EFAI NEUROSUITE CTA LVO ASSESSMENT SYSTEM (LVO-CTA-100)
Applicant
Ever Fortune.Ai, Co., Ltd.
Product Code
QAS · Radiology
Decision Date
Oct 30, 2026
Decision
SESE
Submission Type
Traditional
Regulation
21 CFR 892.2080
Device Class
Class 2
Attributes
AI/ML, Software as a Medical Device, Real-World Evidence
Real-World Evidence
Submission
Device
Sponsor
RWD Sources
RWE Use Summary
Key Tags
K263109 · Oct 30, 2026
EFAI NEUROSUITE CTA LVO ASSESSMENT SYSTEM (LVO-CTA-100)
Ever Fortune.Ai, Co., Ltd.
Retrospective clinical head CTA images
The device performance was evaluated using a retrospective, blinded, multisite study of 313 consecutively collected clinical head CTA cases to demonstrate sensitivity, specificity, and processing time in a real-world clinical context.
Retrospective study; Clinical performance; Head CTA; Multisite
Retrospective, blinded, multisite standalone performance study: 313 head CTA cases.
3 (US board-certified radiologists)
Indications for Use
EFAI NEUROSUITE CTA LVO ASSESSMENT SYSTEM (EFAI LVO) is a radiological computer aided triage and notification software indicated for use in the analysis of CTA head images. The device is intended to assist hospital networks and trained clinicians in workflow triage by flagging and communication of suspected positive ICA, MCA-M1, or MCA-M2 Large Vessel Occlusion (LVO) findings in head CTA images. EFAI LVO uses an artificial intelligence algorithm to analyze images and highlight cases with suspected LVO in parallel to the ongoing standard of care image interpretation. The user is presented with notifications for cases with suspected LVO findings. The device does not alter the original medical image and is not intended to be used as a diagnostic device. The results of EFAI LVO are intended to be used in conjunction with other patient information and based on professional judgment, to assist with triage/prioritization of medical images. Notified clinicians are responsible for viewing full images per the standard of care.
Device Story
Software-based radiological triage system; analyzes contrast-enhanced head CTA images for suspected Large Vessel Occlusion (LVO) in ICA, MCA-M1, and MCA-M2 segments. Uses deep learning AI to process DICOM images; operates in parallel to standard-of-care interpretation. Provides passive, case-level notifications to clinicians via PACS/workstation; does not highlight specific image locations or provide image previews. Intended to assist in worklist prioritization; does not alter original images or serve as a diagnostic device. Used in hospital settings by trained clinicians to expedite review of potential LVO cases.
Clinical Evidence
Retrospective, blinded, multisite standalone study of 313 head CTA cases (171 positive, 142 negative) evaluated against a reference standard of three board-certified radiologists. Results: sensitivity 0.96 (95% CI: 0.92–0.98), specificity 0.94 (95% CI: 0.88–0.97), PPV 0.95, NPV 0.95, AUROC 0.96. Mean processing time 3.03 minutes (95% CI: 3.02–3.05). Performance consistent across demographics and imaging parameters.
Technological Characteristics
Radiological computer-aided triage and notification software (SaMD). Utilizes deep learning AI algorithms. Inputs: DICOM-compliant head CTA images. Outputs: Case-level notification for worklist prioritization. Deployment: On-premises. Compliant with IEC 62304:2006/A1:2015 and ISO 14971:2019.
Indications for Use
Indicated for use in the analysis of head CTA images to assist hospital networks and trained clinicians in workflow triage by flagging suspected ICA, MCA-M1, or MCA-M2 Large Vessel Occlusion (LVO) findings. Intended for adolescents (12-22 years) and adults (22+ years). Not for diagnostic use.
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.
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FDA
U.S. FOOD & DRUG
ADMINISTRATION
Ever Fortune.Ai, Co., Ltd.
Ti-Hao Wang
Chief Technology Officer
8f. # 360, Sec.1 Jingmao Rd., Beitun Dist.
