AI/ML, Software as a Medical Device, Real-World Evidence
Real-World Evidence
Submission
Device
Sponsor
RWD Sources
RWE Use Summary
Key Tags
K220624 · Jul 22, 2022
AI4CMR v1.0
Ai4medimaging Medical Solutions S.A.
Retrospective clinical CMR cases from Hospital de Braga; Retrospective clinical CMR cases for multi-reader multi-center (MRMC) performance assessment
Retrospective clinical data was used for training, testing, and validating the AI algorithm's performance in segmenting and quantifying cardiac function metrics.
Retrospective clinical data; Model training; Clinical performance assessment; Cardiac MRI
Clinical Evidence
Study Design
Population
Comparator
Key Endpoints
Model Training Dataset; Retrospective cohort; Follow-up/Duration: 2015 to January 2019; Study Period: 2015-2019
Patients aged 13-89 (mean 58) undergoing CMR; 63% male, 37% female; Sample Size: 824 cases; Number of Sites: 1 (Hospital de Braga, Portugal)
Not applicable for this study
Model training, validation, and testing for cardiac segmentation
Clinical Performance Assessment; Retrospective multi-reader multi-center (MRMC) study
Patients aged 17-85 (average 51); 77% male; diseased (~60%) and non-diseased (~40%); Sample Size: 146 CMR cases; Number of Sites: Multi-center
2 expert readers (consensus)
Agreement between AI4CMR and expert readers for LV/RV volumes, mass, and ejection fraction
AI4CMR software is designed to report cardiac function measurements (ventricle volumes, ejection fraction, indices etc.) from 1.5T and 3T magnetic resonance (MR) scanners. AI4CMR uses artificial intelligence to automatically segment and quantify the different cardiac measurements. Its results are not intended to be used on a stand-alone basis for clinical decision-making. The user incorporating AI4CMR into their DICOM application of choice is responsible for implementing a user interface.
Device Story
AI4CMR v1.0 is a cloud-hosted software plug-in for third-party DICOM viewers; processes multi-phase, multi-slice cardiac MR images (1.5T/3T). Uses AI to automatically segment cardiac anatomy and quantify metrics: LV/RV stroke volume, cardiac output, ejection fraction, end-diastolic/systolic volumes, and LV myocardial mass. Output is a report provided to the clinician via the DICOM viewer interface. Not for stand-alone clinical decision-making; intended to assist clinicians in cardiac assessment. Benefits include automated, consistent quantification of cardiac function, reducing manual segmentation time.
Clinical Evidence
Retrospective multi-reader multi-center study (146 CMR cases, ages 17-85, 77% male). Evaluated agreement between AI4CMR and 2 expert readers. Results showed high agreement: LV EDV ICC 0.99, LV ESV ICC 0.99, LV EF ICC 0.96, RV EDV ICC 0.96, RV ESV ICC 0.95, RV EF ICC 0.81, LV Mass ICC 0.96. Bench testing on 15 SCMR consensus cases also performed (Dice similarity 0.72 for myocardium).
Technological Characteristics
Cloud-hosted software; DICOM-compliant; AI-based segmentation algorithm. Operates as a plug-in for third-party DICOM viewers. Processes 1.5T/3T cardiac MRI data. Software lifecycle follows ANSI AAMI IEC 62304.
Indications for Use
Indicated for patients undergoing cardiac MRI (1.5T or 3T) requiring quantification of cardiac function metrics, including ventricle volumes, ejection fraction, and myocardial mass. No specific contraindications listed.
Regulatory Classification
Identification
A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.
Special Controls
*Classification.* Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).
{0}------------------------------------------------
Image /page/0/Picture/0 description: The image shows the logo for the U.S. Food and Drug Administration (FDA). The logo consists of two parts: on the left, there is the Department of Health & Human Services logo, which features an abstract image of a human figure. To the right of this is the FDA logo, which has the letters "FDA" in a blue square, followed by the words "U.S. FOOD & DRUG ADMINISTRATION" in blue text.
AI4MedImaging Medical Solutions S.A. % Carla Almeida Regulatory Affairs and Quality Manager Rua do Parque Poente, Lote 35 Braga, Minho 4705-002 PORTUGAL
July 22, 2022
### Re: K220624
Trade/Device Name: AI4CMR v1.0 Regulation Number: 21 CFR 892.2050 Regulation Name: Medical image management and processing system Regulatory Class: Class II Product Code: QIH Dated: June 17, 2022 Received: June 23, 2022
Dear Carla Almeida:
We have reviewed your Section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database located at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.
