(87 days)
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.
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. ●
Here's a detailed breakdown of the acceptance criteria and the study that proves the device meets them, based on the provided text:
Acceptance Criteria and Device Performance
| Acceptance Criteria Category | Specific Criteria | Reported Device Performance (EFAI HCAPSeg) | Statistical Result (p-value) |
|---|---|---|---|
| OARs Present in Both EFAI HCAPSeg and Comparison Device | The mean Dice Coefficient (DSC) of OARs for each body part (Head & Neck, Chest, Abdomen & Pelvis) should be non-inferior to that of the comparison device, with a pre-specified margin. | Overall Mean DSC: 0.83 (vs. 0.75 for Head & Neck, 0.84 for Chest, 0.82 for Abdomen & Pelvis in comparison device) | <.0001 (for all three body parts, indicating non-inferiority) |
| OARs Unique to EFAI HCAPSeg | The mean DSC of unique OARs should be superior to a pre-specified value. | Mean DSC: 0.82 | <.0001 (indicating superiority to pre-specified value) |
| Individual OAR DSC Performance | The referenced performance acts as the performance target for each individual OAR. For OARs present in both devices, the benchmark is non-inferiority to the comparison device. For unique OARs, the benchmark DSC is 0.80/0.65/0.50 for large/medium/small volume structures. | Detailed in Table G of the original document. EFAI HCAPSeg met or exceeded its referenced performance for all individual OARs where a comparison was made or a benchmark was set. For example, for A_Aorta, EFAI HCAPSeg Mean DSC was 0.86 compared to a referenced performance of 0.26 (presumably from the comparison device, indicating superiority). For Brain, EFAI HCAPSeg Mean DSC was 0.99 compared to a referenced performance of 0.86. | No specific p-values for each individual OAR against its reference are explicitly given in the summary tables but are generalized by the overall group p-values. The statement "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" suggests these were met. |
| Overall Median 95% Hausdorff Distance (HD) | No explicit acceptance criterion stated as a specific value, but performance is compared against the comparison device. | Overall Median 95% HD: 2.23 mm | N/A (Overall median is reported, not directly compared to a specific acceptance value in this summary) |
| 95% HD for OARs Present in Both EFAI HCAPSeg and Comparison Device | While not explicitly stated as an "acceptance criterion" with a numerical threshold in the same way as DSC, the statistical results demonstrate significant improvement. | Median 95% HD (Head & Neck): 2.17 mm (vs. 3.09 mm for comparison device)Median 95% HD (Chest): 2.23 mm (vs. 3.87 mm for comparison device)Median 95% HD (Abdomen & Pelvis): 2.28 mm (vs. 3.90 mm for comparison device) | <.0001 (for all three body parts, indicating superiority) |
| 95% HD for OARs Unique to EFAI HCAPSeg | No explicit acceptance criterion stated, but the performance is presented. | Median 95% HD: 2.24 mm | <.0001 (indicating superiority to pre-specified performance for unique OARs, likely using an implicit benchmark similar to DSC) |
| Performance Across Subgroups | Device should consistently and reliably perform under varying gender, age group, CT manufacturer, and CT slice thickness. | Consistently High Performance (DSC: 0.82-0.89; HD: 2.20-2.30 mm) across all tested subgroups (Gender: Female, Male; Age: 18-49, 50-69, ≥70; CT Manufacturer: GE, Philips, Siemens, Others; CT Slice Thickness: 0.5-3.0 mm, 3.1-5.0 mm). | N/A (Subgroup analyses demonstrate consistency rather than direct pass/fail criteria) |
Study Details
-
Sample Size and Data Provenance (Test Set):
- Sample Size: 157 non-contrast CT cases.
- Data Provenance: Consecutively collected from the United States (U.S.). All data was acquired independently from product development training and internal testing. This indicates a prospective-like collection for validation.
- Demographics:
- 30.57% females, 57.96% males, 11.46% gender not reported.
- Mean age: 61.69 years (SD 11.90 years).
