AI/ML, Software as a Medical Device, Real-World Evidence
Real-World Evidence
Submission
Device
Sponsor
RWD Sources
RWE Use Summary
Key Tags
K231025 · Oct 4, 2023
EFAI NeuroSuite CT ICH Assessment System
Ever Fortune.Ai, Co., Ltd.
Retrospective clinical CT studies from 23 U.S. clinical sites
A retrospective, multisite clinical validation study was conducted to evaluate the performance of the EFAI ICHCT in identifying intracranial hemorrhage (ICH) findings from non-contrast head CT scans.
Retrospective study; Clinical validation; Multisite; Non-contrast head CT
EFAI ICHCT is a software workflow tool designed to aid in prioritizing the clinical assessment of adult non-contrast head CT cases with features suggestive of acute intracranial hemorrhage (ICH). EFAI ICHCT analyzes cases using deep learning algorithms to identify suspected ICH findings. It makes case-level output available to a PACS/workstation for worklist prioritization or triage. EFAI ICHCT is not intended to direct attention to specific portions of an image or to anomalies other than acute ICH. Its results are not intended to be used on a stand-alone basis for clinical decision-making nor is it intended to rule out hemorrhage or otherwise preclude clinical assessment of CT studies.
Device Story
EFAI NeuroSuite CT ICH Assessment System (EFAI ICHCT) is a radiological computer-assisted triage and notification software. It ingests non-contrast head CT images from DICOM-compliant modalities; processes data using deep learning algorithms to detect features suggestive of acute intracranial hemorrhage (ICH); and outputs a case-level notification (Yes/No) to a PACS/workstation. Used in clinical settings by radiologists/clinicians to prioritize worklists; the device does not highlight specific image regions or anomalies. It functions as a passive notification tool; does not remove cases from the standard-of-care workflow. Benefits include earlier review of potential ICH cases by radiologists, potentially improving clinical assessment speed. The system operates independently of the clinician during analysis.
Clinical Evidence
Retrospective, blinded, multisite clinical validation study using 288 U.S. CT studies (132 ICH positive, 156 ICH negative) from 23 sites. Ground truth established by majority agreement of three board-certified neuroradiologists. Primary endpoints: sensitivity and specificity. Results: Sensitivity 0.947 (95% CI: 0.895-0.974), Specificity 0.949 (95% CI: 0.902-0.974), AUROC 0.983 (95% CI: 0.969-0.997). Subgroup analysis confirmed consistent performance across age, gender, CT manufacturer, and slice thickness. Processing time averaged 34.96 seconds.
Technological Characteristics
Software-only device; deep learning algorithm; DICOM-compliant input; PACS/workstation integration. Complies with IEC 62304:2006/A1:2016 for software life cycle. No hardware components. Operates on standard radiological imaging infrastructure.
Indications for Use
Indicated for adult patients undergoing non-contrast head CT scans to aid in prioritizing clinical assessment of cases with features suggestive of acute intracranial hemorrhage. Not for stand-alone clinical decision-making, ruling out hemorrhage, or directing attention to specific image regions.
Regulatory Classification
Identification
Radiological computer aided triage and notification software is an image processing prescription device intended to aid in prioritization and triage of radiological medical images. The device notifies a designated list of clinicians of the availability of time sensitive radiological medical images for review based on computer aided image analysis of those images performed by the device. The device does not mark, highlight, or direct users' attention to a specific location in the original image. The device does not remove cases from a reading queue. The device operates in parallel with the standard of care, which remains the default option for all cases.