If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.
Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801 and Part 809); medical device reporting of medical device-related adverse events) (21 CFR
{1}------------------------------------------------
803) for devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.
For comprehensive regulatory information about medical devices and radiation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely,
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
{2}------------------------------------------------
# Indications for Use
510(k) Number (if known) K220624
Device Name AI4CMR v1.0
Indications for Use (Describe)
AI4CMR software is designed to report cardiac function measurements (ventricle volumes, ejection fraction, indices etc.) from 1.5T and 3T magnetic resonance (MR) scanners. AI4CMR uses artificial intelligence to automatically segment and quantify the different cardiac measurements. Its results are not intended to be used on a stand-alone basis for clinical decision-making.
The user incorporating AI4CMR into their DICOM application of choice is responsible for implementing a user interface.
| Type of Use (Select one or both, as applicable) | |
|--------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------|
| <div> <span> <span style="text-decoration: overline;">X</span> Prescription Use (Part 21 CFR 801 Subpart D) </span> </div> | <div> <span> <span style="text-decoration: overline;">☐</span> Over-The-Counter Use (21 CFR 801 Subpart C) </span> </div> |
### 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/1 description: The image shows the logo for AI4MedImaging. The logo consists of a circular graphic on the left and the text "AI4MedImaging" on the right. The circular graphic is made up of a series of blue lines that radiate out from the center, creating a spiral effect. The text is also blue and is written in a sans-serif font.
# Section 5. 510(k) Summary
#### 1. General Information
| 510(k) Sponsor | AI4MedImaging Medical Solutions S.A. |
|-----------------------|------------------------------------------------------------------|
| Address | Rua do Parque Poente, It 32<br>4705-002 Sequeira, Braga Portugal |
| Correspondence Person | Rory A. Carrillo<br>Quality and Regulatory Consultant<br>Cosm |
| Contact Information | Email: rory@cosmhq.com<br>Phone: 562-533-7010 |
| Date Prepared | June 17, 2022 |
#### 2. Subject Device
| Proprietary Name | AI4CMR v1.0 |
|---------------------|------------------------------------------------|
| Common Name | AI4CMR |
| Classification Name | System, Image Processing, Radiological |
| Regulation Number | 21 CFR 892.2050 |
| Regulation Name | Medical Image Management and Processing System |
| Product Code | OIH |
| Regulatory Class | II |
#### 3. Predicate Device
| Proprietary Name | Imbio RV/LV Software |
|------------------------|------------------------------------------------|
| Premarket Notification | K203256 |
| Classification Name | System, Image Processing, Radiological |
| Regulation Number | 21 CFR 892.2050 |
| Regulation Name | Medical Image Management and Processing System |
| Product Code | QIH |
| Regulatory Class | II |
#### Device Description 4.
AI4CMR v1.0 is a cloud-hosted service used with any third-party DICOM viewer application where the DICOM viewer serves as the user interface and the interface to a PACS or scanner for AI4CMR. AI4CMR is implemented as a plug-in to the DICOM viewer by the user and automatically processes and analyses cardiac MR images received by the DICOM viewer to quantify relevant cardiac function metrics and makes the information available to the user at the user's discretion.
The following are the cardiac function metrics quantified and reported by the software:
AI4CMR v1.0 Traditional 510(k)
{4}------------------------------------------------
Image /page/4/Picture/0 description: The image shows the logo for AI4MedImaging. The logo consists of a circular graphic on the left and the text "AI4MedImaging" on the right. The circular graphic is made up of many small blue lines that form a spiral shape. The text "AI4MedImaging" is also in blue and is written in a sans-serif font.
