Strain AI (SAI001)
Device Facts
| Record ID | K242359 |
|---|---|
| Device Name | Strain AI (SAI001) |
| Applicant | Exo, Inc. |
| Product Code | QIH · Radiology |
| Decision Date | Nov 20, 2024 |
| Decision | SESE |
| Submission Type | Traditional |
| Regulation | 21 CFR 892.2050 |
| 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 |
|---|---|---|---|---|---|
| K242359 · Nov 20, 2024 | Strain AI (SAI001) | Exo, Inc. | Routine clinical practice cardiac ultrasound images | The device performance was evaluated using images acquired during routine clinical practice across multiple sites to assess the accuracy and reliability of the Strain AI global longitudinal strain (GLS) measurements. | Routine clinical practice; Cardiac ultrasound; GLS measurement; Performance validation |
Clinical Evidence
| Study Design | Population | Comparator | Key Endpoints |
|---|---|---|---|
| Retrospective performance evaluation using routine clinical images | Adult patients (age 21-96) with diverse demographic variables (gender, ethnicity, BMI); Number of Sites: Multiple clinical sites | Reference device (Us2.v2) | Intraclass correlation coefficient (ICC) and root mean square difference (RMSD) for Global Longitudinal Strain (GLS) |
AI Performance
| Output | Algorithm | Acceptance | Observed | Dev DS | Dev Readers | Test DS | Test Readers |
|---|---|---|---|---|---|---|---|
| Global Longitudinal Strain (GLS) | Deep Convolutional Neural Networks | — | ICC: 0.95 (95% CI: 0.91 - 0.97); RMSD: 2.76 (95% CI: 2.44 - 3.17) | — | — | Clinical performance evaluation on images acquired from cart-based and portable ultrasound devices (1.2 to 4 MHz) from multiple clinical sites. | — |
Indications for Use
Strain AI is intended for noninvasive processing of cardiac ultrasound images to provide measurements of global longitudinal strain of adult patients with suspected disease.
Device Story
Strain AI is a software-as-a-medical-device (SaMD) module integrated into third-party ultrasound imaging systems. It processes multi-frame apical 4-chamber (A4C) cardiac ultrasound images acquired during routine clinical practice. Using deep convolutional neural networks, the software performs automated segmentation and landmark detection to calculate global longitudinal strain (GLS). The device functions as a post-processing tool; clinicians review the automated measurements and have the option to accept or reject them. It is intended to aid in diagnostic analysis, not for sole patient management decisions. By providing quantitative strain measurements, the device assists clinicians in assessing cardiac function, potentially benefiting patients through more efficient and standardized diagnostic workflows.
Clinical Evidence
Performance evaluated using independent test data from multiple clinical sites, including diverse demographics (ages 21-96). Data acquired from various cart-based and portable ultrasound devices (1.2-4 MHz). Ground truth established using reference device (Us2.v2). Primary endpoints were Intraclass Correlation Coefficient (ICC) and Root Mean Square Difference (RMSD). Results for GLS: ICC 0.95 (95% CI: 0.91-0.97); RMSD 2.76 (95% CI: 2.44-3.17). Performance was consistent across subgroups (gender, age, BMI, image source).
Technological Characteristics
Software-only package operating on off-the-shelf hardware. Utilizes deep convolutional neural networks for segmentation and landmark detection. Non-adaptive machine learning algorithm. Processes multi-frame ultrasound images. Complies with IEC 62304:2006/AC:2015 software life cycle processes.
Indications for Use
Indicated for noninvasive processing of cardiac ultrasound images to provide global longitudinal strain measurements in adult patients with suspected disease.
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
- LVivo Software Application (K210053)
Reference Devices
- Us2.v2 (K233676)
Submission Summary (Full Text)
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November 20, 2024
Image /page/0/Picture/1 description: The image shows the logo for the U.S. Food and Drug Administration (FDA). The logo consists of two parts: the Department of Health & Human Services logo on the left and the FDA logo on the right. The FDA logo includes the letters "FDA" in a blue square, followed by the words "U.S. FOOD & DRUG ADMINISTRATION" in blue text.
Exo Inc % Jacqueline Murray Senior Regulatory Affairs Specialist 4201 Burton Drive Santa Clara, California 95054
Re: K242359
Trade/Device Name: Strain AI (SAI001) Regulation Number: 21 CFR 892.2050 Regulation Name: Medical image management and processing system Regulatory Class: Class II Product Code: QIH Dated: October 22, 2024 Received: October 22, 2024
Dear Jacqueline Murray:
We have reviewed your section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (the Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database available at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.
