← Product Code [QKB](/submissions/RA/subpart-b%E2%80%94diagnostic-devices/QKB) · K231928
# EFAI RTSUITE CT HCAP-Segmentation System (K231928)
_Ever Fortune.Ai, Co., Ltd. · QKB · Sep 25, 2023 · Radiology · SESE_
**Canonical URL:** https://fda.innolitics.com/submissions/RA/subpart-b%E2%80%94diagnostic-devices/QKB/K231928
## Device Facts
- **Applicant:** Ever Fortune.Ai, Co., Ltd.
- **Product Code:** [QKB](/submissions/RA/subpart-b%E2%80%94diagnostic-devices/QKB.md)
- **Decision Date:** Sep 25, 2023
- **Decision:** SESE
- **Submission Type:** Special
- **Regulation:** 21 CFR 892.2050
- **Device Class:** Class 2
- **Review Panel:** Radiology
- **Attributes:** AI/ML, Software as a Medical Device
## Intended Use
EFAI HCAPSeg 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 on non-contrast CT images. EFAI HCAPSeg 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 HCAPSeg must be used in conjunction with a DICOM-compliant treatment planning system to review and edit results generated. EFAI HCAPSeg is not intended to be used for decision making or to detect lesions. EFAI HCAPSeg 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 Story
EFAI HCAPSeg is a standalone software tool for radiation oncology professionals (oncologists, physicists, dosimetrists) to automate organ-at-risk (OAR) contouring on non-contrast CT images. Input consists of DICOM CT images; the device uses deep-learning algorithms to generate OAR contours, which are output as DICOM RTSTRUCT files. The software operates on a specialized server within a local hospital network; it lacks a user interface and requires integration with a DICOM-compliant treatment planning system (TPS). Clinicians review and edit the auto-generated contours within the TPS. By providing initial segmentations, the device facilitates treatment planning workflows, potentially increasing efficiency while remaining an adjunct tool that requires final clinical validation.
## Clinical Evidence
Bench testing and standalone performance evaluation were conducted. Training (n=1,410) and testing (n=436) datasets were used for internal validation. A standalone performance study compared EFAI HCAPSeg against a comparison device using 157 independent U.S. clinical cases. Primary endpoints (Dice Similarity Coefficient and 95% Hausdorff Distance) were met, showing EFAI HCAPSeg is non-inferior to the comparison device across all body parts (Head & Neck, Chest, Abdomen & Pelvis) with p-values <0.0001. Mean DSC was 0.83 overall.
## Technological Characteristics
Standalone software; deep-learning algorithm for automated OAR segmentation. Operates on Linux Ubuntu 20.04 server. Inputs/outputs via DICOM 3.0/RTSTRUCT. No user interface; requires integration with DICOM-compliant TPS. Cybersecurity controls implemented per FDA guidance. Software lifecycle processes follow IEC 62304.
## Regulatory 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.
## Special Controls
*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).
## Predicate Devices
- EFAI RTSuite CT HN-Segmentation System ([K220264](/device/K220264.md))
## Reference Devices
- AccuContour ([K191928](/device/K191928.md))
- AutoContour RADAC V2 ([K220598](/device/K220598.md))
## Submission Summary (Full Text)
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September 25, 2023
Image /page/0/Picture/1 description: The image contains the logo of the U.S. Food and Drug Administration (FDA). On the left is the Department of Health & Human Services logo. To the right of that is the FDA logo, which is a blue square with the letters "FDA" in white. To the right of the blue square is the text "U.S. FOOD & DRUG ADMINISTRATION" in blue.
Ever Fortune.AI Co., Ltd. % Ti-Hao Wang Chief Technology Officer 8 F., No. 573, Sec. 2, Taiwan Blvd., West Dist. Taichung City, 403020 TAIWAN
#### Re: K231928
Trade/Device Name: EFAI RTSUITE CT HCAP-Segmentation System Regulation Number: 21 CFR 892.2050 Regulation Name: Medical image management and processing system Regulatory Class: Class II Product Code: QKB Dated: August 31, 2023 Received: September 1, 2023
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 801); medical device reporting of medical device-related adverse events) (21 CFR 803) for
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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.
