(92 days)
Yes
The device description explicitly mentions "AI Platform" and describes "AI-assisted tools" for quantifying ejection fraction and suggesting lung structures/artifacts.
No
The device is intended for noninvasive processing of ultrasound images to detect, measure, and calculate medical parameters for diagnostic assessment, not for treatment or therapy.
Yes
The device "detect[s], measure[s], and calculate[s] relevant medical parameters of structures and function of adult patients with suspected disease," which aids in diagnosis.
Yes
The device description explicitly states "Exo Al Platform is a software as a medical device (SaMD)". It takes DICOM images as input and provides software-based analysis and reporting tools. There is no mention of accompanying hardware components that are part of the device itself.
Based on the provided information, this device is not an In Vitro Diagnostic (IVD).
Here's why:
- IVDs analyze samples taken from the human body. This device processes images of the human body acquired through ultrasound.
- The intended use is image processing and analysis. The device helps with the interpretation of ultrasound images to detect, measure, and calculate parameters of structures and function within the body.
- The input is DICOM images from ultrasound scanners. This is image data, not biological samples.
IVDs typically involve analyzing substances like blood, urine, tissue, etc., to provide information about a person's health status. This device operates on visual data acquired non-invasively.
No
The letter does not explicitly state that the FDA has reviewed and approved or cleared a PCCP for this specific device.
Intended Use / Indications for Use
The AI Platform is intended for noninvasive processing of ultrasound images to detect, measure, and calculate relevant medical parameters of structures and function of adult patients with suspected disease. The device is intended to be used on images from adult patients.
Product codes
QIH
Device Description
Exo Al Platform is a software as a medical device (SaMD) that helps qualified users with image-based assessment of ultrasound examinations in adult patients. It is designed to simplify workflow by helping trained healthcare providers evaluate, quantify, and generate reports for ultrasound images. The device is intended to generate images and a report that can be reviewed in a typical standard of care setting.
Al Platform takes as an input imported Digital Imaging and Communications in Medicine (DICOM) images from ultrasound scanners of a specific range and allows users to detect, measure, and calculate relevant medical parameters of structures and function of patients with suspected disease. It provides users with a specific toolset for viewing ultrasound images of the lung and heart, placing landmarks, and creating reports.
Key features of the software are
- LVEF AI: an Al-assisted tool for quantification of ejection on cardiac ultrasound images.
- . Lung Al: an Al-assisted tool to suggest presence of lung structures and artifacts on ultrasound images.
Exo Al Platform does not perform any function that could not be accomplished by a trained user manually. It's important to note that patient management decisions should not be made solely on the results of the Al Platform analysis.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes
Input Imaging Modality
Ultrasound
Anatomical Site
Heart, Lungs
Indicated Patient Age Range
Adult patients
Intended User / Care Setting
Qualified users / trained healthcare providers. A typical standard of care setting.
Description of the training set, sample size, data source, and annotation protocol
The test data was entirely separated from the training/validation datasets and was not used for any part of the training. To ensure data separation and generalizability, the data sources used in the test set are chosen to be different from the data sources used in the training set.
Description of the test set, sample size, data source, and annotation protocol
The clinical performance of the Al platform was successfully evaluated on a test data encompassing diverse demographic variables, including gender, age (ranging from 20 to 96), BMI (ranging from 15.3 to 52.8), and ethnicity from multiple clinical sites in metropolitan cities with diverse racial patient populations. The Lung function was evaluated with 125 subjects, on images acquired during a routine clinical practice from cart-based and portable ultrasound devices (with frequency ranging from 1.5 to 7 MHz). The LVEF function was evaluated with 151 subjects, on images acquired 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 and was not used for any part of the training. To ensure data separation and generalizability, the data sources used in the test set are chosen to be different from the data sources used in the training set. We also established auditability measures, by assigning a unique identification number to each study and its corresponding images.
The ground truth for ejection fraction (reference data) was obtained as the average ejection fraction measurement of three experts.
The ground truth of the presence of A-line was determined by consensus of two or more experts.