### Quantitative Analysis
The subject device performs the following anatomical measurements:
- Anatomy and tissue segmentation ●
- LV/RV stroke volume 0
- LV/RV cardiac output
- LV/RV ejection fraction ●
- LV/RV end-diastolic volume
- LV/RV end-systolic volume
### Reporting
The subject device enables the following metrics to be reported as desired by the user:
| Metric | Unit | Accuracy |
|-----------------------------------|--------|----------------------------------------------------------------------------------------------|
| LV/RV stroke volume | ml | n/a1 |
| LV/RV cardiac output | L/min | n/a1 |
| LV/RV ejection fraction (EF) | % | LV bias (std): 3.87 (5.74)<br>LV ICC: 0.96<br>RV bias (std): 7.80 (7.32)<br>RV ICC: 0.81 |
| LV/RV end-diastolic volume (EDV) | ml | LV bias (std): 8.66 (17.28)<br>LV ICC: 0.99<br>RV bias (std): 8.36 (15.59)<br>RV ICC: 0.96 |
| LV/RV end-systolic volume (ESV) | ml | LV bias (std): -2.90 (17.52)<br>LV ICC: 0.99<br>RV bias (std): -9.08 (13.83)<br>RV ICC: 0.95 |
| LV myocardial mass | g | bias (std): 2.45 (18.04)<br>ICC: 0.96 |
| LV/RV end-systolic volume index2 | ml/m^2 | n/a1 |
| LV/RV end-diastolic volume index2 | ml/m^2 | n/a1 |
| LV/RV stroke volume index2 | ml/m^2 | n/a1 |
| Myocardium mass index2 | g/m^2 | n/a1 |
AI4CMR v1.0 Traditional 510(k)
{5}------------------------------------------------
Image /page/5/Picture/0 description: The image contains the logo for AI4MedImaging. The logo consists of a circular design on the left, resembling a stylized spiral or a series of concentric circles with varying shades of blue. To the right of the circular design, the text "AI4MedImaging" is written in a sans-serif font, with each letter in a light blue color. The text is aligned horizontally and appears to be the primary identifier for the organization or product.
| Metric | Unit | Accuracy |
|-------------------|-------------|----------|
| Cardiac index $2$ | L/(min m^2) | n/a $1$ |
Notes:
1 These values are derived by performing simple mathematical operations and are derived from EDV, ESV, EF, and Mass metrics.
2 These values are only provided if the patient's height and weight are included in the DICOM data.
### Training Dataset
The AI4CMR training was performed on a dataset of 824 anonvmized cases collected retrospectively from Hospital de Braga, Portugal. Acquisition occurred between 2015 to January 2019 and consisted of male (63%) and female (37%) patients ranging in age from 13 to 89 (mean of 58) years old from Siemens acquisition system. This dataset is independent from the clinical validation set. This dataset was split into the 3 sets (training, validation, test). The splitting ratio is 70% for the "training set", 15% for the "validation set" and 15% for the "test set", resulting in 577, 121 and 126 cases each, respectively.
#### 5. Indications for Use
AI4CMR software is designed to report cardiac function measurements (ventricle volumes, ejection fraction, indices etc.) from 1.5T and 3T magnetic resonance (MR) scanners. AI4CMR uses artificial intelligence to automatically segment and quantify the different cardiac measurements. Its results are not intended to be used on a stand-alone basis for clinical decision-making.
The user incorporating AI4CMR into their DICOM application of choice is responsible for implementing a user interface.
#### Substantial Equivalence & Technical Characteristics 6.
| | Subject Device<br>AI4CMR v1.0 | Predicate Device:<br>Imbio RV/LV (K203256) |
|--------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Intended Use | AI4CMR software is designed to<br>report cardiac function<br>measurements (ventricle volumes,<br>ejection fraction, indices etc.) from<br>1.5T and 3T magnetic resonance<br>(MR) scanners. AI4CMR uses<br>artificial intelligence to<br>automatically segment and<br>quantify the different cardiac<br>measurements. Its results are not<br>intended to be used on a | The Imbio RV/LV Software device<br>is designed to measure the<br>maximal diameters of the right and<br>left ventricles of the heart from a<br>volumetric CTPA acquisition and<br>report the ratio of those<br>measurements. RV/LV analyzes<br>cases using an artificial<br>intelligence algorithm to identify<br>the location and measurements of<br>the ventricles. The RV/LV software |
{6}------------------------------------------------
Image /page/6/Picture/0 description: The image shows the logo for AI4MedImaging. The logo consists of a circular graphic on the left and the text "AI4MedImaging" on the right. The circular graphic is made up of several blue lines that radiate out from the center. The text is also blue and is in a sans-serif font.