Locon Weidner
Lora D. Weidner, Ph.D. Assistant Director Radiation Therapy Team DHT8C: Division of Radiological Imaging and Radiation Therapy Devices OHT8: Office of Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
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## Indications for Use
510(k) Number (if known) K231928
Device Name EFAI RTSuite CT HCAP-Segmentation System
#### Indications for Use (Describe)
EFAI HCAPSeg 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 on non-contrast CT images. EFAI HCAPSeg 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 HCAPSeg must be used in conjunction with a DICOM-compliant treatment planning system to review and edit results generated. EFAI HCAPSeg is not intended to be used for decision making or to detect lesions.
EFAI HCAPSeg 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) | X
|
|----------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------|
| Over-The-Counter Use (21 CFR 801 Subpart C) | |
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Image /page/3/Picture/0 description: The image shows the logo for Ever Fortune AI. The logo consists of a stylized human figure in teal with a green circle containing a network of lines and dots for the head. To the right of the figure is the text "EVER" stacked on top of "FORTUNE.AI" in teal. The "O" in fortune is replaced with a green circle containing a network of lines and dots.
# 510(k) Summary
K231928
## 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, CTO |
| Contact Information | 886-04-23213838 #168
tihao.wang@everfortune.ai |
| Date Prepared | June 28, 2023 |
## 2. Proposed Device
| Proprietary Name | EFAI RTSuite CT HCAP-Segmentation System |
|---------------------|--------------------------------------------------------------|
| Common Name | EFAI HCAPSeg |
| Classification Name | Radiological Image Processing Software For Radiation Therapy |
| 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 | EFAI RTSuite CT HN-Segmentation System |
|------------------------|--------------------------------------------------------------|
| Premarket Notification | K220264 |
| Classification Name | Radiological Image Processing Software For Radiation Therapy |
| 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/0 description: The image shows the logo for EVER FORTUNE.AI. The logo features a stylized figure of a person in teal, with a green network-like design for the head. The text "EVER FORTUNE.AI" is displayed to the right of the figure, also in teal. The word "FORTUNE" has a similar network-like design in place of the letter "O".
## 4. Device Description
EFAI RTSuite CT HCAP-Segmentation System, herein referred to as EFAI HCAPSeg, is a standalone software that is designed to be used by trained radiation oncology professionals to automatically delineate 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 HCAPSeg, 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 hospital-grade IT system in place. EFAI HCAPSeg 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 HCAPSeg 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 on non-contrast CT images. EFAI HCAPSeg 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 HCAPSeg must be used in conjunction with a DICOM-compliant treatment planning system to review and edit results generated. EFAI HCAPSeg is not intended to be used for decision making or to detect lesions.
EFAI HCAPSeg 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.
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Image /page/5/Picture/0 description: The image contains a logo for Ever Fortune AI. The logo consists of a teal icon on the left, resembling a person with a head made of interconnected dots. To the right of the icon, the words "EVER" and "FORTUNE.AI" are written in teal, with the "FORTUNE.AI" text positioned below "EVER" and featuring a similar interconnected dot design in place of the letter "O".
## 6. Comparison of Technological Characteristics with Predicate Device
Table A below provides a comparison of the intended use and key technological features of EFAI HCAPSeg with that of the Predicate, EFAI HNSeg (K220264), as well as the reference devices.
| Characteristic | Proposed Device | Predicate Device | Reference Device- 1 | Reference Device- 2 |
|---------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Company | Ever Fortune.AI Co., Ltd.
(EFAI) | Ever Fortune.AI Co., Ltd.
(EFAI) | Xiamen Manteia Technology
LTD. | Radformation, Inc. |
| Device Name | EFAI HCAPSeg | EFAI HNSeg | AccuContour™ | AutoContour RADAC V2 |
| 510k Number | K231928 | K220264 | K191928 | K220598 |
| Regulation No. | 21CFR 892.2050 | 21CFR 892.2050 | 21CFR 892.2050 | 21CFR 892.2050 |
| Classification | II | II | II | II |
| Product Code | QKB | QKB | QKB | QKB |
| Intended
Use/Indication
for Use | EFAI HCAPSeg is a software
device intended to assist trained
radiation
oncology
professionals, including, but not
limited to, radiation oncologists,
physicists,
medical
and
dosimetrists,
their
during
clinical workflows of radiation
therapy treatment planning by
providing initial contours of
organs at risk on non-contrast
CT images. EFAI HCAPSeg is
intended to be used on adult
patients only. | 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. | It is used by radiation oncology
department
register
to
multimodality
images
and
CT
segment
(non-contrast)
generate needed
to
images,
information
treatment
for
planning, treatment evaluation
and treatment adaptation. | is
AutoContour
intended
to
assist
radiation
treatment
in contouring and
planners
reviewing
structures within
medical images in preparation
for radiation therapy treatment
planning. |
| Table A. Comparison with the Predicate and Reference Devices | |
|--------------------------------------------------------------|--|
|--------------------------------------------------------------|--|
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Image /page/6/Picture/0 description: The image contains a logo for a company called EVER FORTUNE.AI. The logo consists of a stylized human figure with a circular head made of interconnected nodes, suggesting a network or AI connection. The text "EVER FORTUNE.AI" is written in a sans-serif font, with the word "FORTUNE" having a similar node-based design element within it.