- CT Manufacturer: GE (43.31%), Philips (36.30%), Siemens (9.55%), Toshiba (2.55%), PNS (0.64%), not reported (7.64%).
- Location, Race, and Ethnic distribution: Unavailable.
- Cancer types: Head-and-neck, pancreas, colorectal, breast, bladder, prostate, stomach, gynecologic, sarcoma, and metastatic tumors from multiple origins.
-
Number of Experts and Qualifications (Ground Truth for Test Set):
- Number of Experts: Three (3) board-certified radiation oncologists.
- Qualifications: "Board-certified radiation oncologists." (Specific years of experience are not mentioned, but board certification implies a high level of expertise). Adjudication method is described next.
-
Adjudication Method (Test Set):
- The OAR contouring for the ground truth was generated by "three board-certified radiation oncologists as the ground truth (GT)." The text implicitly suggests a consensus or independent review that established the GT, but it doesn't specify a formal adjudication method like "2+1" or "3+1". It simply states that their combined work constituted the GT.
-
Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:
- No, a MRMC comparative effectiveness study focusing on human readers improving with AI vs. without AI assistance was not explicitly described.
- The study performed a standalone performance test comparing the algorithm's performance (EFAI HCAPSeg) against a "comparison device" (another algorithm/device, not human readers). The "comparison device" results are used as a benchmark for EFAI HCAPSeg's performance.
-
Standalone Performance Study (Algorithm Only):
- Yes, a standalone performance test was done.
- Method: The EFAI HCAPSeg's OAR contouring capabilities were compared against a "comparison device."
- Results (EFAI HCAPSeg vs. Comparison Device, Mean DSC):
- Head & Neck: 0.80 (EFAI HCAPSeg) vs. 0.75 (Comparison Device)
- Chest: 0.90 (EFAI HCAPSeg) vs. 0.84 (Comparison Device)
- Abdomen & Pelvis: 0.90 (EFAI HCAPSeg) vs. 0.82 (Comparison Device)
- Unique OARs of EFAI HCAPSeg: 0.82 (compared against a pre-specified value, not the comparison device)
- Results (EFAI HCAPSeg vs. Comparison Device, Median 95% HD mm):
- Head & Neck: 2.17 (EFAI HCAPSeg) vs. 3.09 (Comparison Device)
- Chest: 2.23 (EFAI HCAPSeg) vs. 3.87 (Comparison Device)
- Abdomen & Pelvis: 2.28 (EFAI HCAPSeg) vs. 3.90 (Comparison Device)
- Unique OARs of EFAI HCAPSeg: 2.24 (compared against a pre-specified value)
-
Type of Ground Truth Used (Test Set):
- Expert Consensus: The ground truth was established by "three board-certified radiation oncologists" who manually contoured each Organ-at-Risk (OAR).
-
Sample Size for Training Set:
- Total Cases: 1,410 adult cases.
- Number of Images: Varies significantly by OAR, ranging from hundreds to over 100,000 images per OAR (e.g., SpinalCord had 139,337 images). (Refer to Table C for detailed counts per OAR).
-
How the Ground Truth for the Training Set Was Established:
- The text states, "The process of ground-truthing, involving the manual contouring of each OAR, was undertaken by three board-certified radiation oncologists." It further adds, "The data collection and ground truth protocol was done following the identical procedures as those of the predicate device." While not explicitly stated for the training set ground truth, it is highly implied that the same process (manual contouring by three board-certified radiation oncologists) was used for both training and testing datasets for consistency, especially given the statement about identical procedures to the predicate device. The demographic information in Table B covers both training and testing datasets, reinforcing this.