The ground truth of B-line counts was determined as the average of B-line counts from three experts.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Validation Performance Testing
The clinical performance of the Al platform was successfully evaluated on a test data encompassing diverse demographic variables, including gender, age (ranging from 20 to 96), BMI (ranging from 15.3 to 52.8), and ethnicity from multiple clinical sites in metropolitan cities with diverse racial patient populations. The Lung function was evaluated with 125 subjects, on images acquired during a routine clinical practice from cart-based and portable ultrasound devices (with frequency ranging from 1.5 to 7 MHz). The LVEF function was evaluated with 151 subjects, on images acquired from cart-based and portable ultrasound devices (with frequency ranging from 1.2 to 4 MHz).
Performance was assessed by calculating the intraclass correlation coefficient (ICC) and ejection fraction root mean square difference (RMSD).
Performance was assessed by measuring the agreement using Cohen's kappa coefficient (k).
Table 1: Summary of Al Platform accuracy and reliability for cardiac ultrasound images
| Subgroup (View) | ICC (95% CI) | RMSD (95% CI) |
|---|---|---|
| Ejection Fraction Parasternal Long-axis | 0.93 (0.89 - 0.96) | 6.12 (5.30 - 8.36) |
| Ejection Fraction Apical Biplane | 0.95 (0.90 - 0.98) | 4.81 (3.99 - 7.25) |
| Ejection Fraction Apical (AP4) Single Plane | 0.92 (0.88 - 0.95) | 6.06 (5.27 - 8.20) |
| Ejection Fraction Apical (AP2) Single Plane | 0.92 (0.87 - 0.95) | 6.25 (5.33 - 8.82) |
| All | 0.93 (0.91 - 0.95) | 5.90 (5.35 - 7.23) |
Table 2: Summary of Al Platform reliability for lung ultrasound images
| Reliability | |
|---|---|
| A-lines | Kappa = 0.84 |
| B-lines | ICC = 0.97 |
The device performance was also assessed across a wide range of Ultrasound manufacturer, demographic subgroups, (including gender and BMI) and clinical confounders present including heart failure with reduced ejection fraction, Covid-19, Chronic obstructive pulmonary disease (COPD), Pneumonia, Pulmonary Edema, Coronary artery disease (CAD) and Cardiomyopathy. The evaluation concluded that the device performance was consistent among clinically meaningful subgroups.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
ICC (Intraclass Correlation Coefficient), RMSD (Root Mean Square Difference), Kappa (Cohen's kappa coefficient).
Predicate Device(s)
Reference Device(s)
Predetermined Change Control Plan (PCCP) - All Relevant Information
Not Found
§ 892.2050 Medical image management and processing system.
(a)
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.(b)
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 & Drug Administration (FDA). The logo consists of two parts: the Department of Health & Human Services seal on the left and the FDA acronym followed by the full name of the agency on the right. The FDA part of the logo is in blue, with the acronym in a solid blue square and the agency name in a lighter blue. The text reads "FDA U.S. FOOD & DRUG ADMINISTRATION".
Exo Inc Jacqueline Murray Senior Regulatory Affairs Specialist 4201 Burton Drive Santa Clara, CA 95054
November 17, 2023
Re: K232501
Trade/Device Name: AI Platform (AIP001) Regulation Number: 21 CFR 892.2050 Regulation Name: Medical Image Management And Processing System Regulatory Class: Class II Product Code: QIH Dated: October 25, 2023 Received: October 26, 2023
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/cdrb/cfdocs/cfpmn/pmn.cfm identifies.combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.
If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.
Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device" (https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).
1
Your device is also subject to, among other requirements, the Quality System (QS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30, Design controls; 21 CFR 820.90, Nonconforming product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review, the QS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181).
Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR Part 803) for devices or postmarketing safety reporting (21 CFR Part 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.
For comprehensive regulatory information about 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
Jessica Lamb, Ph.D. Assistant Director Imaging Software Team DHT 8B: Division of Radiological Imaging Devices and Electronic Products OHT 8: Office of Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
2
Indications for Use
510(k) Number (if known) K232501
Device Name AI Platform (AIP001)
Indications for Use (Describe)
The AI Platform is intended for noninvasive processing of ultrasound images to detect, measure, and calculate relevant medical parameters of structures and function of adult patients with suspected disease. The device is intended to be used on images from adult patients.