| | The contours are generated by
deep-learning algorithms and
then transferred to radiation
therapy treatment planning
systems. EFAI HCAPSeg must
be used in conjunction with a
DICOM-compliant treatment
planning system to review and
edit results generated. EFAI
HCAPSeg is not intended to be
used for decision making or to
detect lesions.
EFAI HCAPSeg 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. | 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. | | | | | | | application compatible with
Linux.
Windows python-based
automatic contouring
application supporting
Microsoft Windows 10 (64-bit)
and Microsoft Windows Server
2016. |
|-------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------|------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Segmentation
(Contouring)
Technology | Deep learning | Deep learning | Deep learning | Deep learning | User Population | Trained medical professionals
including, but not limited to,
radiation oncologists, medical
physicists, and dosimetrists. | Trained medical professionals
including, but not limited to,
radiation oncologists, medical
physicists, and dosimetrists. | It is used by radiation oncology
department. | Radiation treatment planners |
| Operating
System | Linux Ubuntu 20.04 | Linux Ubuntu 20.04 | Microsoft Windows | Windows based .NET front-end
application that also serves as
agent Uploader supporting
Microsoft Windows 10 (64-bit)
and Microsoft Windows Server
2016.
Cloud-based Server based
automatic contouring | Supported
Modalities | Non-contrast CT | Non-contrast CT | Segmentation Features:
Non-Contrast CT
Registration Features: CT, MRI,
PET | CT or MR input for contouring
or registration/fusion.
PET/CT input for
registration/fusion only.
DICOM RTSTRUCT for output |
| Localization
and Definition
of Objects
(ROI) | Organ-at risk of head and neck,
chest, abdomen, and pelvis | Organ-at risk of head and neck
region | Organ-at-risk, including head
and neck, thorax, abdomen and
pelvis (for both male and
female) | AutoContour is intended to
assist radiation treatment
planners in contouring and
reviewing structures within
medical images in preparation
for radiation therapy treatment
planning.
CT or MR input for contouring
of anatomical regions: Head and
Neck, Thorax, Abdomen and
Pelvis | | | | | |
| Organ-at risk
(OAR) | A_Aorta,
A_Carotid_L,
A_Carotid_R,
Bladder,
Bone_Mandible,
BrachialPlex_L,
BrachialPlex_R,
Brain, | Brain, BrainStem, Esophagus,
Eye_L, Eye_R, Lens_L,
Lens_R, Mandible,
OpticChiasm, OpticNerve_L,
OpticNerve_R, OralCavity, | A_Aorta,
Bladder,
Bone_Mandible,
BrachialPlex_L,
BrachialPlex_R,
Brain,
Brainstem, Breast_L, Breast_R, | CT Models:
A_Aorta, A_Aorta_Asc,
A_Aorta_Dsc, A_LAD,
Bladder, Bone_Ilium_L,
Bone_Ilium_R, | | | | | |
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Image /page/7/Picture/0 description: The image contains a logo for a company called EVER FORTUNE.AI. The logo consists of a stylized human figure in teal, with a green globe-like shape with interconnected nodes as its head. To the right of the figure, the company name "EVER" is written in teal, with "FORTUNE.AI" below it, also in teal. The dot in "FORTUNE.AI" is replaced with a smaller version of the globe-like shape.