Taichung City, 406040
Taiwan
October 30, 2026
Re: K263109
Trade/Device Name: Efai Neurosuite Cta Lvo Assessment System (lvo-cta-100)
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, 2026
Received: September 4, 2026
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.
Before making any change significantly affecting the safety or effectiveness of the device, you must submit a new premarket notification in accordance with 21 CFR 807.81. Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding
U.S. Food & Drug Administration
10903 New Hampshire Avenue
Silver Spring, MD 20993
www.fda.gov
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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).
Your device is also subject to, among other requirements, the Quality Management System Regulation (QMSR) (21 CFR Part 820), which includes, but is not limited to, ISO 13485 clause 7.3 (Design controls), ISO 13485 clause 8.3 (Nonconforming product), ISO 13485 clause 8.5.2 (Corrective action), and ISO 13485 clause 8.5.3 (Preventative action). Please note that regardless of whether a change requires premarket review, the QMSR requires device manufacturers to review and approve changes to device design and production (ISO 13485 clause 7.3 and ISO 13485 clause 7.5) and document changes and approvals in the Medical Device File (ISO 13485 clause 4.2.3).
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 (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-reporting-combination-products); good manufacturing practice requirements as set forth in the Quality Management System Regulation (QMSR) (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.
All medical devices, including Class I and unclassified devices and combination product device constituent parts are required to be in compliance with the final Unique Device Identification System rule ("UDI Rule"). The UDI Rule requires, among other things, that a device bear a unique device identifier (UDI) on its label and package (21 CFR 801.20(a)) unless an exception or alternative applies (21 CFR 801.20(b)) and that the dates on the device label be formatted in accordance with 21 CFR 801.18. The UDI Rule (21 CFR 830.300(a) and 830.320(b)) also requires that certain information be submitted to the Global Unique Device Identification Database (GUDID) (21 CFR Part 830 Subpart E). For additional information on these requirements, please see the UDI System webpage at https://www.fda.gov/medical-devices/device-advice-comprehensive-regulatory-assistance/unique-device-identification-system-udi-system.
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-devices/medical-device-safety/medical-device-reporting-mdr-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/medical-devices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Office of Industry and Consumer Education (OICE) to ask a question about a specific regulatory topic. See the OICE website (https://www.fda.gov/medical-devices/device-advice-comprehensive-regulatory-
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K263109 - Ti-Hao Wang
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assistance/contact-us-office-industry-and-consumer-education-oice) for more information or contact OICE by email (AskOICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely,
Jessica Lamb, Ph.D.
Assistant Director
Imaging Software Team
DHT8B: Division of Radiological Imaging
Devices and Electronic Products
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
| Please type in the marketing application/submission number, if it is known. This textbox will be left blank for original applications/submissions. | | K263109 | ? |
| --- | --- | --- | --- |
| Please provide the device trade name(s). | | | ? |
| EFAI NEUROSUITE CTA LVO ASSESSMENT SYSTEM (LVO-CTA-100) | | | |
| Please provide your Indications for Use below. | | | ? |
| EFAI NEUROSUITE CTA LVO ASSESSMENT SYSTEM (EFAI LVO) is a radiological computer aided triage and notification software indicated for use in the analysis of CTA head images. The device is intended to assist hospital networks and trained clinicians in workflow triage by flagging and communication of suspected positive ICA, MCA-M1, or MCA-M2 Large Vessel Occlusion (LVO) findings in head CTA images. | | | |
| EFAI LVO uses an artificial intelligence algorithm to analyze images and highlight cases with suspected LVO in parallel to the ongoing standard of care image interpretation. The user is presented with notifications for cases with suspected LVO findings. The device does not alter the original medical image and is not intended to be used as a diagnostic device. | | | |