Special Controls
Radiological computer aided triage and notification software must comply with the following special controls: 1. Design verification and validation must include: i. A detailed description of the notification and triage algorithms and all underlying image analysis algorithms including, but not limited to, a detailed description of the algorithm inputs and outputs, each major component or block, how the algorithm affects or relates to clinical practice or patient care, and any algorithm limitations. ii. A detailed description of pre-specified performance testing protocols and dataset(s) used to assess whether the device will provide effective triage (e.g., improved time to review of prioritized images for pre-specified clinicians). iii. Results from performance testing that demonstrate that the device will provide effective triage. The performance assessment must be based on an appropriate measure to estimate the clinical effectiveness. The test dataset must contain sufficient numbers of cases from important cohorts (e.g., subsets defined by clinically relevant confounders, effect modifiers, associated diseases, and subsets defined by image acquisition characteristics) such that the performance estimates and confidence intervals for these individual subsets can be characterized with the device for the intended use population and imaging equipment. iv. Standalone performance testing protocols and results of the device. v. Appropriate software documentation (e.g., device hazard analysis; software requirements specification document; software design specification document; traceability analysis; description of verification and validation activities including system level test protocol, pass/fail criteria, and results). 2. Labeling must include the following: i. A detailed description of the patient population for which the device is indicated for use. ii. A detailed description of the intended user and user training that addresses appropriate use protocols for the device. iii. Discussion of warnings, precautions, and limitations must include situations in which the device may fail or may not operate at its expected performance level (e.g., poor image quality for certain subpopulations), as applicable. iv. A detailed description of compatible imaging hardware, imaging protocols, and requirements for input images. v. Device operating instructions. vi. A detailed summary of the performance testing, including: test methods, dataset characteristics, triage effectiveness (e.g., improved time to review of prioritized images for pre-specified clinicians), diagnostic accuracy of algorithms informing triage decision, and results with associated statistical uncertainty (e.g., confidence intervals), including a summary of subanalyses on case distributions stratified by relevant confounders, such as lesion and organ characteristics, disease stages, and imaging equipment.
*Classification.* Class II (special controls). The special controls for this device are:(1) Design verification and validation must include:
(i) A detailed description of the notification and triage algorithms and all underlying image analysis algorithms including, but not limited to, a detailed description of the algorithm inputs and outputs, each major component or block, how the algorithm affects or relates to clinical practice or patient care, and any algorithm limitations.
(ii) A detailed description of pre-specified performance testing protocols and dataset(s) used to assess whether the device will provide effective triage (
*e.g.,* improved time to review of prioritized images for pre-specified clinicians).(iii) Results from performance testing that demonstrate that the device will provide effective triage. The performance assessment must be based on an appropriate measure to estimate the clinical effectiveness. The test dataset must contain sufficient numbers of cases from important cohorts (
*e.g.,* subsets defined by clinically relevant confounders, effect modifiers, associated diseases, and subsets defined by image acquisition characteristics) such that the performance estimates and confidence intervals for these individual subsets can be characterized with the device for the intended use population and imaging equipment.(iv) Stand-alone performance testing protocols and results of the device.
(v) Appropriate software documentation (
*e.g.,* device hazard analysis; software requirements specification document; software design specification document; traceability analysis; description of verification and validation activities including system level test protocol, pass/fail criteria, and results).(2) Labeling must include the following:
(i) A detailed description of the patient population for which the device is indicated for use;
(ii) A detailed description of the intended user and user training that addresses appropriate use protocols for the device;
(iii) Discussion of warnings, precautions, and limitations must include situations in which the device may fail or may not operate at its expected performance level (
*e.g.,* poor image quality for certain subpopulations), as applicable;(iv) A detailed description of compatible imaging hardware, imaging protocols, and requirements for input images;
(v) Device operating instructions; and
(vi) A detailed summary of the performance testing, including: test methods, dataset characteristics, triage effectiveness (
*e.g.,* improved time to review of prioritized images for pre-specified clinicians), diagnostic accuracy of algorithms informing triage decision, and results with associated statistical uncertainty (*e.g.,* confidence intervals), including a summary of subanalyses on case distributions stratified by relevant confounders, such as lesion and organ characteristics, disease stages, and imaging equipment.
{0}------------------------------------------------
Image /page/0/Picture/0 description: The image contains the logos of the Department of Health & Human Services and the Food and Drug Administration (FDA). The Department of Health & Human Services logo is on the left, and the FDA logo is on the right. The FDA logo includes the text "U.S. Food & Drug Administration" in blue.
Ever Fortune.AI Co., Ltd. Ti-Hao Wang Chief Technology Officer 8F., No. 573, Sec. 2, Taiwan Blvd., West Dist. Taichung City, 403020 Taiwan
October 4, 2023
#### Re: K231025
Trade/Device Name: EFAI NeuroSuite CT ICH Assessment System Regulation Number: 21 CFR 892.2080 Regulation Name: Radiological Computer Aided Triage And Notification Software Regulatory Class: Class II Product Code: QAS Dated: September 4, 2023 Received: September 5, 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 (the 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 available 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.
Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device" (https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).