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Image /page/8/Picture/0 description: The image shows the logo for Ever Fortune AI. The logo consists of a stylized figure with a green circle containing a network of white dots as its head. To the right of the figure, the text "EVER" is displayed in a larger font, with "FORTUNE.AI" below it in a smaller font. The colors used in the logo are shades of teal and green.
| Brainstem, Breast L, Breast R,
Bronchus_Prox, Cavity_Oral,
Cochlea_L,
Cochlea_R,
Duodenum,
Ear Internal L,
Ear_Internal_R, Ear Middle L,
Ear_Middle_R, Esophagus,
Eye_L,
Eye_R,
FemurHeadNeck L,
FemurHeadNeck R,
Gallbladder, Glnd Submand L,
Glnd Submand R,
Glnd_Thyroid,
Heart,
Hippocampus_L,
Hippocampus_R,
IAC_L,
IAC_R,
Joint TM_L,
Joint TM_R,
Kidney_L,
Kidney_R, Larynx, Lens_L,
Lens_R, Liver, LN_Neck_IA,
LN_Neck_IB_L,
LN_Neck_IB_R,
LN_Neck_II_L,
LN_Neck_II_R,
LN_Neck_III_L,
LN_Neck_III_R,
LN_Neck_IV_L,
LN_Neck_IV_R,
LN_Neck_V_L,
LN_Neck_V_R, LN Pelvics,
Lobe_Temporal_L,
Lobe_Temporal_R, Lung_L,
Lung_R, OpticChiasm,
OpticNrv_L, OpticNrv_R,
Pancreas, Parotid L, Parotid R,
PenileBulb, Pituitary, Prostate,
Rectum, Seminal Ves,
Spc_Bowel, SpinalCanal,
SpinalCord, Spleen, Stomach,
Testis, Trachea, Uterus, | arotid L,
SpinalCord, Thyroid, Trachea | Parotid_R,
Cavity_Oral,
Cochlea_L,
Cochlea_R,
Duodenum,
Ear Internal L, Ear Internal R,
Ear Middle L, Ear Middle R,
Esophagus, Eye_L,
Eye_R,
FemurHeadNeck_L,
FemurHeadNeck R,
Glnd_Submand_L,
Glnd_Submand_R,
Glnd_Thyroid,
Heart,
Hippocampus_L,
Hippocampus_R,
IAC_L,
IAC_R,
Joint TM_L,
Joint TM_R,
Kidney_L,
Kidney_R, Larynx, Lens_L,
Lens_R, Liver,
Lobe_Temporal_L,
Lobe_Temporal_R, Lung_L,
Lung_R, OpticChiasm,
OpticNrv_L, OpticNrv_R,
Pancreas, Parotid_L, Parotid_R,
Pituitary, Prostate, Rectum,
SeminalVes, Spc_Bowel,
SpinalCanal, SpinalCord,
Spleen, Stomach, Testis,
Trachea, Vestibule_R | Bone_Mandible, Bowel_Bag,
BrachialPlex_L,
BrachialPlex_R, Brain,
Brainstem, Breast L, Breast R,
Bronchus, Carina,
CaudaEquina, Cavity_Oral,
Cochlea_L, Cochlea_R,
Ear Internal L, Ear Internal R,
Esophagus, External, Eye_L,
Eye_R, Femur_L, Femur_R,
Femur_RTOG_L,
Femur_RTOG_R,
Glnd_Lacrimal_L,
Glnd_Lacrimal_R,
Glnd_Submand_L,
Glnd_Submand_R,
Glnd_Thyroid, HDR_Cylinder,
Heart, Humerus_L, Humerus_R,
Kidney_L, Kidney_R,
Kidney_Outer_L,
Kidney_Outer_R, Larynx,
Lens_L, Lens_R, Lips,
LN_Ax_L, LN_Ax_R,
LN_IMN_L, LN_IMN_R,
LN_Neck_IA,
LN_Neck_IB-V_L,
LN_Neck_IB-V_R,
LN_Neck_II_L,
LN_Neck_II_R,
LN_Neck_II-IV_L,
LN_Neck_II-IV_R,
LN_Neck_III_L,
LN_Neck_III_R,
LN_Neck_IV_L,
LN_Neck_IV_R,
LN_Neck_VIA,
LN_Neck_VIIA_L,
LN_Neck_VIIA_R, | | V_Venacava_I,
Vestibule_L,
Vestibule_R | | | LN_Neck_VIIB_L,
LN_Neck_VIIB_R,
LN_Pelvics, LN_Sclav_L,
LN_Sclav_R, Liver, Lung_L,
Lung_R, Marrow_Ilium_L,
Marrow_Ilium_R,
Musc_Constrict, OpticChiasm,
OpticNrv_L, OpticNrv_R,
Parotid_L, Parotid_R,
PenileBulb, Pituitary, Prostate,
Rectum, Rib, SeminalVes,
SpinalCanal, SpinalCord,
Stomach, Trachea,
V_Venacava_S
MR Models:
OpticChiasm, OpticNrv_L,
OpticNrv_R, Brainstem,
Hippocampus_L,
Hippocampus_R |
|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------|----------------------------------------------|--|--|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Compatible
Treatment
Planning
System | No Limitation on TPS model,
DICOM 3.0 compliance
required. | No Limitation on TPS model,
DICOM 3.0 compliance
required. | No Limitation on TPS model,
DICOM 3.0 compliance
required | No Limitation | | | | |
| Automated
Workflow | EFAI HCAPSeg automatically
processes input image data and
sends the results as DICOM-RT
Structure Sets to a
user-configurable target node. | 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 | Automatically contour, allow
