EFAI CARDIOSUITE CTA ACUTE AORTIC SYNDROME ASSESSMENT SYSTEM
Applicant
Ever Fortune.Ai, Co., Ltd.
Product Code
QAS · Radiology
Decision Date
Apr 8, 2024
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
K240291 · Apr 8, 2024
EFAI CARDIOSUITE CTA ACUTE AORTIC SYNDROME ASSESSMENT SYSTEM
Ever Fortune.Ai, Co., Ltd.
Retrospective clinical CTA studies
A retrospective, multisite clinical validation study was conducted using 380 consecutively collected clinical CTA studies to evaluate the device's performance in identifying aortic dissection or intramural hematoma.
Adults aged 22 and older; chest or chest-abdomen CTA scans; Sample Size: 380 CTA studies (156 positives and 224 negatives); Number of Sites: Multisite (specific number not provided)
Not applicable for this study
Sensitivity and specificity for identifying AD or IMH; system processing time
AI Performance
Output
Algorithm
Acceptance
Observed
Dev DS
Dev Readers
Test DS
Test Readers
Aortic dissection (AD) or aortic intramural hematoma (IMH) detection
Deep learning algorithm
Lower bounds of 95% confidence intervals of both sensitivity and specificity > 0.8
EFAI CARDIOSUITE CTA ACUTE AORTIC SYNDROME ASSESSMENT SYSTEM (EFAI AASCTA) is a radiological computer aided triage and notification software indicated for use in the analysis of chest or chest-abdomen CTA in adults aged 22 and older. The device is intended to assist hospital networks and appropriately trained medical specialists in workflow triage by flagging and communicating suspected positive cases of aortic dissection (AD) or aortic intramural hematoma (IMH) pathology. EFAI AASCTA uses an artificial intelligence algorithm to identify suspected findings. It makes case-level output available to a PACS/workstation for worklist prioritization or triage. EFAI AASCTA is not intended to direct attention to specific portions or anomalies of an image. Its results are not intended to be used on a stand-alone basis for clinical decision-making nor is it intended to rule out AAS or otherwise preclude clinical assessment of computed tomography angiography cases.
Device Story
EFAI AASCTA is a radiological computer-aided triage and notification software. It processes DICOM chest or chest-abdomen CTA images using a deep learning AI algorithm to detect features suggestive of aortic dissection (AD) or aortic intramural hematoma (IMH). The device operates in the background, providing case-level notifications to a PACS/workstation to prioritize radiologist worklists. It does not highlight specific image anomalies or direct attention to specific locations. Used in hospital settings by radiologists, the system aims to reduce time-to-review for urgent cases. It is a parallel workflow tool; clinicians remain responsible for full image interpretation per standard of care. The output serves only to assist in triage/prioritization and does not replace clinical assessment.
Clinical Evidence
Retrospective, blinded, multisite clinical validation study using 380 CTA studies (156 positive, 224 negative) collected in the U.S. Ground truth established by majority agreement of three board-certified radiologists. Primary endpoints: sensitivity and specificity. Results: sensitivity 0.929 (95% CI: 0.878-0.960), specificity 0.915 (95% CI: 0.871-0.945). Average processing time 37.86 seconds. Subgroup analysis confirmed performance consistency across age, gender, CT manufacturer, and image quality confounders.
Technological Characteristics
Software-based radiological triage system; utilizes deep learning AI algorithm. Inputs: DICOM chest/chest-abdomen CTA. Outputs: Case-level notification to PACS/workstation. Compliant with IEC 62304:2006/A1:2016 (software life cycle) and ISO 14971:2019 (risk management). Standalone software application.
Indications for Use
Indicated for adults aged 22+ undergoing chest or chest-abdomen CTA. Assists hospital networks and medical specialists in triage by flagging suspected aortic dissection (AD) or aortic intramural hematoma (IMH). Not for stand-alone diagnostic use or ruling out AAS.