{1}------------------------------------------------
Your device is also subject to, among other requirements, the Quality System (QS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30, Design controls; 21 CFR 820.90, Nonconforming product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review, the QS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181).
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 Part 803) for devices or postmarketing safety reporting (21 CFR Part 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 Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 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.
Jessica Lamb
Jessica Lamb Assistant Director DHT8B: Division of Imaging Devices and Electronic Products OHT8: Office of Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
{2}------------------------------------------------
#### Indications for Use
510(k) Number (if known) K231025
#### Device Name EFAI NEUROSUITE CT ICH ASSESSMENT SYSTEM
Indications for Use (Describe)
EFAI ICHCT is a software workflow tool designed to aid in prioritizing the clinical assessment of adult non-contrast head CT cases with features suggestive of acute intracranial hemorrhage (ICH). EFAI ICHCT analyzes cases using deep learning algorithms to identify suspected ICH findings. It makes case-level output available to a PACS/workstation for worklist prioritization or triage.
EFAI ICHCT is not intended to direct attention to specific portions of an image or to anomalies other than acute ICH. Its results are not intended to be used on a stand-alone basis for clinical decision-making nor is it intended to rule out hemorrhage or otherwise preclude clinical assessment of CT studies.
| Type of Use (Select one or both, as applicable) | |
|-------------------------------------------------|--|
|-------------------------------------------------|--|
× Prescription Use (Part 21 CFR 801 Subpart D)
Over-The-Counter Use (21 CFR 801 Subpart C)
#### CONTINUE ON A SEPARATE PAGE IF NEEDED.
This section applies only to requirements of the Paperwork Reduction Act of 1995.
#### *DO NOT SEND YOUR COMPLETED FORM TO THE PRA STAFF EMAIL ADDRESS BELOW.*
The burden time for this collection of information is estimated to average 79 hours per response, including the time to review instructions, search existing data sources, gather and maintain the data needed and complete and review the collection of information. Send comments regarding this burden estimate or any other aspect of this information collection, including suggestions for reducing this burden, to:
> Department of Health and Human Services Food and Drug Administration Office of Chief Information Officer Paperwork Reduction Act (PRA) Staff PRAStaff(@fda.hhs.gov
"An agency may not conduct or sponsor, and a person is not required to respond to, a collection of information unless it displays a currently valid OMB number.
{3}------------------------------------------------
Image /page/3/Picture/0 description: The image shows the logo for Ever Fortune AI. The logo consists of a stylized person icon in teal with a green globe with a network pattern on top of the person icon. To the right of the icon is the text "EVER" in teal, with "FORTUNE.AI" in teal below it. The globe icon is also incorporated into the "O" in "FORTUNE".
### 510(k) Summary
K231025
## 1. General Information
| 510(k) Sponsor | Ever Fortune.AI Co., Ltd. |
|-----------------------|-----------------------------------------------------------------------------------------|
| Address | Rm. D, 8F. No. 573, Sec. 2 Taiwan Blvd.<br>West Dist.<br>Taichung City 403020<br>TAIWAN |
| Applicant | Joseph Chang |
| Contact Information | 886-04-23213838 #216<br>joseph.chang@everfortune.ai |
| Correspondence Person | Ti-Hao Wang, MD |
| Contact Information | 886-04-23213838 #168<br>tihao.wang@everfortune.ai |
| Date Prepared | April 10, 2023 |
### 2. Proposed Device
| Proprietary Name | EFAI NEUROSUITE CT ICH ASSESSMENT SYSTEM |
|---------------------|-----------------------------------------------------------------|
| Common Name | EFAI ICHCT100 |
| Classification Name | Radiological Computer-Assisted Triage And Notification Software |
| Regulation Number | 21 CFR 892.2080 |
| Regulation Name | Radiological Computer Aided Triage and Notification Software |
| Product Code | QAS |
| Regulatory Class | II |
# 3. Predicate Device
| Proprietary Name | CuraRad-ICH |
|------------------------|-----------------------------------------------------------------|
| Premarket Notification | K192167 |
| Classification Name | Radiological Computer-Assisted Triage And Notification Software |
| Regulation Number | 21 CFR 892.2080 |
| Regulation Name | Radiological Computer Aided Triage and Notification Software |
| Product Code | QAS |
| Regulatory Class | II |
{4}------------------------------------------------
Image /page/4/Picture/0 description: The image shows the logo for Ever Fortune AI. The logo consists of a teal-colored abstract figure that resembles a person with a circle above their head. The circle contains a green network of lines and dots. To the right of the figure is the text "EVER FORTUNE.AI" in a teal color.
