the following general
functions:
(1) Receive, add/edit/delete,
transmit, input/export, medical
images and DICOM data;
(2) Patient management;
(3) Review of processed images;
(4) Open and save of files. |
| Segmentation
(Contouring)
Technology | Deep learning
| Deep learning |
| Operating System | Linux Ubuntu 20.04 | Microsoft Windows |
| | | |
| User Population | Trained medical professionals
including, but not limited to,
radiation oncologists, medical
physicists, and dosimetrists. | It is used by radiation oncology
department. |
| Supported Modalities | Non-contrast CT | Segmentation Features: Non-
Contrast CT
Registration Features: CT, MRI,
PET |
| Image Input | Complies with DICOM standard | Complies with DICOM standard |
| Compatible Scanner
Models | No Limitation on scanner model
DICOM 3.0 compliance required. | No Limitation on scanner model
DICOM 3.0 compliance
required. |
| Localization and
Definition of Objects
(ROI) | Organ-at risk of head and neck
region | Organ-at-risk, including head and
neck, thorax, abdomen and pelvis
(for both male and female) |
| Compatible
Treatment Planning
System | No Limitation on TPS model,
DICOM compliance required. | No Limitation on TPS model,
DICOM 3.0 compliance required. |
| Automated
Workflow | EFAI HNSeg automatically
processes input image data and
sends the results as DICOM-RT
Structure Sets to a user-
configurable
target node. | AccuContour automatically
processes input image data |
| User Interface | No | Yes |
Table - Comparison with the Predicate Device.
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Image /page/6/Picture/1 description: The image shows the logo for Ever Fortune AI. The logo consists of a stylized teal-colored figure with a circular head made of interconnected dots, resembling a network. To the right of the figure, the words "EVER" and "FORTUNE.AI" are written in a teal sans-serif font, with the word "FORTUNE.AI" appearing below "EVER" and slightly offset to the right. The logo has a clean and modern design.
The proposed device, EFAI HNSeg, is substantially equivalent to the claimed predicate, AccuContour™ (K191928).
7. Performance Data
Performance of the EFAI HNSeg v1.0 has been evaluated and verified in accordance with software specifications and applicable performance standards through software verification and validation testing. Additionally, the software validation activities were performed in accordance with IEC 62304:2006/A1:2016 - Medical device software – Software life cycle processes, in addition to the FDA Guidance documents, "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices"(2005) and the recently published "Content of Premarket submissions for Devices Software Functions (11-04-2021), and "Content of Premarket Submission for Management of Cybersecurity in Medical Devices. "
To establish the contour performance of EFAI HNSeg, a non-inferiority standalone performance test was performed. This non-inferiority test compared the mean Dice coefficient of the automatically generated head and neck OAR contours for EFAI HNSeg against that of the predicate device, AccuContour™. The results demonstrate that the EFAI HNSeg device was noninferior to the predicate by at least a non-inferiority limit of 0.1 Dice, which was the largest
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Image /page/7/Picture/1 description: The image shows the logo for EVER FORTUNE.AI. The logo consists of a stylized human figure in teal with a green globe on top of its head. To the right of the figure is the text "EVER" in teal, with "FORTUNE.AI" below it, also in teal. The globe on the figure's head is made up of interconnected dots, suggesting a network or global connection.
difference that is clinically acceptable based on previous studies, and thus we conclude that equivalence has been demonstrated.
8. Conclusion
Based on the information submitted in this premarket notification, and based on the indications for use, technological characteristics, and performance testing, the EFAI HNSeg v1.0 raises no new questions of safety and effectiveness and is substantially equivalent to the predicate device in terms of safety, effectiveness, and performance.