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September 25, 2023
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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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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, CTO |
| Contact Information | 886-04-23213838 #168tihao.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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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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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 TechnologyLTD. | 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 |
| IntendedUse/Indicationfor Use | EFAI HCAPSeg is a softwaredevice intended to assist trainedradiationoncologyprofessionals, including, but notlimited to, radiation oncologists,physicists,medicalanddosimetrists,theirduringclinical workflows of radiationtherapy treatment planning byproviding initial contours oforgans at risk on non-contrastCT images. EFAI HCAPSeg isintended to be used on adultpatients only. | EFAI HNSeg is a softwaredevice intended to assist trainedradiationoncologyprofessionals, including, but notlimited to, radiation oncologists,medicalphysicists,anddosimetrists,duringtheirclinical workflows of radiationtherapy treatment planning byproviding initial contours oforgans at risk in the head andneck region on non-contrast CTimages.EFAIHNSegisintended to be used on adultpatients only. | It is used by radiation oncologydepartmentregistertomultimodalityimagesandCTsegment(non-contrast)generate neededtoimages,informationtreatmentforplanning, treatment evaluationand treatment adaptation. | isAutoContourintendedtoassistradiationtreatmentin contouring andplannersreviewingstructures withinmedical images in preparationfor radiation therapy treatmentplanning. |
| Table A. Comparison with the Predicate and Reference Devices | |
|---|---|
| -------------------------------------------------------------- | -- |
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| The contours are generated bydeep-learning algorithms andthen transferred to radiationtherapy treatment planningsystems. EFAI HCAPSeg mustbe used in conjunction with aDICOM-compliant treatmentplanning system to review andedit results generated. EFAIHCAPSeg is not intended to beused for decision making or todetect lesions.EFAI HCAPSeg is an adjuncttool and is not intended toreplace a clinician's judgmentand manual contouring of thenormal organs on CT. Cliniciansmust not use the softwaregenerated output alone withoutreview as the primaryinterpretation. | The contours are generated bydeep-learning algorithms andthen transferred to radiationtherapy treatment planningsystems. EFAI HNSeg must beused in conjunction with aDICOM-compliant treatmentplanning system to review andedit results generated. EFAIHNSeg is not intended to beused for decision making or todetect lesions.EFAI HNSeg is an adjunct tooland is not intended to replace aclinician's judgment and manualcontouring of the normal organson CT. Clinicians must not usethe software generated outputalone without review as theprimary interpretation. | application compatible withLinux.Windows python-basedautomatic contouringapplication supportingMicrosoft Windows 10 (64-bit)and Microsoft Windows Server2016. | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Segmentation(Contouring)Technology | Deep learning | Deep learning | Deep learning | Deep learning | User Population | Trained medical professionalsincluding, but not limited to,radiation oncologists, medicalphysicists, and dosimetrists. | Trained medical professionalsincluding, but not limited to,radiation oncologists, medicalphysicists, and dosimetrists. | It is used by radiation oncologydepartment. | Radiation treatment planners |
| OperatingSystem | Linux Ubuntu 20.04 | Linux Ubuntu 20.04 | Microsoft Windows | Windows based .NET front-endapplication that also serves asagent Uploader supportingMicrosoft Windows 10 (64-bit)and Microsoft Windows Server2016.Cloud-based Server basedautomatic contouring | SupportedModalities | Non-contrast CT | Non-contrast CT | Segmentation Features:Non-Contrast CTRegistration Features: CT, MRI,PET | CT or MR input for contouringor registration/fusion.PET/CT input forregistration/fusion only.DICOM RTSTRUCT for output |
| Localizationand Definitionof Objects(ROI) | Organ-at risk of head and neck,chest, abdomen, and pelvis | Organ-at risk of head and neckregion | Organ-at-risk, including headand neck, thorax, abdomen andpelvis (for both male andfemale) | AutoContour is intended toassist radiation treatmentplanners in contouring andreviewing structures withinmedical images in preparationfor radiation therapy treatmentplanning.CT or MR input for contouringof anatomical regions: Head andNeck, Thorax, Abdomen andPelvis | |||||
| 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/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_SMR Models:OpticChiasm, OpticNrv_L,OpticNrv_R, Brainstem,Hippocampus_L,Hippocampus_R | |||
|---|---|---|---|---|---|---|---|---|
| CompatibleTreatmentPlanningSystem | No Limitation on TPS model,DICOM 3.0 compliancerequired. | No Limitation on TPS model,DICOM 3.0 compliancerequired. | No Limitation on TPS model,DICOM 3.0 compliancerequired | No Limitation | ||||
| AutomatedWorkflow | EFAI HCAPSeg automaticallyprocesses input image data andsends the results as DICOM-RTStructure Sets to auser-configurable target node. | EFAI HNSeg automaticallyprocesses input image data andsends the results as DICOM-RTStructure Sets to auser-configurable target node. | AccuContour automaticallyprocesses input image data | Automatically contour, allowthe user to review and modify,generate DICOM-compliantstructure set data. | ||||
| User Interface | No | No | Yes | Yes |
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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.