If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.
Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device"
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(https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).
Your device is also subject to, among other requirements, the Quality 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.
All medical devices, including Class I and unclassified devices and combination product device constituent parts are required to be in compliance with the final Unique Device Identification System rule ("UDI Rule"). The UDI Rule requires, among other things, that a device bear a unique device identifier (UDI) on its label and package (21 CFR 801.20(a)) unless an exception or alternative applies (21 CFR 801.20(b)) and that the dates on the device label be formatted in accordance with 21 CFR 801.18. The UDI Rule (21 CFR 830.300(a) and 830.320(b)) also requires that certain information be submitted to the Global Unique Device Identification Database (GUDID) (21 CFR Part 830 Subpart E). For additional information on these requirements, please see the UDI System webpage at https://www.fda.gov/medical-device-advicecomprehensive-regulatory-assistance/unique-device-identification-system-udi-system.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-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
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(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 Risk 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
Enclosure
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## Indications for Use
510(k) Number (if known) K242359
Device Name Strain AI (SAI001)
Indications for Use (Describe)
Strain AI is intended for noninvasive processing of cardiac ultrasound images to provide measurements of global longitudinal strain of adult patients with suspected disease.
| Type of Use (Select one or both, as applicable) |
|-------------------------------------------------|
|-------------------------------------------------|
X Prescription Use (Part 21 CFR 801 Subpart D)
| Over-The-Counter Use (21 CFR 801 Subpart C)
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Image /page/4/Picture/0 description: The image shows the logo for EXO. On the left side of the logo, there is a cluster of blue circles that are arranged in a triangular shape. To the right of the circles, the word "EXO" is written in a dark gray sans-serif font. The letters are bold and spaced closely together.
# K242359
### 510(k) Summary
General Information
| 510(k) Sponsor | Exo Inc. |
|-----------------------|--------------------------------------------|
| Address | 4201 Burton Drive<br>Santa Clara, CA 95054 |
| Correspondence Person | Jacqueline Murray |
| Contact Information | jmurray@exo.inc<br>Cell: +1 236-838-5056 |
| Date Prepared | August 8, 2024 |
Proposed Device
| Proprietary Name | Strain AI (SAI001) |
|---------------------|--------------------------------------------------|
| Common Name | Strain AI |
| Classification Name | Automated Radiological Image Processing Software |
| Regulation Number | 21 CFR 892.2050 |
| Product Code | QIH |
| Regulatory Class | II |
Predicate Device
| Proprietary Name | LVivo Software Application |
|------------------------|--------------------------------------------------|
| Premarket Notification | K210053 |
| Classification Name | Automated Radiological Image Processing Software |
| Regulation Number | 21 CFR 892.2050 |
| Product Code | QIH |
| Regulatory Class | II |
Reference Device
| Proprietary Name | Us2.v2 |
|------------------------|--------------------------------------------------|
| Premarket Notification | K233676 |
| Classification Name | Automated Radiological Image Processing Software |
| Regulation Number | 21 CFR 892.2050 |
| Product Code | QIH |
| Regulatory Class | II |
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Image /page/5/Picture/1 description: The image shows the logo for EXO. The logo consists of two parts: a blue gradient dot pattern on the left and the word "EXO" in dark gray on the right. The dot pattern is made up of 12 dots arranged in a circular shape. The word "EXO" is written in a sans-serif font.
#### Device Description
Exo's Strain Al is a software as a medical device (SaMD), intended as an aid in diagnostic analysis of echocardiography data. It specifically measures the global longitudinal strain (GLS) from apical 4-chamber (A4C) cardiac ultrasound images.
This software is developed as a module to be integrated by another computer programmer into their legally marketed ultrasound imaging device.
The software does not have a built-in viewer; instead, it integrates into a third-party ultrasound imaging device. The software functions as a post-processing tool, analyzing images after they are acquired. End-users have the option to accept or reject the provided measurements.
Strain Al takes as input image data and provides as an output a quantitative measurement of the global longitudinal strain (GLS) from apical 4-chamber (A4C) cardiac ultrasound images. It is important to note that patient management decisions should not be made solely on the results of the Strain AI analysis.