## 4. Device Description
EFAI NEUROSUITE CT ICH ASSESSMENT SYSTEM (EFAI ICHCT) is a radiological computer-assisted triage and notification software system. The software uses deep learning techniques to automatically analyze non-contrast head CTs and alerts the PACS/RIS workstation once images with features suggestive of acute ICH are identified.
During the process of model development, a total of 5,365 adult cases were retrospectively collected between 2010 and 2018 from Taiwan. These cases were subsequently divided into training, validation, and testing datasets, consisting of 3,776, 1,038, and 551 cases, respectively.
Through the use of EFAI ICHCT, a radiologist is able to review studies with features suggestive of acute ICH earlier than in standard of care workflow.
The device is intended to provide a passive notification through the PACS/workstation to the radiologists indicating the existence of a case that may potentially benefit from the prioritization. It does not mark, highlight, or direct users' attention to a specific location on the original non-contrast head CT. The device aims to aid in prioritization and triage of radiological medical images only.
## 5. Intended Use / Indications for Use
EFAI ICHCT is a software workflow tool designed to aid in prioritizing the clinical assessment of adult non-contrast head CT cases with features suggestive of acute intracranial hemorrhage (ICH). EFAI ICHCT analyzes cases using deep learning algorithms to identify suspected ICH findings. It makes case-level output available to a PACS/workstation for worklist prioritization or triage.
EFAI ICHCT is not intended to direct attention to specific portions of an image or to anomalies other than acute ICH. Its results are not intended to be used on a stand-alone basis for clinical decision-making nor is it intended to rule out hemorrhage or otherwise preclude clinical assessment of CT studies.
{5}------------------------------------------------
Image /page/5/Picture/0 description: The image shows a logo for a company called EVER FORTUNE.AI. The logo consists of a stylized human figure with a green globe as its head, and the company name is written in teal next to it. The word "FORTUNE" has a globe in place of the letter "O".
# 6. Comparison of Technological Characteristics with Predicate Device
Table below provides a comparison of the intended use and key technological features of EFAI ICHCT with that of the Primary Predicate, CuraRad-ICH (K192167).
| Company | Ever Fortune.AI Co., Ltd.<br>(EFAI) | CuraCloud Corp. |
|------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Device Name | EFAI ICHCT | CuraRad-ICH |
| 510k Number | K231025 | K192167 |
| Regulation No. | 21CFR 892.2080 | 21CFR 892.2080 |
| Classification | II | II |
| Product Code | QAS | QAS |
| Intended<br>Use/Indication for<br>Use | EFAI ICHCT is a software<br>workflow tool designed to aid in<br>prioritizing the clinical<br>assessment of adult non-contrast<br>head CT cases with features<br>suggestive of acute intracranial<br>hemorrhage (ICH). EFAI<br>ICHCT analyzes cases using<br>deep learning algorithms to<br>identify suspected ICH findings.<br>It makes case-level output<br>available to a PACS/workstation<br>for worklist prioritization or<br>triage.<br><br>EFAI ICHCT is not intended to<br>direct attention to specific<br>portions of an image or to<br>anomalies other than acute ICH.<br>Its results are not intended to be<br>used on a stand-alone basis for<br>clinical decision-making nor is it<br>intended to rule out hemorrhage<br>or otherwise preclude clinical<br>assessment of CT studies. | CuraRad-ICH is a software<br>workflow tool designed to aid<br>in prioritizing the clinical<br>assessment of adult<br>non-contrast head CT cases<br>with features suggestive of<br>acute intracranial hemorrhage.<br>CuraRad-ICH analyzes cases<br>using deep learning algorithms<br>to identify suspected ICH<br>findings. It makes case-level<br>output available to a<br>PACS/workstation for worklist<br>prioritization or triage.<br><br>CuraRad-ICH is not intended to<br>direct attention to specific<br>portions of an image or to<br>anomalies other than acute ICH.<br>Its results are not intended to be<br>used on a stand-alone basis for<br>clinical decision-making nor is it<br>intended to rule out<br>hemorrhage or otherwise<br>preclude clinical assessment of<br>CT studies. |