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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April 8, 2024
Ever Fortune.AI, Co., Ltd. Ti-Hao Wang Chief Technology Officer Rm. D, 8F. No. 573, Sec. 2 Taiwan Blvd. West Dist. Taichung City, 403020, TAIWAN
Re: K240291
EFAI CARDIOSUITE CTA ACUTE AORTIC SYNDROME ASSESSMENT Trade/Device Name: SYSTEM Regulation Number: 21 CFR 892.2080 Regulation Name: Radiological Computer Aided Triage And Notification Software Regulatory Class: Class II Product Code: OAS Dated: January 31, 2024 Received: February 1, 2024
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/cdrb/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).
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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 mediation-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,
Samul for
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
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## Indications for Use
510(k) Number (if known) K240291
Device Name EFAI CARDIOSUITE CTA ACUTE AORTIC SYNDROME ASSESSMENT SYSTEM
## Indications for Use (Describe)
EFAI CARDIOSUITE CTA ACUTE AORTIC SYNDROME ASSESSMENT SYSTEM (EFAI AASCTA) is a radiological computer aided triage and notification software indicated for use in the analysis of chest-abdomen CTA in adults aged 22 and older. The device is intended to assist hospital networks and appropriately trained medical specialists in workflow triage by flagging and communicating suspected positive cases of aortic dissection (AD) or aortic intramural hematoma (IMH) pathology.
EFAI AASCTA uses an artificial intelligence algorithm to identify suspected findings. It makes case-level output available to a PACS/workstation for worklist prioritization or triage. EFAI AASCTA is not intended to direct attention to specific portions or anomalies of an image. Its results are not intended to be used on a stand-alone basis for clinical decisionmaking nor is it intended to rule out AAS or otherwise preclude clinical assessment of computed tomography cases.
Type of Use (Select one or both, as applicable)
| <span> X Prescription Use (Part 21 CFR 801 Subpart D) </span> |
|-----------------------------------------------------------------|
| <span> Over-The-Counter Use (21 CFR 801 Subpart C) </span> |
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# 510(k) Summary
#### General Information 1.
| 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 |
| Contact Information | 886-04-23213838 #168<br>thothwang@gmail.com<br>tihao.wang@everfortune.ai |
| Date Prepared | January, 2024 |
#### 2. Proposed Device
| Proprietary Name | EFAI CARDIOSUITE CTA ACUTE AORTIC SYNDROME<br>ASSESSMENT SYSTEM |
|---------------------|-----------------------------------------------------------------|
| Common Name | EFAI AASCTA |
| Classification Name | Radiological computer aided triage and notification software |
| Regulation Number | 21 CFR 892.2080 |
| Product Code | QAS |
| Regulatory Class | II |
#### 3. Predicate Device
| Proprietary Name | BriefCase |
|------------------------|--------------------------------------------------------------|
| Premarket Notification | K222329 |
| Classification Name | Radiological computer aided triage and notification software |
| Regulation Number | 21 CFR 892.2080 |
| Product Code | QAS |
| Regulatory Class | II |
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Image /page/4/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 with a network pattern on top of the head. To the right of the figure is the text "EVER" in a larger teal font, with "FORTUNE.AI" in a smaller teal font below it.
#### Device Description 4.
EFAI CARDIOSUITE CTA ACUTE AORTIC SYNDROME ASSESSMENT SYSTEM (EFAI AASCTA) is a radiological computer-assisted triage and notification software system. The software uses deep learning techniques to automatically analyze chest or chest-abdomen CTA and alerts the PACS/RIS workstation once images with features suggestive of AD or IMH are identified.
Through the use of EFAI AASCTA, a radiologist is able to review studies with features suggestive of AD or IMH 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 chest or chest-abdomen CTA. The device aims to aid in prioritization and triage of radiological medical images only.