Type of Use (Select one or both, as applicable)
| ☑ Prescription Use (Part 21 CFR 801 Subpart D) |
|---|
| ☐ Over-The-Counter Use (21 CFR 801 Subpart C) |
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Image /page/3/Picture/1 description: The image shows the logo for EXO. The logo consists of a pattern of blue and green dots on the left, followed by the word "EXO" in a dark gray sans-serif font on the right. The dots are arranged in a grid-like pattern, with the color transitioning from blue to green.
510(k) Summary
General Information
| 510(k) Sponsor | Exo Imaging |
|---|---|
| Address | 4201 Burton Drive |
| Santa Clara, CA 95054 | |
| Correspondence Person | Jacqueline Murray |
| Contact Information | jmurray@exo.inc |
| Cell: +1 236 838-5056 | |
| Date Prepared | September 21st, 2023 |
Proposed Device
| Proprietary Name | AI Platform (AIP001) |
|---|---|
| Common Name | AI Platform |
| 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 | Lumify Diagnostic Ultrasound System |
|---|---|
| Premarket Notification | K223771 |
| Classification Name | Ultrasonic pulsed doppler imaging system |
| Regulation Number | 21 CFR 892.1550 |
| Product Code | IYN, IYO, ITX, QIH |
| Regulatory Class | II |
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Image /page/4/Picture/1 description: The image contains the logo for EXO. On the left side of the logo is a pattern of blue circles arranged in a grid-like fashion. The circles are arranged in a way that they form a larger X shape. To the right of the circles is the word "EXO" in a sans-serif font. The letters are dark gray.
Device Description
Exo Al Platform is a software as a medical device (SaMD) that helps qualified users with image-based assessment of ultrasound examinations in adult patients. It is designed to simplify workflow by helping trained healthcare providers evaluate, quantify, and generate reports for ultrasound images. The device is intended to generate images and a report that can be reviewed in a typical standard of care setting.
Al Platform takes as an input imported Digital Imaging and Communications in Medicine (DICOM) images from ultrasound scanners of a specific range and allows users to detect, measure, and calculate relevant medical parameters of structures and function of patients with suspected disease. It provides users with a specific toolset for viewing ultrasound images of the lung and heart, placing landmarks, and creating reports.
Key features of the software are
- LVEF AI: an Al-assisted tool for quantification of ejection on cardiac ultrasound images.
- . Lung Al: an Al-assisted tool to suggest presence of lung structures and artifacts on ultrasound images.
Exo Al Platform does not perform any function that could not be accomplished by a trained user manually. It's important to note that patient management decisions should not be made solely on the results of the Al Platform analysis.
Indications for Use
The Al Platform is intended for noninvasive processing of ultrasound images to detect, measure, and calculate relevant medical parameters of structures and function of adult patients with suspected disease. The device is intended to be used on images from adult patients.
Comparison of Technological Characteristics with the Predicate Device
| Feature/
Function | Subject Device
Exo Al Platform | Predicate Device
LVivo Software Application
(K210053) | Reference Device
Lumify Diagnostic
Ultrasound System
(K223771) |
|--------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------|-------------------------------------------------------------------------|
| Image input | Complies with DICOM
Standard | Same as subject device | Same as subject device |
| Scan type | Single and Multi-frame
ultrasound images | Same as subject device | Same as subject device |
| Feature/
Function | Subject Device
Exo Al Platform | Predicate Device
LVivo Software Application
(K210053) | Reference Device
Lumify Diagnostic
Ultrasound System
(K223771) |
| Image display
mode | Static | Same as subject device | Same as subject device |
| Image navigation
and manipulation
tools | Slice-scroll, pane layout,
reset | Same as subject device | Same as subject device |
| Image review | Yes, capable of reviewing
all frames of multi-frame
(multi-slice) images | Same as subject device | Same as subject device |
| Principle of
Operation and
Technology | Ultrasound image
processing software
implementing artificial
intelligence including
non-adaptive machine
learning algorithms trained
with clinical data intended
for non-invasive analysis of
ultrasound data | Same as subject device | Same as subject device |
| Al Algorithm | Deep Convolutional Neural
Networks for
Segmentation or
Landmark Detection | Same as subject device | Same as subject device |
| Manual
Adjustments or
Editing by User
Allowed | Yes | Same as subject device | Same as subject device |
| Anatomical Sites | Heart, Lungs | Heart, Bladder | Lungs |
| Ejection Fraction
Measurement
Views | AP4, AP2, Bi-plane, PLAX | AP4, AP2, Bi-plane | No |
| Non Cardiac
functions | A-lines, B-lines | Bladder Volume | B-lines |
| Report creation | Yes | Same as subject device | Same as subject device |
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Image /page/5/Picture/1 description: The image contains a logo with two distinct parts. On the left, there is a cluster of blue and cyan gradient circles arranged in a pattern. To the right of the circles, the letters 'EXO' are written in a bold, dark gray sans-serif font. The overall design is clean and modern.