#### Indications for Use
Strain Al is intended for noninvasive processing of cardiac ultrasound images to provide measurements of global longitudinal strain of adult patients with suspected disease.
#### Comparison of Technological Characteristics with the Predicate Device
| Feature/<br>Function | Subject Device<br>Strain AI | Predicate Device<br>LVivo Software Application<br>(K210053) | Reference Device<br>Us2.v2 (K233676) |
|---------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------|--------------------------------------|
| Physical<br>Characteristic | Software package that<br>operates utilizing<br>off-the-shelf hardware | Same as subject device | Same as subject device |
| Scan type | Multi-frame ultrasound<br>images | Same as subject device | Same as subject device |
| Principle of<br>Operation and<br>Technology | Ultrasound image<br>processing software<br>implementing artificial<br>intelligence including<br>non-adaptive machine<br>learning algorithms trained<br>with clinical data intended<br>for non-invasive analysis of<br>ultrasound data | Same as subject device | Same as subject device |
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Image /page/6/Picture/1 description: The image shows the logo for EXO. On the left side of the logo is a collection of blue and light blue dots arranged in a circular pattern. To the right of the dots is the word "EXO" in a dark gray sans-serif font. The letters are spaced closely together.
| Feature/<br>Function | Subject Device<br>Strain AI | Predicate Device<br>LVivo Software Application<br>(K210053) | Reference Device<br>Us2.v2 (K233676) |
|----------------------|------------------------------------------------------------------------------------|-------------------------------------------------------------|--------------------------------------|
| AI Algorithm | Deep Convolutional Neural<br>Networks for<br>Segmentation or<br>Landmark Detection | Same as Subject<br>Device | Same as subject device |
| Anatomical Sites | Heart | Heart, Bladder | Same as subject device |
| GLS calculation | Yes | Same as Subject<br>Device | Same as Subject Device |
#### Performance Data
Safety and performance of the Strain Al 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 /EC 62304:2006/AC:2015 - Medical device software - Software life cycle processes, FDA's 'Content of Premarket Submissions for Device Software Functions'' Guidance for Industry and Food and Drug Administration Staff Document issued on June 14, 2023 and FDA Guidance (June 2022) "Technical performance assessment of quantitative imaging in radiological device premarket submissions".
#### Validation Performance Testing
The clinical performance of the Strain Al was successfully evaluated on test data encompassing diverse demographic variables, including gender, age (ranging from 21 to 96), and ethnicity from multiple clinical sites in metropolitan cities with diverse racial patient populations. The Strain function was evaluated with subjects, on images acquired during a routine clinical practice from cart-based and portable ultrasound devices (with frequency ranging from 1.2 to 4 MHz).
The test data was entirely separated from the training/validation datasets acquired from independent clinical sites and was not used for any part of the training. We established auditability measures, by assigning a unique identification number to each study and its corresponding images.
The ground truth (reference data) was obtained using the reference device. Performance was assessed by calculating the intraclass correlation coefficient (ICC) and root mean square difference (RMSD).
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Image /page/7/Picture/0 description: The image shows the logo for EXO. The logo consists of two parts: a cluster of blue circles on the left and the word "EXO" in a dark gray sans-serif font on the right. The blue circles are arranged in a pattern that resembles a network or constellation. The word "EXO" is written in all capital letters and has a modern, clean appearance.
The measurement accuracy of Strain Al for cardiac ultrasound images compared with reference data is summarized in Table 1 below:
| Table 1: Summary of Strain AI accuracy and reliability for cardiac ultrasound images | | |
|--------------------------------------------------------------------------------------|--|--|
|--------------------------------------------------------------------------------------|--|--|
| Measurement | ICC (95% CI) | RMSD (95% CI) |
|----------------------------------|--------------------|--------------------|
| Global Longitudinal Strain (GLS) | 0.95 (0.91 - 0.97) | 2.76 (2.44 - 3.17) |
The device performance was also assessed across a wide range of Ultrasound manufacturers and demographic subgroups (Gender, Image source, Age and BMI). The evaluation concluded that the device performance was consistent among clinically meaningful subgroups.
#### Conclusions
Exo's Strain Al is substantially equivalent in intended use, design, principles of operation, technological characteristics, and safety features to the predicate device. There are no different questions of safety and/or effectiveness introduced by the Strain Al when used as intended.