| Subject Device<br>AI4CMR v1.0 | Predicate Device:<br>Imbio RV/LV (K203256) |
|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| stand-alone basis for clinical<br>decision-making.<br>The user incorporating AI4CMR<br>into their DICOM application of<br>choice is responsible for<br>implementing a user interface. | provides the user with annotated<br>images showing ventricular<br>measurements. Its results are not<br>intended to be used on a<br>stand-alone basis for clinical<br>decision-making or otherwise<br>preclude clinical assessment of<br>CTPA cases. |
| Feature/<br>Function | Subject Device:<br>AI4CMR v1.0 | Predicate Device:<br>Imbio RV/LV<br>(K203256) | Substantially<br>Equivalent? |
|---------------------------------|-------------------------------------------------------------------------------------|-----------------------------------------------|------------------------------|
| Indication for Use | See table above | See table above | Yes |
| Input Data<br>Requirements | Cardiovascular images:<br>multi-phase,<br>multi-slice acquired from<br>MRI scanners | Non-gated, CT Pulmonary<br>Angiography images | Yes¹ |
| DICOM Compliant | Yes | Yes | Yes |
| LV Segmentation | Yes | Yes | Yes |
| RV Segmentation | Yes | Yes | Yes |
| Diameter Measurements | Yes | Yes | Yes |
| Fully Automated<br>Segmentation | Yes | Yes | Yes |
| Interface | 3rd party Viewer as a<br>plug-in | Command line | Yes |
| Outputs | Report only | Report, DICOM<br>Secondary Capture Series | Yes |
See discussion below
The subject device and predicate device have similar indications for use and technological characteristics. Differences with the input data requirement do not raise questions of safety or effectiveness as the underlying technology is similar with similar risks that are mitigated by the same general and special controls.
#### 7. Performance Data
Safety and performance of the AI4CMR v1.0 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
{7}------------------------------------------------
Image /page/7/Picture/0 description: The image contains the logo for AI4MedImaging. The logo consists of a circular graphic to the left of the company name. The graphic is made up of several blue lines that form a spiral shape. The company name, "AI4MedImaging", is written in blue, sans-serif font to the right of the graphic.
with ANSI AAMI IEC 62304:2006/41:2016 - Medical device software - Software life cycle processes, in addition to the FDA Guidance documents, "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices" and "Content of Premarket Submission for Management of Cybersecurity in Medical Devices."
### 7.1 Bench Testing
AI4MED performed a standalone performance test on the Society of Cardiac Magnetic Resonance (SCMR) Consensus Contour Data' which consists of a total of 15 CMR cases annotated by seven (7) independent expert readers from various core laboratories. The dataset consisted of male and female patients with an age range of 42 to 77 (average of 61) years old across various 1.5T and 3T scanners (Siemens, GE, Phillips). Readers performed myocardial segmentation and quantified End-diastolic volume (EDV), End-systolic volume (ESV), LV mass (LVM), and Ejection Fraction (EF). Agreement was evaluated and achieved between AI4CMR and the SCMR Consensus data. For myocardium segmentation, an average dice similarity coefficient (DSC) of 0.72 per image was achieved. For LVM, EDV, ESV, and EF the intraclass correlation coefficient (ICC) was evaluated and the following performance was obtained:
| LV parameter | LOA (± 2 SD) | Bias ± SD | r 2 | ICC |
|--------------|------------------------|----------------------|------|------|
| EDV | [-58.5912, 45.9097] ml | -6.3407 ± 26.1252 ml | 0.85 | 0.95 |
| ESV | [-30.2173, 22.4768] ml | -3.8703 ± 13.1735 ml | 0.97 | 0.99 |
| EF | [-7.2668, 6.9567] % | -0.155 ± 3.5559 % | 0.95 | 0.99 |
| LVM | [-57.2331, 11.7108] g | -22.7611 ± 17.236 g | 0.72 | 0.78 |
### 7.2 Clinical Performance Assessment
AI4MED performed a multi-reader multi-center (MRMC) retrospective study consisting of 146 CMR cases with patients ranging from 17 to 85 years old (average of 51) that were predominantly male (77%) - consistent with cardiovascular disease incidence". Patient data consisted of diseased (~60%) and non-diseased (~40%) where the diseased was spread across the prevalent cardiovascular diseases worldwide'. CMR cases was acquired and balanced across Siemens, GE, and Philips 1.5T scanners with slice thickness of 8mm and slice diameter ranging from 8 to 10.5mm.
The primary objective was to evaluate agreement between the AI4MED device and 2 expert readers who achieved excellent interrater variability (ICC > 0.75). Readers manually segmented the myocardium for each CMR case per standard of care and manually determined volumes, LV mass, and Ejection Fraction. The dataset was independent of the data used for model training and development.
https://www.cardiacatlas.org/studies/scmr-consensus-data.