the user to review and modify,
generate DICOM-compliant
structure set data. | | | | |
| User Interface | No | No | Yes | Yes | | | | |
{9}------------------------------------------------
Image /page/9/Picture/0 description: The image contains a logo for a company called "EVER FORTUNE.AI". The logo consists of two parts: a stylized human figure with a network-like head and the company name. The human figure is teal and has a rounded shape. The company name is written in a sans-serif font, with "EVER" in a larger size and "FORTUNE.AI" in a smaller size below it. The color of the text matches the color of the human figure.
{10}------------------------------------------------
Image /page/10/Picture/0 description: The image contains a logo for a company called "EVER FORTUNE.AI". The logo consists of a stylized figure with a circular head and a plus-shaped body, colored in a gradient of teal. The head of the figure is a green sphere with white dots and lines, resembling a network or a globe. To the right of the figure, the company name "EVER" is written in large, teal letters, and below it, "FORTUNE.AI" is written in smaller letters, also in teal.
### 7. Performance Data
Performance of the EFAI HCAPSeg 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 cvcle 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 performance of the EFAI HCAPSeg, the performance was validated by clinical and nonclinical tests.
### Nonclinical Tests
During the process of model development, a total of 1,846 adult cases were collected between 2008 and 2018 from the radiation oncology department in Taiwan. These cases were subsequently divided into training and testing datasets, consisting of 1,410 and 436 cases, respectively. These cases covered a range of body parts, including the Head & Neck, Chest, and/or Abdomen & Pelvis.
The demographic information for both the training and test datasets. including age, gender, and CT manufacturer are presented in Table B. The data population included a balanced distribution between females and males. The acquired data encompasses CT manufacturers such as Siemens, GE Medical Systems, Philips, Toshiba.
| | Training Dataset
(n=1,410) | Testing Dataset
(n=436) |
|--------------------|-------------------------------|----------------------------|
| Age | | |
| 18 - 49 years old | 95 | 16 |
| 50 - 69 years old | 772 | 219 |
| Above 70 years old | 235 | 64 |
| N/A | 308 | 137 |
| Gender | | |
| Female | 563 | 110 |
| Male | 581 | 154 |
| N/A | 266 | 172 |
| CT Manufacturer | | |
| Siemens | 914 | 289 |
Table B. Demographic Information for Training and Test Data Sets
{11}------------------------------------------------
Image /page/11/Picture/0 description: The image shows the logo for EVER FORTUNE.AI. The logo features a stylized human figure in teal, with a green globe-like head made of interconnected dots. To the right of the figure, the word "EVER" is written in a teal sans-serif font, with "FORTUNE.AI" below it in a smaller font, also in teal. The logo has a clean and modern design.
| GE Medical Systems | 436 | 109 |
|--------------------|-----|-----|
| Philips | 56 | 35 |
| Toshiba | 1 | 2 |
| N/A | 3 | 1 |
The company used the testing dataset to conduct an internal validation to assess the performance of the EFAI HCAPSeg. The process of ground-truthing, involving the manual contouring of each OAR, was undertaken by three board-certified radiation oncologists. The data collection and ground truth protocol was done following the identical procedures as those of the predicate device. Table C displays the number of images/cases per OAR for both the training and test datasets.