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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
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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 |
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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.
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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.
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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.
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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 BothEFAI HCAPSeg and theComparison 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 EFAIHCAPSeg | 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 * | ||||
|---|---|---|---|---|---|---|
| Median95% HD(mm) | Q1 - Q3(mm) | Median95% HD(mm) | Q1 - Q3(mm) | p-value | ||
| OARs Present in BothEFAI HCAPSeg and theComparison 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 EFAIHCAPSeg | 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 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.
Moreover, the DSC and 95% HD performance for subgroup analyses based on gender, age group, CT manufacturer, and CT slice thickness (Table F) showed that the device consistently and reliably performed under these circumstances.
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Image /page/16/Picture/0 description: The image contains the logo for Ever Fortune AI. The logo consists of a stylized human figure in teal with a green network-like structure as the head. To the right of the figure, the text "EVER FORTUNE.AI" is displayed in teal. The word "EVER" is in a larger font size than "FORTUNE.AI", and the "O" in fortune is replaced with a green network-like structure.
| EFAI HCAPSeg | |||||
|---|---|---|---|---|---|
| Mean DSC | SD | Median95% HD (mm) | Q1 - Q3 (mm) | ||
| Gender | Female | 0.83 | 0.23 | 2.28 | 1.40 - 3.30 |
| Gender | Male | 0.85 | 0.17 | 2.23 | 1.39 - 3.16 |
| Age Group | 18 - 49 y/o | 0.85 | 0.18 | 2.22 | 1.32 - 2.81 |
| 50 - 69 y/o | 0.83 | 0.23 | 2.28 | 1.40 - 3.38 | |
| ≥ 70 y/o | 0.85 | 0.20 | 2.27 | 1.44 - 4.06 | |
| CT Manufacturer | GE | 0.84 | 0.22 | 2.23 | 1.37 - 3.16 |
| Philips | 0.82 | 0.19 | 2.24 | 1.41 - 3.15 | |
| Siemens | 0.89 | 0.15 | 2.29 | 1.42 - 3.31 | |
| Others ‡ | 0.89 | 0.15 | 2.30 | 1.41 - 3.47 | |
| CT Slice Thickness | 0.5 - 3.0 mm | 0.84 | 0.15 | 2.24 | 1.43 - 3.13 |
| 3.1 - 5.0 mm | 0.83 | 0.23 | 2.20 | 1.33 - 3.05 |
Table F. DSC and 95% HD Performance of EFAI HCAPSeg across All Subgroups
DSC, Dice Similarity Coefficient; SD, Standard Deviation; Q1, First Quartile; Q3, Third Quartile. The unit of measurement for the 95% HD is millimeters (mm).
- Other CT manufacturers include Toshiba and PNS.
Lastly, the DSC and 95% HD performance of individual OARs, alongside their Referenced Performance benchmarks, which represent the target performance for each OAR are shown in Table G.