<sup>1</sup> SCMR. Cardiac Atlas Project - SCMR Consensus Contour Data [Internet]. Available from:
<sup>2</sup> Walli-Attaei, Marjan, et al. "Variations between women in risk factors, treatments, cardiovascular disease incidence, and death in 27 high-income, middle-income, and low-income countries (PURE); a prospective cohort study: " The Lancet 396.10244 (2020): 97-109 3 Ischemic heart disease, cardiomyopathis, pericardial abnormality, valve disease, cardiac mass tumor and others
AI4CMR v1.0 Traditional 510(k)
{8}------------------------------------------------
Image /page/8/Picture/0 description: The image contains the logo for AI4MedImaging. The logo consists of a circular graphic on the left and the text "AI4MedImaging" on the right. The circular graphic is made up of several concentric circles, each of which is composed of a series of small, blue lines. The text "AI4MedImaging" is written in a sans-serif font and is also blue.
A summary of the agreement for Left and Right Ventricular EDV, Left and Right Ventricular ESV, Left and Right Ventricular Ejection Fraction, LV Myocardial Mass is provided below:
### Left Ventricular EDV
| | Cronbach's<br>Alpha | Correlation<br>Coef. (ρ) | Bias | ICC | 95% Confidence Interval | |
|---------------------|---------------------|--------------------------|-------|-------|-------------------------|-------------|
| | | | | | Lower Bound | Upper Bound |
| AI4CMR vs consensus | 0.992 | 0.980 | 8.663 | 0.990 | 0.98 | 0.99 |
Image /page/8/Figure/4 description: The image contains two scatter plots, labeled A and B. Plot A shows the difference between AI4CMR and Readers' Consensus on the y-axis, and the mean of AI4CMR and Readers' Consensus on the x-axis. The plot includes a mean line at 8.66, and lines for +1.96 SD at 42.65 and -1.96 SD at -25.33. Plot B shows AI4CMR on the y-axis and Readers' Consensus on the x-axis, with a regression line described by the equation Y = 14.49 + 0.90 * X and an R-squared value of 0.98.
[see next page]
AI4CMR v1.0 Traditional 510(k)
{9}------------------------------------------------
Image /page/9/Picture/0 description: The image shows the logo for AI4MedImaging. The logo consists of a circular design on the left and the text "AI4MedImaging" on the right. The circular design is made up of several blue lines that form a spiral shape. The text is also in blue and is written in a clear, sans-serif font.
# Left Ventricular ESV
Image /page/9/Figure/2 description: The image contains two scatter plots, labeled A and B, and a table of statistical values. Plot A shows the difference between AI4CMR and Readers' Consensus against the mean of AI4CMR and Readers' Consensus, with a mean difference of -2.89 and limits of agreement at +1.96 SD (31.56) and -1.96 SD (-37.35). Plot B shows AI4CMR values plotted against Readers' Consensus values, with a regression line equation of Y = 14.38 + 0.91 * X and an R^2 value of 0.97. The table presents statistical measures such as Cronbach's Alpha (0.992), Correlation Coefficient (0.975), Bias (-2.893), ICC (0.991), and a 95% Confidence Interval with a lower and upper bound of 0.99.
# Left Ventricular Ejection Fraction
| | Cronbach's<br>Alpha | Correlation<br>Coef. (ρ) | Bias | ICC | 95% Confidence Interval | |
|---------------------|---------------------|--------------------------|-------|-------|-------------------------|-------------|
| | | | | | Lower Bound | Upper Bound |
| AI4CMR vs consensus | 0,969 | 0,909 | 3,867 | 0,956 | 0,87 | 0,98 |
Image /page/9/Figure/5 description: The image contains two scatter plots, labeled A and B. Plot A shows the difference between AI4CMR and Readers' Consensus on the y-axis, plotted against the mean of AI4CMR and Readers' Consensus on the x-axis. The plot includes a mean line at 3.87 and lines indicating +1.96 SD at 15.15 and -1.96 SD at -7.42. Plot B shows AI4CMR on the y-axis plotted against Readers' Consensus on the x-axis, along with a regression line described by the equation Y = 1.36 + 0.89 * X, with an R^2 value of 0.89.
{10}------------------------------------------------
Image /page/10/Picture/0 description: The image contains the logo for AI4MedImaging. The logo consists of a circular graphic on the left and the text "AI4MedImaging" on the right. The circular graphic is made up of several blue lines arranged in a circular pattern. The text "AI4MedImaging" is written in a blue sans-serif font.
### Left Ventricular Myocardial Mass
Image /page/10/Figure/2 description: This image shows a table comparing AI4CMR vs consensus. The table includes Cronbach's Alpha, Correlation Coef. (p), Bias, ICC, and 95% Confidence Interval. The Cronbach's Alpha is 0.956, the Correlation Coef. (p) is 0.936, the Bias is 2.452, the ICC is 0.955, and the 95% Confidence Interval is 0.94 to 0.97.