Table C. Number of Images/Cases per OAR for Training and Testing Datasets
| | Training Dataset | | Testing Dataset | |
|-----------------|------------------|------------------|-----------------|------------------|
| OAR | Number of Cases | Number of Images | Number of Cases | Number of Images |
| A_Aorta | 1,091 | 77,731 | 119 | 7,030 |
| A_Carotid_L | 350 | 15,124 | 114 | 4,883 |
| A_Carotid_R | 347 | 11,883 | 114 | 4,118 |
| Bladder | 458 | 7,366 | 61 | 988 |
| Bone_Mandible | 667 | 17,013 | 66 | 1,457 |
| BrachialPlex_L | 474 | 17,909 | 113 | 3,903 |
| BrachialPlex_R | 473 | 16,978 | 113 | 3,643 |
| Brain | 484 | 22,154 | 66 | 2,894 |
| Brainstem | 484 | 11,126 | 66 | 1,403 |
| Breast_L | 350 | 14,049 | 76 | 2,832 |
| Breast_R | 347 | 14,495 | 75 | 2,727 |
| Bronchus_Prox | 353 | 7,523 | 113 | 2,315 |
| Cavity_Oral | 613 | 12,899 | 66 | 1,320 |
| Cochlea_L | 471 | 926 | 66 | 126 |
| Cochlea_R | 475 | 1,077 | 66 | 125 |
| Duodenum | 686 | 19,167 | 77 | 2,063 |
| Ear_Internal_L | 484 | 2,945 | 66 | 375 |
| Ear_Internal_R | 484 | 2,428 | 66 | 308 |
| Ear_Middle_L | 484 | 2,162 | 66 | 277 |
| Ear_Middle_R | 484 | 2,128 | 66 | 258 |
| Esophagus | 1,126 | 61,405 | 114 | 4,844 |
| Eye_L | 482 | 6,150 | 66 | 721 |
| Eye_R | 484 | 6,182 | 66 | 727 |
| FemurHeadNeck_L | 462 | 13,358 | 61 | 1,819 |
| FemurHeadNeck_R | 457 | 13,136 | 61 | 1,802 |
| Gallbladder | 309 | 3,095 | 168 | 1,609 |
| Glnd_Submand_L | 604 | 7,233 | 66 | 808 |
| Glnd_Submand_R | 592 | 7,343 | 66 | 788 |
| Glnd_Thyroid | 838 | 14,605 | 114 | 1,956 |
| Heart | 716 | 22,271 | 119 | 3,012 |
| Hippocampus_L | 478 | 4,653 | 66 | 534 |
| Hippocampus_R | 475 | 4,432 | 66 | 463 |
| IAC_L | 117 | 326 | 105 | 176 |
| IAC_R | 121 | 333 | 100 | 148 |
| Joint_TM_L | 481 | 2,339 | | 229 |
| Joint_TM_R | 484 | 2,231 | | 233 |
| Kidney_L | 762 | 22,347 | 119 | 3,310 |
| Kidney_R | 720 | 20,989 | 119 | 3,407 |
| Larynx | 723 | 13,548 | 114 | 2,134 |
| Lens_L | 475 | 1,716 | | 214 |
| Lens_R | 476 | 1,628 | 66 | 203 |
| Liver | 853 | 37,523 | 119 | 5,061 |
| LN_Neck_IA | 98 | 484 | 73 | 297 |
| LN_Neck_IB_L | 98 | 1,385 | 73 | 905 |
| LN_Neck_IB_R | 98 | 1,376 | 73 | 960 |
| LN_Neck_II_L | 98 | 1,936 | 73 | 1,201 |
| LN_Neck_II_R | 98 | 1,937 | 73 | 1,201 |
| LN_Neck_III_L | 98 | 1,083 | 73 | 817 |
| LN_Neck_III_R | 98 | 1,082 | 73 | 817 |
| LN_Neck_IV_L | 98 | 1,220 | 73 | 1,003 |
| LN_Neck_IV_R | 98 | 1,219 | 73 | 1,003 |
| LN_Neck_V_L | 98 | 2,773 | 73 | 2,227 |
| LN_Neck_V_R | 98 | 2,768 | 73 | 2,227 |
| LN_Pelvics | 220 | 9,486 | 177 | 7,872 |
| Lobe_Temporal_L | 481 | 10,045 | 66 | 1,175 |
| Lobe_Temporal_R | 483 | 10,141 | 66 | 1,185 |
| Lung_L | 896 | 50,332 | 119 | 4,261 |
| Lung_R | 878 | 49,483 | 119 | 4,263 |
| OpticChiasm | 479 | 1,793 | 66 | 279 |
| OpticNrv_L | 479 | 1,832 | 66 | 255 |
| OpticNrv_R | 479 | 2,089 | 66 | 276 |
| Pancreas | 757 | 16,684 | 77 | 1,707 |
| Parotid_L | 515 | 12,722 | 66 | 1,513 |
| Parotid_R | 514 | 12,408 | 66 | 1,501 |
| PenileBulb | 91 | 430 | 73 | 333 |
| Pituitary | 475 | 1,419 | 66 | 197 |
| Prostate | 205 | 2,887 | 75 | 1,091 |
| Rectum | 456 | 12,836 | 61 | 1,779 |
| SeminalVes | 206 | 1,534 | 75 | 613 |
| Spc_Bowel | 737 | 51,228 | 77 | 6,304 |
| SpinalCanal | 1,269 | 119,225 | 157 | 12,760 |
| SpinalCord | 1,407 | 139,337 | 157 | 13,881 |
| Spleen | 793 | 21,027 | 119 | 3,051 |
