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.

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.

AI/ML Overview

Here is a summary of the acceptance criteria and study information for the EFAI RTSuite CT HN-Segmentation System based on the provided document:

Acceptance Criteria and Reported Device Performance

Acceptance Criteria (Quantitative Metrics)	Reported Device Performance (EFAI HNSeg)
Non-inferiority to predicate device (AccuContour™) with a non-inferiority limit of 0.1 Dice coefficient.	The EFAI HNSeg device was non-inferior to the predicate (AccuContour™) by at least a non-inferiority limit of 0.1 Dice.

Study Information

Sample size used for the test set and data provenance:
- Test Set Size: Not explicitly stated in the provided text.
- Data Provenance: Not explicitly stated in the provided text (e.g., country of origin, retrospective or prospective).
Number of experts used to establish the ground truth for the test set and their qualifications: Not explicitly stated in the provided text for the test set.
Adjudication method for the test set: Not explicitly stated in the provided text.
Multi-Reader Multi-Case (MRMC) comparative effectiveness study: No, an MRMC study was not conducted. The study was a "non-inferiority standalone performance test" comparing the device's output to a predicate device. It did not involve comparing human readers with and without AI assistance to determine an effect size.
Standalone performance study: Yes, a standalone performance test was done. The document states: "To establish the contour performance of EFAI HNSeg, a non-inferiority standalone performance test was performed." This study compared the device's automatically generated contours against those of a predicate device.
Type of ground truth used: The ground truth for contour performance, though not explicitly detailed in its establishment, was used to compare against the device's output and the predicate device's output. Given the context of segmenting "organs at risk," it can be inferred that the ground truth would typically be expert-annotated contours. The comparison was specifically against the performance of a legally marketed predicate device (AccuContour™) which itself would have established its own performance against a form of ground truth or clinical standard.
Sample size for the training set: Not explicitly stated in the provided text.
How the ground truth for the training set was established: Not explicitly stated in the provided text. However, for deep learning models like EFAI HNSeg, training set ground truth for segmentation would typically be established through expert manual contouring of OARs on CT images by qualified professionals (e.g., radiation oncologists, medical physicists, dosimetrists).

Summary

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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

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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

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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)

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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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 403020TAIWAN
Applicant	Joseph Chang
Contact Information	886-04-23213838 #216joseph.chang@everfortune.ai
Correspondence Person	Ti-Hao Wang, MD
Contact Information	886-04-23213838 #168tihao.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

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

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/Indicationfor Use	EFAI HNSeg is a software deviceintended to assist trained radiationoncology professionals, including,but not limited to, radiationoncologists, medical physicists,and dosimetrists, during theirclinical workflows of radiationtherapy treatment planning byproviding initial contours of organsat risk in the head and neck regionon non-contrast CT images. EFAIHNSeg is intended to be used onadult patients only.The contours are generated bydeep-learning algorithms and thentransferred to radiation therapytreatment planning systems. EFAIHNSeg must be used in conjunctionwith a DICOM-complianttreatment planning system toreview and edit results generated.EFAI HNSeg is not intended to beused for decision making or todetect lesions.EFAI HNSeg is an adjunct tool andis not intended to replace aclinician's judgment and manualcontouring of the normal organs onCT. Clinicians must not use thesoftware generated output alonewithout review as the primaryinterpretation.	It is used by radiation oncologydepartment to registermultimodality images and segment(non-contrast) CT images, togenerate needed information fortreatment planning, treatmentevaluation and treatment adaptation.The product has two image processfunctions:(1) Deep learning contouring: it canautomatically contour the organ-at-risk, including head and neck,thorax, abdomen and pelvis (forboth male and female),(2) Automatic Registration, and(3) Manual Contour.It also has the following generalfunctions:(1) Receive, add/edit/delete,transmit, input/export, medicalimages 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 professionalsincluding, but not limited to,radiation oncologists, medicalphysicists, and dosimetrists.	It is used by radiation oncologydepartment.
Supported Modalities	Non-contrast CT	Segmentation Features: Non-Contrast CTRegistration Features: CT, MRI,PET
Image Input	Complies with DICOM standard	Complies with DICOM standard
Compatible ScannerModels	No Limitation on scanner modelDICOM 3.0 compliance required.	No Limitation on scanner modelDICOM 3.0 compliancerequired.
Localization andDefinition of Objects(ROI)	Organ-at risk of head and neckregion	Organ-at-risk, including head andneck, thorax, abdomen and pelvis(for both male and female)
CompatibleTreatment PlanningSystem	No Limitation on TPS model,DICOM compliance required.	No Limitation on TPS model,DICOM 3.0 compliance required.
AutomatedWorkflow	EFAI HNSeg automaticallyprocesses input image data andsends the results as DICOM-RTStructure Sets to a user-configurabletarget node.	AccuContour automaticallyprocesses 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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K220264

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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.

Regulation Number and Section

§ 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).