| The results of EFAI LVO are intended to be used in conjunction with other patient information and based on professional judgment, to assist with triage/prioritization of medical images. Notified clinicians are responsible for viewing full images per the standard of care. | | | |
| Please select the types of uses. | ☑ Prescription Use (21 CFR 801 Subpart D) | | ? |
| | ☐ Over-The-Counter Use (21 CFR 801 Subpart C) | | |
| Please select the age group(s) for which the device(s) is to be used. | ☐ Neonates/Newborns (Birth to < 29 days old) | | ? |
| | ☐ Infants (29 days old to < 2 years old) | | |
| | ☐ Children (2 years old to < 12 years old) | | |
| | ☑ Adolescents (12 years old to < 22 years old) | | |
| | ☑ Adults (22 years old and greater) | | |
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EVER
FORTUNE.AI
K263109
# 510(k) Summary
# 1. General Information
| 510(k) Sponsor | Ever Fortune.AI Co., Ltd. |
| --- | --- |
| Address | 8F., No.360, Sec. 1, Jingmao Rd., Beitun Dist., Taichung City 406040, Taiwan |
| Applicant | Joseph Chang |
| Contact Information | 886-04-23213838 #216 joseph.chang@everfortune.ai |
| Correspondence Person | Ti-Hao Wang |
| Contact Information | 886-04-23213838 #168 thothwang@gmail.com tihao.wang@everfortune.ai |
| Date Prepared | Sep. 04, 2026 |
# 2. Subject Device
| Proprietary Name | EFAI NEUROSUITE CTA LVO ASSESSMENT SYSTEM (LVO-CTA-100) |
| --- | --- |
| Common Name | EFAI LVO |
| Classification Name | Radiological computer-assisted triage and notification software |
| Regulation Number | 21 CFR 892.2080 |
| Product Code | QAS |
| Regulatory Class | II |
# 3. Predicate Device
| Proprietary Name | Rapid LVO |
| --- | --- |
| Premarket Notification | K221248 |
| Classification Name | Radiological computer-assisted triage and notification software |
| Regulation Number | 21 CFR 892.2080 |
| Product Code | QAS |
| Regulatory Class | II |
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#### 4. Reference Device
| **Proprietary Name** | syngo.CT LVO Detection |
| --- | --- |
| **Premarket Notification** | K243145 |
| **Classification Name** | Radiological computer-assisted triage and notification software |
| **Regulation Number** | 21 CFR 892.2080 |
| **Product Code** | QAS |
| **Regulatory Class** | II |
#### 5. Device Description
EFAI NEUROSUITE CTA LVO ASSESSMENT SYSTEM (EFAI LVO) is a radiological computer-assisted triage and notification software system. The software uses deep learning techniques to automatically analyze contrast-enhanced head computed tomography angiography (CTA) and alerts the PACS/RIS workstation in parallel to the ongoing standard of care image interpretation.
Within the scope of this device, Large Vessel Occlusion (LVO) refers to vascular obstruction (total occlusion or high-grade stenosis) within the anterior circulation, targeted across three designated segments: ICA, MCA-M1, and MCA-M2. Through the use of EFAI LVO, a trained clinician is able to review studies with features suggestive of LVO in these segments earlier than in standard of care workflow.
The device is intended to provide a passive notification through the PACS/workstation to the trained clinicians 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 contrast-enhanced head CTA. The device aims to aid in prioritization and triage of radiological medical images only.
#### 6. Intended Use / Indications for Use
EFAI NEUROSUITE CTA LVO ASSESSMENT SYSTEM (EFAI LVO) is a radiological computer aided triage and notification software indicated for use in the analysis of CTA head images. The device is intended to assist hospital networks and trained clinicians in workflow triage by flagging and communication of suspected positive ICA, MCA-M1, or MCA-M2 Large Vessel Occlusion (LVO) findings in head CTA images.
EFAI LVO Traditional 510(k)
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EFAI LVO uses an artificial intelligence algorithm to analyze images and highlight cases with suspected LVO in parallel to the ongoing standard of care image interpretation. The user is presented with notifications for cases with suspected LVO findings. The device does not alter the original medical image and is not intended to be used as a diagnostic device.
The results of EFAI LVO are intended to be used in conjunction with other patient information and based on professional judgment, to assist with triage/prioritization of medical images. Notified clinicians are responsible for viewing full images per the standard of care.
