AI4CMR v1.0
Device Facts
| Record ID | K220624 |
|---|---|
| Device Name | AI4CMR v1.0 |
| Applicant | Ai4medimaging Medical Solutions S.A. |
| Product Code | LLZ · Radiology |
| Decision Date | Jul 22, 2022 |
| Decision | SESE |
| Submission Type | Traditional |
| Regulation | 21 CFR 892.2050 |
| Device Class | Class 2 |
| Attributes | AI/ML, Software as a Medical Device |
Intended 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.
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).
Predicate Devices
- Imbio RV/LV Software (K203256)
Related Devices
- K252084 — AI4CMR v2.0 · Ai4medimaging Medical Solutions S.A. · Feb 11, 2026
- K242062 — 1CMR Pro · Mycardium AI Limited · Nov 15, 2024
- K241038 — Cardiac CT Function Software Application · Circle Cardiovascular Imaging · Jun 7, 2024
- K162513 — Arterys Software v2.0 · Arterys, Inc. · Oct 28, 2016
- K241922 — Myomics · Phantomics, Inc. · Feb 28, 2025
Submission Summary (Full Text)
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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
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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
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# 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> |
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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)
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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)
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| 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 |
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| 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
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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)
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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)
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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.
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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)
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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.
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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.