#### ട. Intended Use / Indications for Use
EFAI CARDIOSUITE CTA ACUTE AORTIC SYNDROME ASSESSMENT SYSTEM (EFAI AASCTA) is a radiological computer aided triage and notification software indicated for use in the analysis of chest or chest-abdomen CTA in adults aged 22 and older. The device is intended to assist hospital networks and appropriately trained medical specialists in workflow triage by flagging and communicating suspected positive cases of aortic dissection (AD) or aortic intramural hematoma (IMH) pathology.
EFAI AASCTA uses an artificial intelligence algorithm to identify suspected findings. It makes case-level output available to a PACS/workstation for worklist prioritization or triage. EFAI AASCTA is not intended to direct attention to specific portions or anomalies of an image. Its results are not intended to be used on a stand-alone basis for clinical decision-making nor is it intended to rule out AAS or otherwise preclude clinical assessment of computed tomography angiography cases.
#### Comparison of Technological Characteristics with Predicate Device 6.
| Feature/<br>Function | Proposed Device:<br>EFAI AASCTA<br>(K240291) | Predicate Device:<br>BriefCase<br>(K222329) |
|--------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Intended<br>Use/Indication<br>for Use | EFAI CARDIOSUITE CTA<br>ACUTE AORTIC SYNDROME<br>ASSESSMENT SYSTEM (EFAI | BriefCase is a radiological<br>computer-aided triage and notification<br>software indicated for use in the analysis |
| | AASCTA) is a radiological<br>computer aided triage and<br>notification software indicated for<br>use in the analysis of chest or<br>chest-abdomen CTA in adults aged<br>22 and older. The device is<br>intended to assist hospital networks<br>and appropriately trained medical<br>specialists in workflow triage by<br>flagging and communicating<br>suspected positive cases of aortic<br>dissection (AD) or aortic intramural<br>hematoma (IMH) pathology.<br>EFAI AASCTA uses an artificial<br>intelligence algorithm to identify<br>suspected findings. It makes<br>case-level output available to a<br>PACS/workstation for worklist<br>prioritization or triage. EFAI<br>AASCTA is not intended to direct<br>attention to specific portions or<br>anomalies of an image. Its results<br>are not intended to be used on a<br>stand-alone basis for clinical<br>decision-making nor is it intended<br>to rule out AAS or otherwise<br>preclude clinical assessment of<br>computed tomography angiography<br>cases. | of CT exams with contrast (CTA and CT<br>with contrast) that include the chest in<br>adults or transitional adolescents aged 18<br>and older. The device is intended to assist<br>hospital networks and appropriately<br>trained medical specialists in workflow<br>triage by flagging and communicating<br>suspected positive cases of aortic<br>dissection (AD) pathology. BriefCase<br>uses an artificial intelligence algorithm to<br>analyze images and highlight cases with<br>detected findings on a standalone<br>application in parallel to the ongoing<br>standard of care image interpretation. The<br>user is presented with notifications for<br>cases with suspected findings.<br>Notifications include compressed<br>preview images that are meant for<br>informational purposes only and not<br>intended for diagnostic use beyond<br>notification. The device does not alter the<br>original medical image and is not<br>intended to be used as a diagnostic<br>device. The results of BriefCase are<br>intended to be used in conjunction with<br>other patient information and based on<br>the user's professional judgment, to assist<br>with triage/prioritization of medical<br>images. Notified clinicians are<br>responsible for viewing full images per<br>the standard of care. |
| User population | Hospital<br>networks<br>and<br>appropriately<br>trained<br>medical<br>specialists | Hospital networks and appropriately<br>trained medical specialists |
| Anatomical<br>region of interest | Chest and thoracoabdominal | Chest, abdomen and thoracoabdominal |
| Data acquisition<br>protocol | chest or chest-abdomen CTA | CT exams with contrast (CTA and CT<br>with contrast) that include the chest |
| Notification-only<br>(notification<br>alerts), parallel<br>workflow tool | Yes | Yes |
| Images<br>format | DICOM | DICOM |
| Interference with<br>standard<br>workflow | No | No |
| Algorithm | Artificial intelligence algorithm with database of images | Artificial intelligence algorithm with database of images |
EFAI AASCTA Traditional 510(k)
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#### 7. Performance Data
Performance of the EFAI AASCTA 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, "Content of Premarket Submissions for Device Software Functions" and "Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions."