### 7. Comparison of Technological Characteristics with Predicate Device
EFAI LVO does not raise new questions of safety or effectiveness compared to the predicate device, Rapid LVO (K221248). Both devices are radiological computer-aided triage and notification software applications for determining suspicion of Large Vessel Occlusion (LVO) using head CTA images.
Compared to the predicate device, EFAI LVO identifies an additional type of occlusion, the MCA-M2 segment, which does not raise a substantial difference in the indications for use nor different questions of safety and effectiveness, as this capability is identical to the reference device, syngo.CT LVO Detection (K243145).
While the subject device utilizes artificial intelligence learning algorithms rather than the traditional programming algorithms used by the predicate, this technological approach is also consistent with the reference device. Identification of suspected findings for these devices is for informational triage purposes and not for diagnostic use beyond notification.
The following table summarizes and compares the EFAI LVO, the predicate device (K221248), and the reference device (K243145):
| Feature/Function | Subject Device: EFAI LVO | Predicate Device: Rapid LVO (K221248) | Reference Device: syngo.CT LVO Detection (K243145) |
| --- | --- | --- | --- |
| Intended Use/Indication for Use | EFAI NEUROSUITE CTA LVO ASSESSMENT SYSTEM (EFAI LVO) is a radiological computer aided triage and notification software indicated for use in the analysis of CTA head | Rapid LVO is a radiological computer aided triage and notification software indicated for use in the analysis of CTA head images. The device is intended to assist hospital networks and trained | syngo.CT LVO Detection is a radiological postprocessing application for the analysis of CT angiography (CTA) head images. syngo.CT LVO Detection supports |
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| | images. The device is intended to assist hospital networks and trained clinicians in workflow triage by flagging and communication of suspected positive ICA, MCA-M1, or MCA-M2 Large Vessel Occlusion (LVO) findings in head CTA images. EFAI LVO uses an artificial intelligence algorithm to analyze images and highlight cases with suspected LVO in parallel to the ongoing standard of care image interpretation. The user is presented with notifications for cases with suspected LVO findings. The device does not alter the original medical image and is not intended to be used as a diagnostic device. The results of EFAI LVO are intended to be used in conjunction with other patient information and based on professional judgment, to assist with triage/prioritization of medical images. Notified clinicians are responsible for viewing full images per the standard of care. | radiologists in workflow triage by flagging and communication of suspected positive ICA or MCA-M1 Large Vessel Occlusion (LVO) findings in head CTA images. Rapid LVO uses a software algorithm to analyze images and highlight cases with suspected LVO on a server or standalone desktop application in parallel to the ongoing standard of care image interpretation. The user is presented with notifications for cases with suspected LVO findings. Notifications include compressed preview images. These are meant for informational purposes only and are not intended for diagnostic use beyond notification. The device does not alter the original medical image and is not intended to be used as a diagnostic device. The results of Rapid LVO are intended to be used in conjunction with other patient information and based on professional judgment, to assist with triage /prioritization of medical images. Notified clinicians are responsible for viewing full images per the standard of care. | computer-aided triage, and it addresses vascular abortions in the CTA of the brain, commonly referred to as large vessel occlusion (LVO), in the ICA, M1, and M2 segment. It is intended for all patient populations of age ≥ 22 years, without any of the following contraindications: old infarcts or other diseases impacting the brain vasculature (for example, brain tumors), metal artifacts (for example, coils), surgical signs in the images. The output for triage is intended for informational purposes only. It is not intended for diagnostic use and does not alter the original medical image. |
| --- | --- | --- | --- |
| Stroke/Head | Stroke/Head | Intracranial Stroke/Head | Stroke/Head |
| Region of Interest | ICA, MCA-M1, and MCA-M2 | ICA and MCA-M1 | ICA, MCA-M1, and MCA-M2 |
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| **Computer Platform** | On-premises deployment | Standard off-the-shelf PC workstation/server; Virtual platform such as VMware | syngo.via platform |
| --- | --- | --- | --- |
| **Imaging Type** | CT Angiography | CT Angiography | CT Angiography |
| **Data Acquisition** | Acquires medical image data from DICOM compliant imaging devices and modalities | Acquires medical image data from DICOM compliant imaging devices and modalities | Acquires medical image data from DICOM compliant imaging devices and modalities |