| Stomach | 808 | 25,949 | 119 | 3,758 |
| Testis | 128 | 1,301 | 67 | 801 |
| Trachea | 876 | 31,998 | 76 | 2,543 |
| Uterus | 163 | 4,087 | 78 | 1,522 |
| V_Venacava_I | 510 | 24,117 | 119 | 3,201 |
| Vestibule_L | 470 | 1,035 | 66 | 133 |
| Vestibule_R | 480 | 1,002 | 66 | 113 |
{12}------------------------------------------------
Image /page/12/Picture/0 description: The image shows the logo for Ever Fortune AI. The logo consists of a stylized human figure in teal with a green globe above its head. To the right of the figure, the word "EVER" is written in teal, with "FORTUNE.AI" written below it in a smaller font, also in teal. The logo appears to be for a company that specializes in artificial intelligence.
{13}------------------------------------------------
Image /page/13/Picture/0 description: The image shows the logo for Ever Fortune AI. The logo consists of a stylized figure of a person with a green globe on top of their head. The text "EVER" is to the right of the figure, and below that is "FORTUNE.AI". The text and figure are all in a light blue color.
{14}------------------------------------------------
Image /page/14/Picture/0 description: The image contains a logo for a company called "EVER FORTUNE.AI". The logo consists of a stylized human figure with a circular head made of interconnected dots, suggesting a network or AI connection. The text "EVER" is placed above "FORTUNE.AI" in a sans-serif font, with the "O" in "FORTUNE" replaced by a similar circular network design.
Overall, the mean Dice coefficient (DSC) was 0.85 for OAR contouring compared to the ground truth (GT). This result, surpassing the pre-specified performance objectives, confirms the validation of the EFAI HCAPSeg algorithm's performance.
## Clinical Tests
A standalone performance test was also performed. The test was conducted to compare the OAR contouring capabilities of EFAI HCAPSeg against the comparison device. The dataset used in this study comprised 157 non-contrast CT cases consecutively collected from the United States (U.S.), all meeting the established inclusion criteria. All data used during the standalone performance evaluation was acquired independently from product development training and internal testing.
The study population contained 30.57% females, 57.96% males, and 11.46% gender not reported. The mean age was 61.69 years old with standard deviation (SD) of 11.90 years old. The acquired data encompasses CT manufacturers such as GE (43.31%), Philips (36.30%), Siemens (9.55%), Toshiba (2.55%), PNS (0.64%), and not reported (7.64%). Location, Race and Ethnic distribution within the study data patient population was unavailable. The cancer types included head-and-neck cancer, pancreas cancer, colorectal cancer, breast cancer, bladder cancer, prostate cancer, stomach cancer, gynecologic cancer, sarcoma, and metastatic tumors from multiple origins.
Each of the OAR contouring was generated by three board-certified radiation oncologists as the ground truth (GT). The acceptance criteria were defined as the following:
- · For OARs present in both EFAI HCAPSeg and the comparison device, the mean Dice coefficient (DSC) of OARs for each body part (Head & Neck, Chest, and Abdomen & Pelvis) should be non-inferior to that of the comparison device, with a pre-specified margin.