| OAR | DSC Performance | 95% HD Performance | ||||
|---|---|---|---|---|---|---|
| EFAI HCAPSeg | ReferencedPerformance § | EFAI HCAPSeg | ReferencedPerformance § | |||
| Mean DSC | SD | Mean DSC | Median 95%HD (mm) | Q1 - Q3 (mm) | Median95% HD | |
| A_Aorta | 0.86 | 0.04 | 0.26 | 2.36 | 2.08 - 3.43 | 66.60 |
| A_Carotid_L † | 0.71 | 0.20 | 0.65 | 5.46 | 2.21 - 10.13 | ≤3.50 |
| A_Carotid_R † | 0.67 | 0.21 | 0.65 | 5.63 | 2.52 - 8.14 | ≤3.50 |
| Bladder | 0.77 | 0.30 | 0.70 | 4.93 | 3.16 - 8.54 | 6.22 |
| Bone_Mandible | 0.89 | 0.04 | 0.81 | 2.23 | 2.05 - 2.35 | 4.32 |
| BrachialPlex_L | 0.72 | 0.02 | 0.63 | 2.22 | 2.04 - 2.40 | 8.45 |
| BrachialPlex_R | 0.71 | 0.03 | 0.62 | 2.25 | 2.13 - 3.33 | 9.67 |
| Brain | 0.99 | 0.04 | 0.86 | 1.36 | 1.15 - 2.18 | 3.96 |
| Brainstem | 0.92 | 0.07 | 0.75 | 3.17 | 2.49 - 3.47 | 5.75 |
| Breast_L | 0.89 | 0.03 | 0.78 | 3.15 | 1.60 - 6.33 | 10.86 |
| Breast_R | 0.89 | 0.03 | 0.78 | 2.38 | 2.14 - 4.76 | 11.54 |
| Bronchus_Prox† | 0.93 | 0.03 | 0.65 | 1.00 | 1.00 - 2.99 | ≤3.50 |
| Cavity_Oral | 0.92 | 0.12 | 0.78 | 2.28 | 1.35 - 4.13 | 7.70 |
| Cochlea_L | 0.65 | 0.26 | 0.45 | 2.11 | 1.35 - 2.32 | 3.96 |
| Cochlea_R | 0.70 | 0.21 | 0.51 | 2.09 | 1.33 - 2.33 | 3.96 |
| Duodenum | 0.74 | 0.24 | 0.57 | 6.49 | 2.44 - 20.81 | 27.41 |
| Ear_Internal_L | 0.85 | 0.08 | 0.71 | 1.48 | 1.24 - 2.25 | 3.97 |
| Ear_Internal_R | 0.84 | 0.14 | 0.69 | 1.46 | 1.22 - 2.24 | 3.97 |
| Ear_Middle_L | 0.83 | 0.16 | 0.67 | 2.15 | 1.37 - 2.30 | 4.87 |
| Ear_Middle_R | 0.84 | 0.16 | 0.68 | 1.43 | 1.23 - 2.19 | 3.97 |
| Esophagus | 0.84 | 0.08 | 0.71 | 2.50 | 2.08 - 7.36 | 6.01 |
| Eye_L | 0.94 | 0.14 | 0.80 | 1.37 | 1.23 - 2.21 | 3.95 |
| Eye_R | 0.92 | 0.19 | 0.79 | 1.33 | 1.16 - 2.30 | 3.95 |
| FemurHeadNeck_L | 0.95 | 0.15 | 0.82 | 2.21 | 1.37 - 2.45 | 4.86 |
| FemurHeadNeck_R | 0.92 | 0.14 | 0.76 | 2.30 | 1.42 - 3.02 | 5.69 |
| Gallbladder† | 0.81 | 0.31 | 0.65 | 1.00 | 1.00 - 2.93 | ≤3.50 |
| Glnd_Submand_L | 0.82 | 0.08 | 0.69 | 1.40 | 1.16 - 2.17 | 5.80 |
| Glnd_Submand_R | 0.74 | 0.19 | 0.67 | 1.35 | 1.21 - 2.10 | 5.83 |
| Glnd_Thyroid | 0.79 | 0.16 | 0.60 | 2.24 | 1.91 - 2.59 | 8.60 |
| Heart | 0.93 | 0.08 | 0.84 | 2.25 | 1.42 - 3.32 | 4.91 |
| Hippocampus_L | 0.69 | 0.27 | 0.54 | 1.50 | 1.28 - 2.29 | 5.13 |
| Hippocampus_R | 0.55 | 0.34 | 0.45 | 1.52 | 1.29 - 2.49 | 5.88 |
| IAC_L | 0.60 | 0.34 | 0.46 | 2.39 | 2.20 - 3.18 | 5.85 |
| IAC_R | 0.66 | 0.31 | 0.41 | 2.37 | 2.16 - 2.77 | 3.97 |
| Joint_TM_L | 0.63 | 0.28 | 0.50 | 3.06 | 2.36 - 3.45 | 4.93 |
| Joint_TM_R | 0.67 | 0.18 | 0.49 | 3.10 | 2.49 - 3.65 | 4.90 |