| **SaMD** | Artificial Intelligence learning algorithms | Traditional Algorithms | Artificial Intelligence learning algorithms |
| **Pathways** | External notification device | PACS, email, mobile | External notification device |
| **Preview/ Prioritization** | No image preview provided / The device provides informational case-level output for worklist prioritization. | Notification Message of Suspected LVO. Presentation of a compressed preview of the study for initial assessment not meant for diagnostic purposes. The device operates in parallel with the standard of care, which remains. | No image preview provided; informational output only that does not highlight or direct attention to any portion of the image for worklist prioritization. |
| **SoC Workflow** | In parallel to the SoC | In parallel to the SoC | In parallel to the SoC |
| **Original Image** | No Alteration | No Alteration | No Alteration |
| **Primary Users** | Trained clinician | Clinician | Radiologists and clinical administrators |
## 8. Performance Data
Performance of the EFAI LVO has been evaluated and verified in accordance with software specifications and applicable performance standards through software verification and validation testing. Software validation activities were conducted in compliance with IEC 62304:2006/A1:2015 (Medical device software – Software life cycle processes), ISO 14971:2019 (Application of risk management to medical devices), and 21 CFR 820.30 design controls, alongside FDA guidance documents, “Content of Premarket Submissions for Device Software Functions” and “Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions.”
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Clinical validation was conducted by independent clinical users representative of the intended end-user population. The results demonstrated that EFAI LVO operates as intended within a clinical workflow and supports its intended use for worklist prioritization and triage of suspected Large Vessel Occlusions (LVO) in head CTA images.
Ever Fortune.AI conducted a retrospective, blinded, multisite standalone performance study to evaluate the subject device, EFAI LVO. The study included 313 consecutively collected head CTA cases from multiple U.S. clinical sites, with one case per patient, and no overlap from model development or internal testing. The reference standard was established via majority agreement among three U.S. board-certified radiologists, yielding 171 positive and 142 negative cases.
EFAI LVO exceeded the pre-specified 80% performance goal using the lower bound of the 95% confidence interval (CI) for sensitivity and specificity. The observed results were sensitivity: 0.96 (95% CI: 0.92–0.98) and specificity: 0.94 (95% CI: 0.88–0.97), with a PPV of 0.95, NPV of 0.95, and an AUROC of 0.96. Additionally, an analysis of total system processing time achieved a mean of 3.03 minutes (95% CI: 3.02–3.05), meeting the pre-specified goal of ≤ 3.5 minutes.
Moreover, subgroup analyses demonstrated consistent performance across demographic variables (sex, age, race and ethnicity), imaging acquisition parameters (CT manufacturer, CT slice thickness), and diverse concomitant imaging attributes, encompassing non-target vascular conditions, parenchymal pathologies, and technical quality artifacts.
In conclusion, the results demonstrate that EFAI LVO is substantially equivalent in safety and effectiveness to the predicate device.
## 9. Substantial Equivalence
The EFAI LVO is substantially equivalent to the predicate Rapid LVO (K221248) as both share an identical intended use for the triage and notification of suspected LVO findings in head CTA images. Technological differences, such as the use of artificial intelligence learning algorithms and the inclusion of the MCA-M2 segment, are directly supported by the reference device syngo.CT LVO (K243145) and do not raise new questions of safety or effectiveness.
Additionally, the subject device's passive notification design without image previews ensures that it remains an informational tool for worklist prioritization, operating strictly in parallel to the standard of care. Consequently, the EFAI LVO is as safe and effective as the legally marketed devices.
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## **10. Conclusion**
Based on the information submitted in this premarket notification, and based on the indications for use, technological characteristics, and performance testing, the EFAI LVO raises no new questions of safety and effectiveness and is substantially equivalent to the predicate device in terms of safety and effectiveness.
EFAI LVO Traditional 510(k)
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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 as a Medical Device, 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 as a Medical Device), 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.