- For OARs unique to the EFAI HCAPSeg, the mean DSC of unique OARs should be ● superior to a pre-specified value.
The overall performance showed a mean DSC of 0.83 and the non-inferiority tests indicated that EFAI HCAPSeg successfully met the primary endpoint across all body parts. Specifically, EFAI HCAPSeg achieved mean DSC values of 0.80, 0.90, and 0.90 in Head & Neck, Chest, and Abdomen & Pelvis, respectively, while the comparison device vielded slightly lower values of 0.75, 0.84, and 0.82 for the same body parts. Furthermore, when considering OARs unique to EFAI HCAPSeg. the mean DSC value reached 0.82. As shown in Table D, these results strongly indicate the successful attainment of the acceptance criteria for the primary endpoint was met.
{15}------------------------------------------------
Image /page/15/Picture/0 description: The image shows the logo for Ever Fortune AI. The logo consists of a teal-colored figure with a green circle on top, representing a person with a connected network on their head. To the right of the figure, the words "EVER FORTUNE.AI" are written in teal, with the "O" in "FORTUNE" replaced by a similar connected network symbol. The logo appears to be for a company specializing in artificial intelligence.
| | | EFAI HCAPSeg | | Comparison Device | | Statistical Result + |
|-------------------------------------------------------------------|------------------|--------------|------|-------------------|------|----------------------|
| | | Mean DSC | SD | Mean DSC | SD | p-value |
| OARs Present in Both
EFAI HCAPSeg and the
Comparison Device | Head & Neck | 0.80 | 0.21 | 0.75 | 0.25 | <.0001 |
| | Chest | 0.90 | 0.11 | 0.84 | 0.20 | <.0001 |
| | Abdomen & Pelvis | 0.90 | 0.14 | 0.82 | 0.24 | <.0001 |
| OARs Unique to EFAI
HCAPSeg | Unique OAR | 0.82 | 0.19 | - | - | <.0001 |
Table D. DSC Performance of EFAI HCAPSeg
OAR, Organ-At-Risk; DSC, Dice Similarity Coefficient; SD, Standard Deviation.
* A statistical result includes outcomes obtained from data analysis using statistical methods, encompassing the non-inferiority test for OARs presented in both EFAI HCAPSeg and the Comparison Device, as well as the superiority test for OARs unique to EFAI HCAPSeg. When the p-value is <0.05, it indicates that EFAI HCAPSeg's performance is significantly non-inferior to or superior to the pre-specified performance, respectively.
In addition to the primary endpoint, the overall data showed a median 95% Hausdorff Distance (HD) of 2.23 mm. Table E further presents detailed insights into the 95% HD performance, including testing results across all body parts and the 95% HD values for the OARs unique to EFAI HCAPSeg.
Table E. 95% HD Performance of EFAI HCAPSeg
| | | EFAI HCAPSeg | | Comparison Device | | Statistical Result * |
|-------------------------------------------------------------------|------------------|--------------------------|-----------------|--------------------------|-----------------|----------------------|
| | | Median
95% HD
(mm) | Q1 - Q3
(mm) | Median
95% HD
(mm) | Q1 - Q3
(mm) | p-value |
| OARs Present in Both
EFAI HCAPSeg and the
Comparison Device | Head & Neck | 2.17 | 1.40 - 2.44 | 3.09 | 1.96 - 4.73 | <.0001 |
| | Chest | 2.23 | 1.43 - 2.56 | 3.87 | 2.37 - 7.42 | <.0001 |
| | Abdomen & Pelvis | 2.28 | 1.44 - 3.41 | 3.90 | 2.36 - 10.85 | <.0001 |
| OARs Unique to EFAI
HCAPSeg | Unique OAR | 2.24 | 1.00 - 4.69 | - | - | <.0001 |
OAR, Organ-At-Risk; HD, Hausdorff Distance; Q1, First Quartile; Q3, Third Quartile.
The unit of measurement for the 95% HD is millimeters (mm).
* A statistical result includes outcomes obtained from data analysis using statistical methods, encompassing the non-inferiority test for OARs presented in both EFAI HCAPSeg and the Comparison Device, as well as the superiority test f…
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