Image /page/10/Figure/3 description: The image contains two scatter plots, labeled A and B. Plot A shows the difference between AI4CMR and Readers' Consensus on the y-axis, and the mean of AI4CMR and Readers' Consensus on the x-axis. The plot includes a mean line at 2.45, and lines indicating +1.96 SD at 37.94 and -1.96 SD at -33.03. Plot B shows AI4CMR on the y-axis and Readers' Consensus on the x-axis, with a regression line and the equation Y = 22.18 + 0.82 * X, and R^2 = 0.85.
### Right Ventricular EDV
| | Cronbach's<br>Alpha | Correlation<br>Coef. (ρ) | Bias | ICC | 95% Confidence Interval | |
|---------------------|---------------------|--------------------------|-------|-------|-------------------------|-------------|
| | | | | | Lower Bound | Upper Bound |
| AI4CMR vs consensus | 0,972 | 0,924 | 8,355 | 0,964 | 0,92 | 0,98 |
Image /page/10/Figure/6 description: The image contains two scatter plots, labeled A and B. Plot A shows the difference between AI4CMR and Readers' Consensus on the y-axis, and the mean of AI4CMR and Readers' Consensus on the x-axis. The plot includes a mean line at 8.35, and lines indicating +1.96 SD at 39.02 and -1.96 SD at -22.31. Plot B shows AI4CMR on the y-axis and Readers' Consensus on the x-axis, with a regression line and the equation Y = 10.70 + 0.88 * X, with R^2 = 0.90.
AI4CMR v1.0 Traditional 510(k)
{11}------------------------------------------------
Image /page/11/Picture/0 description: The image shows the logo for AI4MedImaging. The logo consists of a circular graphic on the left and the text "AI4MedImaging" on the right. The circular graphic is made up of several blue lines that form a spiral shape. The text "AI4MedImaging" is also blue and is written in a sans-serif font.
# Right Ventricular ESV
| | Cronbach's<br>Alpha | Correlation<br>Coef. (p) | Bias | ICC | 95% Confidence Interval | |
|---------------------|---------------------|--------------------------|--------|-------|-------------------------|-------------|
| | | | | | Lower Bound | Upper Bound |
| AI4CMR vs consensus | 0,967 | 0,888 | -9,083 | 0,953 | 0,87 | 0,98 |
Image /page/11/Figure/3 description: The image contains two scatter plots, labeled A and B. Plot A shows the difference between AI4CMR and Readers' Consensus on the y-axis, and the mean of AI4CMR and Readers' Consensus on the x-axis. The plot includes a mean line at -9.08, and lines for +1.96 SD at 18.12 and -1.96 SD at -36.28. Plot B shows AI4CMR on the y-axis and Readers' Consensus on the x-axis, with a regression line and the equation Y = 11.47 + 0.96 * X and R^2 = 0.88.
# Right Ventricular Ejection Fraction
| | Cronbach's<br>Alpha | Correlation<br>Coef. (ρ) | Bias | ICC | 95% Confidence Interval | |
|---------------------|---------------------|--------------------------|-------|-------|-------------------------|-------------|
| | | | | | Lower Bound | Upper Bound |
| AI4CMR vs consensus | 0,902 | 0,712 | 7,802 | 0,814 | 0,15 | 0,93 |
Image /page/11/Figure/6 description: The image contains two scatter plots, labeled A and B. Plot A shows the difference between AI4CMR and Readers' Consensus on the y-axis, and the mean of AI4CMR and Readers' Consensus on the x-axis. The plot includes a mean line at 7.80, and lines indicating +1.96 SD at 22.20 and -1.96 SD at -6.60. Plot B shows AI4CMR on the y-axis and Readers' Consensus on the x-axis, with a regression line described by the equation Y = 0.93 + 0.86 * X and an R-squared value of 0.68.
{12}------------------------------------------------
Image /page/12/Picture/0 description: The image contains the logo for AI4MedImaging. The logo consists of a circular graphic on the left and the text "AI4MedImaging" on the right. The circular graphic is made up of several blue lines arranged in a circular pattern. The text "AI4MedImaging" is written in a blue sans-serif font.
#### 8. Conclusion
Based on the information submitted in this premarket notification, and based on the indications for use, technological characteristics and performance testing, the AI4CMR v1.0 raises no new questions of safety and effectiveness and is substantially equivalent to the predicate device in terms of safety and effectiveness.
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.