| Population | Adult patients indicated for<br>non-contrast head CT | Adult patients indicated for<br>non-contrast head CT |
| Intended Clinical<br>End User | Radiologists/Trained Clinicians | Radiologists/Trained Clinicians |
| AI Used | Yes | Yes |
| Data Acquisition | Acquires medical image data<br>from DICOM compliant<br>imaging devices and modalities. | Acquires medical image data<br>from DICOM compliant<br>imaging devices and modalities. |
| Input Image<br>Modality | Non-contrast Head CT | Non-contrast Head CT |
| Non-Diagnostic<br>Preview | No | No |
| Clinical condition | Acute Intracranial Hemorrhage | Acute Intracranial Hemorrhage |
| Independent of<br>standard of care<br>workflow | Yes; No cases are removed from<br>worklist | Yes; No cases are removed<br>from worklist |
| Output | Suspected ICH (Yes or No) | Suspected ICH (Yes or No) |
| Results Receiver | PACS / Workstation | PACS / Workstation |
| Performance<br>Results | Sensitivity: 0.947<br>(95% CI: 0.895 - 0.974) | Sensitivity: 0.906<br>(95% CI: 0.859 - 0.942) |
| | Specificity: 0.949<br>(95% CI: 0.902 - 0.974) | Specificity: 0.931<br>(95% CI: 0.883 - 0.964) |
| | Processing time: 34.96 seconds<br>(95% CI: 33.89 - 36.03 seconds) | Processing time: 43 seconds<br>(95% CI: 39 - 46 seconds) |
Table - Comparison with the Predicate Device.
{6}------------------------------------------------
Image /page/6/Picture/0 description: The image shows the logo for Ever Fortune AI. The logo consists of a teal-colored icon that resembles a person with a globe as their head, and the text "EVER FORTUNE.AI" in teal. The globe on the icon and the "O" in "FORTUNE" are both designed with a network of interconnected dots, suggesting a focus on technology and global connectivity.
The proposed device, EFAI ICHCT, is substantially equivalent to the claimed predicate, CuraRad-ICH (K192167).
{7}------------------------------------------------
Image /page/7/Picture/0 description: The image shows the logo for Ever Fortune AI. The logo consists of a teal-colored abstract figure with a circle on top, resembling a person. The circle on top has a green network design inside. To the right of the figure is the text "EVER" in teal, with "FORTUNE.AI" below it, also in teal. The "O" in fortune is replaced with a similar green network design.
### 7. Performance Data
Performance of the EFAI ICHCT has been evaluated and verified in accordance with software specifications and applicable performance standards through software verification and validation testing. Additionally, the software validation activities were performed in accordance with IEC 62304:2006/A1:2016 - Medical device software - Software life cycle processes, in addition to the FDA Guidance documents, "Guidance for the Content of Premarket Submissions for Software in Medical Devices"(2005), and "Content of Premarket Submission for Management of Cybersecurity in Medical Devices."
Ever Fortune.AI conducted a retrospective, blinded, multisite clinical validation study with the proposed device EFAI ICHCT with a pre-determined primary and secondary endpoint and performance goals to evaluate the performance of the EFAI ICHCT in identifying intracranial hemorrhage (ICH) findings from non-contrast head computed tomography (CT) scans on a validation dataset of 288 CT studies (132 ICH positives and 156 ICH negatives) consecutively collected from 23 clinical sites in the United States (U.S.). Each patient included only one CT study. None of the studies was used as part of the EFAI ICHCT model development or analytical validation testing.
The study population contained 49.31% females and 50.69% males; the mean age of cases was 59.43 years. The ethnic and racial distribution includes 52.43% White, 10.42% Asian, 9.38% Black or African American, 7.99% Hispanic, and 19.79% others. The CT scanner manufacturers of images were acquired from Toshiba, Hitachi, Philips, Siemens, GE Medical Systems, and Canon. The CT is taken in a standard brain CT protocol.
The presence of ICH in each case was determined independently by three U.S. board-certified neuroradiologists, and the reference standard (ground truth) was generated by the majority agreement between the three experts. The performance acceptance criteria were set such that the lower bounds of 95% confidence intervals of both sensitivity and specificity should exceed 0.8.