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Image /page/6/Picture/1 description: The image shows a logo with two distinct parts. On the left, there is a cluster of blue and light blue gradient circles arranged in a pattern resembling a stylized network or constellation. To the right of the circles, the letters 'EXO' are displayed in a bold, sans-serif font, with a dark gray color. The overall design is clean and modern, suggesting a technology-oriented or innovative company.
Performance Data
Safety and performance of the AI Platform has been evaluated and verified in accordance with software specifications and applicable performance standards through software verification and validation testing. Additionally, the software validation activities were performed in accordance with IEC 62304:2006/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 Al platform was successfully evaluated on a test data encompassing diverse demographic variables, including gender, age (ranging from 20 to 96), BMI (ranging from 15.3 to 52.8), and ethnicity from multiple clinical sites in metropolitan cities with diverse racial patient populations. The Lung function was evaluated with 125 subjects, on images acquired during a routine clinical practice from cart-based and portable ultrasound devices (with frequency ranging from 1.5 to 7 MHz). The LVEF function was evaluated with 151 subjects, on images acquired 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 and was not used for any part of the training. To ensure data separation and generalizability, the data sources used in the test set are chosen to be different from the data sources used in the training set. We also established auditability measures, by assigning a unique identification number to each study and its corresponding images.
The ground truth for ejection fraction (reference data) was obtained as the average ejection fraction measurement of three experts. Performance was assessed by calculating the intraclass correlation coefficient (ICC) and ejection fraction root mean square difference (RMSD).
The measurement accuracy of Al Platform for cardiac ultrasound images compared with reference data is summarized in Table 1 below:
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Image /page/7/Picture/1 description: The image shows the logo for EXO. The logo consists of a pattern of blue-green gradient dots arranged in a triangular shape on the left, followed by the word "EXO" in a dark gray sans-serif font on the right. The dots are arranged in a way that suggests a network or constellation, while the word "EXO" is simple and modern.
| Subgroup (View) | ICC (95% CI) | RMSD (95% CI) |
|---|---|---|
| Ejection Fraction Parasternal | ||
| Long-axis | 0.93 (0.89 - 0.96) | 6.12 (5.30 - 8.36) |
| Ejection Fraction Apical Biplane | 0.95 (0.90 - 0.98) | 4.81 (3.99 - 7.25) |
| Ejection Fraction Apical (AP4) | ||
| Single Plane | 0.92 (0.88 - 0.95) | 6.06 (5.27 - 8.20) |
| Ejection Fraction Apical (AP2) | ||
| Single Plane | 0.92 (0.87 - 0.95) | 6.25 (5.33 - 8.82) |
| All | 0.93 (0.91 - 0.95) | 5.90 (5.35 - 7.23) |
Table 1: Summary of Al Platform accuracy and reliability for cardiac ultrasound images
The ground truth of the presence of A-line was determined by consensus of two or more experts. Performance was assessed by measuring the agreement using Cohen's kappa coefficient (k), The ground truth of B-line counts was determined as the average of B-line counts from three experts. Performance was assessed by calculating the intraclass correlation coefficient (ICC). The reliability of Al platform for lung ultrasound images compared with reference data is summarized in Table 2 below:
Table 2: Summary of Al Platform reliability for lung ultrasound images
| Reliability | |
|---|---|
| A-lines | Kappa = 0.84 |
| B-lines | ICC = 0.97 |
The device performance was also assessed across a wide range of Ultrasound manufacturer, demographic subgroups, (including gender and BMI) and clinical confounders present including heart failure with reduced ejection fraction, Covid-19, Chronic obstructive pulmonary disease (COPD), Pneumonia, Pulmonary Edema, Coronary artery disease (CAD) and Cardiomyopathy. The evaluation concluded that the device performance was consistent among clinically meaningful subgroups.
Conclusions
Exo's Al Platform 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 Al Platform when used as intended.