| Kidney_L | 0.93 | 0.17 | 0.78 | 1.36 | 1.21 - 2.37 | 4.88 |
| Kidney_R | 0.95 | 0.14 | 0.79 | 1.34 | 1.15 - 1.89 | 4.38 |
| Larynx | 0.83 | 0.11 | 0.66 | 2.44 | 2.18 - 3.54 | 6.46 |
| Lens_L | 0.78 | 0.28 | 0.69 | 2.11 | 1.43 - 2.26 | 3.96 |
| Lens_R | 0.80 | 0.31 | 0.70 | 2.20 | 1.44 - 2.39 | 3.95 |
| Liver | 0.95 | 0.02 | 0.86 | 2.02 | 1.31 - 2.41 | 4.95 |
| LN_Neck_IA† | 0.83 | 0.06 | 0.65 | 2.00 | 1.00 - 2.73 | ≤3.50 |
| LN_Neck_IB_L† | 0.90 | 0.03 | 0.65 | 1.87 | 1.10 - 2.24 | ≤3.50 |
| LN_Neck_IB_R† | 0.88 | 0.04 | 0.65 | 2.00 | 1.80 - 2.95 | ≤3.50 |
| LN_Neck_II_L† | 0.89 | 0.04 | 0.65 | 1.73 | 1.41 - 2.73 | ≤3.50 |
| LN_Neck_II_R† | 0.87 | 0.06 | 0.65 | 2.24 | 1.41 - 3.00 | ≤3.50 |
| LN_Neck_III_L† | 0.81 | 0.09 | 0.65 | 2.94 | 2.02 - 4.51 | ≤3.50 |
| LN_Neck_III_R† | 0.84 | 0.07 | 0.65 | 2.53 | 1.56 - 3.74 | ≤3.50 |
| LN_Neck_IV_L† | 0.81 | 0.11 | 0.65 | 3.10 | 2.19 - 4.16 | ≤3.50 |
| LN_Neck_IV_R† | 0.81 | 0.08 | 0.65 | 3.02 | 2.40 - 4.16 | ≤3.50 |
| LN_Neck_V_L† | 0.84 | 0.06 | 0.65 | 2.94 | 2.02 - 5.57 | ≤3.50 |
| LN_Neck_V_R† | 0.86 | 0.04 | 0.65 | 2.91 | 2.00 - 3.57 | ≤3.50 |
| LN_Pelvics† | 0.90 | 0.23 | 0.65 | 1.00 | 1.00 - 2.99 | ≤3.50 |
| Lobe_Temporal_L | 0.94 | 0.03 | 0.77 | 1.43 | 1.21 - 2.21 | 4.91 |
| Lobe_Temporal_R | 0.93 | 0.08 | 0.76 | 2.03 | 1.30 - 2.33 | 4.90 |
| Lung_L | 0.96 | 0.01 | 0.87 | 2.27 | 2.02 - 2.42 | 4.32 |
| Lung_R | 0.95 | 0.01 | 0.88 | 2.05 | 1.25 - 2.38 | 3.97 |
| OpticChiasm | 0.75 | 0.24 | 0.60 | 2.30 | 2.10 - 2.52 | 5.13 |
| OpticNrv_L | 0.68 | 0.25 | 0.56 | 2.24 | 1.59 - 2.36 | 3.97 |
| OpticNrv_R | 0.68 | 0.22 | 0.54 | 2.23 | 1.88 - 2.46 | 4.91 |
| Pancreas | 0.83 | 0.16 | 0.63 | 2.82 | 2.12 - 7.89 | 10.63 |
| Parotid_L | 0.87 | 0.14 | 0.74 | 2.08 | 1.38 - 2.27 | 6.52 |
| Parotid_R | 0.86 | 0.12 | 0.73 | 2.04 | 1.31 - 2.40 | 7.11 |
| PenileBulb† | 0.66 | 0.34 | 0.50 | 2.30 | 0.98 - 4.25 | ≤3.50 |
| Pituitary | 0.67 | 0.39 | 0.57 | 2.02 | 2.02 - 2.02 | 3.97 |
| Prostate | 0.79 | 0.21 | 0.41 | 2.43 | 2.10 - 5.10 | 7.36 |
| Rectum | 0.84 | 0.08 | 0.51 | 2.92 | 2.30 - 5.32 | 10.33 |
| SeminalVes | 0.92 | 0.12 | 0.46 | 2.07 | 1.37 - 3.57 | 6.04 |
| Spc_Bowel | 0.87 | 0.18 | 0.57 | 8.07 | 5.63 - 14.46 | 26.64 |
| SpinalCanal | 0.88 | 0.03 | 0.73 | 2.30 | 2.14 - 2.47 | 8.92 |
| SpinalCord | 0.90 | 0.02 | 0.78 | 2.16 | 1.43 - 2.37 | 5.84 |
| Spleen | 0.93 | 0.15 | 0.79 | 1.45 | 1.21 - 2.39 | 4.94 |
| Stomach | 0.85 | 0.12 | 0.80 | 2.89 | 2.22 - 7.10 | 5.82 |
| Testis | 0.82 | 0.29 | 0.31 | 5.46 | 3.16 - 22.56 | 40.74 |