The observed results of the standalone performance validation study demonstrated that EFAI ICHCT by itself, in the absence of any interaction with a clinician, can provide case-level notifications with features suggestive of ICH with satisfactory results. The EFAI ICHCT was able to demonstrate sensitivity and specificity of 0.947 (95% CI-0.895-0.974) and 0.949 (95% CI=0.902-0.974) respectively, as well as an AUROC of 0.983 (95% Cl=0.969-0.997), which is substantially equivalent to the predicate device (CuraRad-ICH, K192167). The observed system processing time per study is 34.96 seconds (95% CI: 33.89-36.03) on average and was comparable with the predicate device, CuraRad-ICH
{8}------------------------------------------------
Image /page/8/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 circle containing a network of interconnected dots above its head. To the right of the figure, the company name is written in teal, with "EVER" stacked above "FORTUNE.AI". The dot in ".AI" is green.
(CuraCloud, K192167, 43 seconds). In addition, the subgroup analysis results, which encompassed different genders, age groups, CT manufacturer groups, CT slice thickness groups, and types of ICH, revealed that EFAI ICHCT maintained a consistently high performance, indicating the device's reliability and effectiveness across diverse subgroups. No significant statistical difference was observed between EFAI ICHCT and GT. We found the device performs consistently and reliably under these circumstances. The results demonstrate that the EFAI ICHCT device is as safe and effective as the predicate device CuraRad-ICH.
### 8. Conclusion
Based on the information submitted in this premarket notification, and based on the indications for use, technological characteristics, and performance testing, the EFAI ICHCT raises no new questions of safety and effectiveness and is substantially equivalent to the predicate device in terms of safety, effectiveness, and performance.
Predicate graph will load when search results are available.
Embedding visualization will load when search results are available.
PDF viewer will load when search results are available.
Loading panels...
Select an item from Submissions
Click any panel, subpart, regulation, product code, or device to see details here.
Section Matches
Results will appear here.
Product Code Matches
Results will appear here.
Special Control Matches
Results will appear here.
Loading collections...
Loading
My Alerts
You will receive email notifications based on the filters and frequency you set for each alert.
Sort by:
Create Alert
Search Filters
Agent Token
Create a read-only bearer token for Claude, ChatGPT, or other agents that can call HTTP APIs.
Copy this now. It will not be shown again.
Connected apps
Apps you authorized through browser sign-in. Disconnecting revokes their access immediately.
Learn the FDA Browser
Two short videos show you everything — or skip straight to the written tutorial if you'd rather read. You can reopen this any time from the Tutorial button in the top bar.
Part 1 — Search, results, and everyday workflows 16 min
Part 2 — Embeddings: the galaxy map 3 min
1. Search: exact and fuzzy
Type a phrase like "coronary artery calcification" into the search box. You get two kinds of results. Exact results match the literal phrase — prefix searches work ("coronary artery calcificati") but suffix searches do not. Fuzzy results match on the meaning and intent of your phrase rather than the exact words, and are sorted by relevance score. Hover over the Exact or Fuzzy badge on any row to see exactly why it matched.
Use the checkboxes above the results to narrow: SaMD keeps only software-only devices, AI / ML keeps only devices with AI.
Exact vs. fuzzy search: what's the difference?
Exact matches on the literal phrase (prefix search works, suffix does not). Fuzzy matches on the meaning and intent of the phrase rather than the exact words. Hover over the badge on any row to see why it matched.
You search "coronary artery calcification" and want only software devices with AI. What two filters do you apply?
Narrow by SaMD (software-only devices), then narrow by AI/ML (devices with AI).
2. The results table
Scroll right in the results table. The intended use is extracted for you — no need to open the PDF. The device story gives a high-level snapshot of what the device does and how it's used. The AI Performance sub-table shows each output name, acceptance criteria, observed values, and development/test dataset descriptions — the same format Innolitics uses for regulatory strategy outputs, and the fastest high-level fingerprint of an AI device. It is AI-generated but has been very reliable in practice.
Where do you find a device's intended use without opening the PDF?
Scroll right in the search results table. The intended use column is extracted for you; no need to dig into the 510(k) summary PDF.
What does the AI Performance sub-table show, and why is it useful?
Output name, acceptance criteria, observed values, development dataset description, and test dataset description. It's the same format we use for regulatory strategy output and Fast 510(k) input, and the fastest high-level fingerprint of an AI device. AI-generated but reliable in practice.