Ever Fortune.AI conducted a retrospective, blinded, multisite clinical validation study with the proposed device EFAI AASCTA with a pre-determined primary and secondary endpoint and performance goals to evaluate the performance of the EFAI AASCTA in identifying positive findings of aortic dissection (AD) or aortic intramural hematoma (IMH) from chest or chest-abdomen CTA scans on a validation dataset of 380 CTA studies (156 positives and 224 negatives) consecutively collected in the United States. None of the studies was used as part of the EFAI AASCTA model development or analytical validation testing.
The study population contained 51.58% females and 48.42% males, and the mean age of cases was 62.90 years. The CT scanner manufacturers of images were acquired from Philips, Toshiba, Siemens. GE, and others. Confounding cases in the dataset include possible confounders as follows: Artifact. Limited Field, Atherosclerotic Disease, Aortic Aneurysm, Arterial Dissection, and Vessel Disease.
The presence of AD or IMH in each case was determined independently by three U.S. board-certified radiologists, and the reference standard (ground truth) was generated by the majority agreement between the three experts. The performance criteria were set such that the lower bounds of 95% confidence intervals (Cls) of both sensitivity and specificity should exceed 0.8.
The observed results of the standalone performance validation study demonstrated that EFAI AASCTA by itself, in the absence of any interaction with a clinician, can provide case-level notifications with features suggestive of positive findings (AD or IMH) with satisfactory results. The EFAI AASCTA was able to demonstrate sensitivity and specificity of 0.929 (95% CI=0.878-0.960) and 0.915 (95% CI=0.871-0.945) respectively, which is substantially equivalent to the predicate device (BriefCase, K222329). The secondary endpoint of the observed system processing time per study is 37.86 seconds (95% CI=35.22-40.50) on average and was comparable with the predicate device (38 seconds).
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Image /page/7/Picture/0 description: The image contains a logo for a company called "EVER FORTUNE.AI". The logo consists of a stylized figure with a circular head and a body that resembles a "T". The head is green and has a network-like pattern on it, while the body is teal. To the right of the figure, the company name is written in teal, with "EVER" on the top line and "FORTUNE.AI" on the bottom line.
In addition, the results of the subgroup analysis, which included different genders, age groups. CT manufacturer groups, and CT slice thickness groups, demonstrated that EFAI AASCTA consistently performed high performance, underscoring its reliability and effectiveness across diverse subgroups. We also evaluated the device's performance in cases with image quality issues (including Artifact and Limited Field) and accompanying radiologic findings (including Atherosclerotic Disease, Aortic Aneurysm, Arterial Dissection, and Vessel Disease) to assess the impact of these potential confounders. The device consistently performed reliably across these circumstances. Furthermore, our analysis of the device's ability to identify specific characteristics of positive findings, including their type, Stanford classification, and location, revealed that EFAI AASCTA maintains stable performance. In conclusion, the results demonstrate that the EFAI AASCTA device is determined to be substantially equivalent in safety and effectiveness to the predicate device. BriefCase.
#### 8. Safety & Effectiveness
EFAI AASCTA has been designed, verified and validated in compliance with 21 CFR, Part 820.30 requirements. The device has been designed to meet the requirements associated with ISO 14971:2019 Medical devices - Application of risk management to medical devices. The EFAI AASCTA performance has been validated using retrospective data from case data and through the use of Reader comparison analysis.
#### 9. Conclusion
Based on the information submitted in this premarket notification, and based on the indications for use, technological characteristics, and performance testing, the EFAI AASCTA raises no new questions of safety and effectiveness and is substantially equivalent to the predicate device in terms of safety, efficacy, and performance.
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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.
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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.