| Trachea | 0.91 | 0.03 | 0.79 | 2.30 | 2.08 - 2.88 | 5.13 |
| Uterus † | 0.78 | 0.29 | 0.80 | 3.85 | 2.91 - 12.38 | $\leq$ 3.50 |
| V_Venacava_I † | 0.93 | 0.10 | 0.65 | 1.00 | 1.00 - 3.01 | $\leq$ 3.50 |
| Vestibule_L | 0.79 | 0.25 | 0.54 | 1.47 | 1.29 - 2.25 | 3.96 |
| Vestibule_R | 0.76 | 0.16 | 0.55 | 2.15 | 1.41 - 2.31 | 3.97 |
Table G. Individual OAR performance of EFAI HCAPSeg (Proposed Device)
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Image /page/17/Picture/0 description: The image shows the logo for Ever Fortune AI. The logo consists of a stylized human figure with a circular head made of interconnected dots, all in a teal color. To the right of the figure, the words "EVER" and "FORTUNE.AI" are written in a similar teal color, with the "FORTUNE.AI" text positioned below "EVER".
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Image /page/18/Picture/0 description: The image shows the logo for Ever Fortune AI. The logo consists of a stylized human figure with a green sphere containing a network of white lines and dots for a head. To the right of the figure is the text "EVER" in a larger font, with "FORTUNE.AI" below it in a smaller font, all in a matching teal color.
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Image /page/19/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 network-like sphere for a head. To the right of the figure, the words "EVER" and "FORTUNE.AI" are written in teal. The word "FORTUNE.AI" also has a network-like sphere in place of the letter "O".
OAR, Organ-At-Risk; DSC, Dice Similarity Coefficient. HD, Hausdorff Distance; Q1, First Quartile; Q3, Third Quartile.
The unit of measurement for the 95% HD is millimeters (mm).
- OARs that are unique to EFAI HCAPSeg.
s The referenced performance acts as the performance target for each individual OAR. The benchmance for OARs that are presented in both EFAI HCAPSeg and the comparison device is established as being non-inferior to the comparison device. As for OARs unique to EFAI HCAPSeg, the benchmark DSC performance is determined using values of 0.80/0.65/0.50 for large/medium/small volume structures.
8. Conclusion
Based on the information submitted in this premarket notification, and based on the intended use, technological characteristics, and performance testing including the nonclinical tests, the EFAI HCAPSeg raises no new questions of safety and effectiveness and is substantially equivalent to the predicate device in terms of safety, effectiveness, and performance.
§ 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).