3. Judging fuzzy relevance
Fuzzy results trail off in relevance as you scroll. Use three signals to decide how far down to go: the fuzzy badge explanations, the intended use column, and whether your target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, you're past the relevant zone. A top hit with a low score (~0.4) and a stretched explanation is a hint the closest predicates are far away — the project may be headed for De Novo. Note the fuzzy search is a pattern match: it doesn't handle negation ("not") well, and hardware devices can appear — filter by SaMD/AI ML to cut them.
How do you judge how far down fuzzy search results to go?
Use the relevancy signals: the fuzzy badge explanations, the intended use column, and whether the target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, results are trailing off in relevancy.
4. Device detail page: chat and citations
Click a device name to open its detail page: device facts on the left, a chat window on the right. Ask something like "Describe the training data". The answer carries little citation bubbles — click one to jump to the highlighted passage in the source PDF, so you can verify every AI answer against the document. There's also a Download PDF button for sharing.
How do you verify an AI chat answer on the device detail page?
Click the citation bubbles to jump to the relevant highlight in the source document.
Reading rule for every project: how many summaries do you read in full?
At least the three most relevant 510(k) or De Novo summaries, in full. After that, use targeted chat questions to confirm your memory quickly. The tool supports this professional habit — it doesn't replace it.
5. Side-by-side comparison
Select multiple rows in the results table (aim for under ~10), then open the PDF Viewer tab. Ask one question — it goes to all selected devices in parallel, each with citations. This is the fastest way to compare and contrast devices: training data, PCCP scope, how they handled adding new scanners, and so on.
What does the side-by-side PDF viewer mode do?
Select multiple devices, open the PDF viewer tab, and ask one question (e.g., "Describe the training data"). It queries all selected devices simultaneously with citations, so you can compare and contrast quickly.
6. Collections
With rows selected, go to the Collections tab and create a labeled collection (e.g., "Cobb Angle Project"). Reload that selection any time — before a client call, pull up the collection and ask questions across all of its devices at once.
How do you save a set of selected devices for later use?
Select the rows, go to the Collections tab, and create a labeled collection (e.g., "Cobb Angle Project"). You can reload the selection anytime and carry it into the PDF viewer and other tabs that support selections.
7. Product codes and the regulations tree
Click a product code in the results to jump to it in the regulations tree — identification text, sibling product codes, and devices you can open in a PDF viewer on the right. Click a regulation number to see its identification, special controls, and related product codes. You can also search by product code or regulation number at the top of the tree. Always read the special controls if any exist for your device — it broadens your search and sharpens pre-kickoff research.
What can you do from the regulations tree view?
Browse product codes and regulation numbers, read the identification text and special controls, browse sibling product codes, open device PDFs on the right, and search by product code or regulation number at the top of the tree.
8. Chart view
Click Show Chart and segment by regulation number (or product code) to see which regulations dominate your result set. Clicking a regulation takes you into the regulations tree. Great for spotting that most matches are, say, hardware laparoscopic devices — a cue to go back and filter.
How do you see which regulations dominate a search result set?
Click "Show Chart" and segment by Regulation Number. Clicking a regulation takes you to the regulations tree.
9. The predicate graph
Open the Predicates tab for a family-tree view of predicate relationships. Click a node to trace its parents and children; selections from search carry over pre-selected. Commonly predicated devices are worth reading — a lot of people predicated them for a reason. The visual lineage is also handy on client calls, e.g. to show how a predicate family evolved and justify why your predicate still holds.
In the predicate graph, why are commonly predicated devices worth reading?
A lot of people predicated them for a reason. Clicking a node traces parents and children, and selections from search carry over pre-selected.
10. Embeddings: the galaxy map
The Embeddings tab plots every matching document in a 2-D "galaxy map" where semantically similar devices cluster together. Hover or click clusters to explore, and let AI label the clusters for you. Embeddings beat product codes for grouping: two devices can carry different product codes (LLZ vs. QIH) yet do the same thing — the embedding captures the meaning of the intended use and device story. This is also exactly how retrieval-augmented generation (RAG) works under the hood, and it makes a great visual on client calls.
Try it yourself
Head to the search page and work through a few of these AI/ML fuzzy searches to build intuition: perivascular fat on CT · aortic valve calcification opportunistic screening on noncontrast CT · breast cancer prediction on digital pathology slides · autism detection · gestational age prediction · a hearing aid that can also detect a pulse · foundation model based analysis of ECG · large language models · penetration test. Watch how the relevance scores, intended use, and AI Performance tables tell you when results stop being meaningful.
