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.

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.

AI/ML Overview

Acceptance Criteria & Device Performance Study for Exo AI Platform (AIP001)

The Exo AI Platform (AIP001) is a software as a medical device (SaMD) intended for noninvasive processing of ultrasound images to detect, measure, and calculate relevant medical parameters of structures and function in adult patients with suspected disease. This document outlines the acceptance criteria and the studies performed to demonstrate the device meets these criteria for both its cardiac (LVEF AI) and lung (Lung AI) functionalities.

1. Table of Acceptance Criteria and Reported Device Performance

For Cardiac Ultrasound (LVEF AI - Ejection Fraction Measurement):

Acceptance Criteria (Performance Metric)	Target	Reported Device Performance (95% CI)
Ejection Fraction Parasternal Long-axis
- Intraclass Correlation Coefficient (ICC)	High	0.93 (0.89 - 0.96)
- Root Mean Square Difference (RMSD)	Low	6.12 (5.30 - 8.36)
Ejection Fraction Apical Biplane
- Intraclass Correlation Coefficient (ICC)	High	0.95 (0.90 - 0.98)
- Root Mean Square Difference (RMSD)	Low	4.81 (3.99 - 7.25)
Ejection Fraction Apical (AP4) Single Plane
- Intraclass Correlation Coefficient (ICC)	High	0.92 (0.88 - 0.95)
- Root Mean Square Difference (RMSD)	Low	6.06 (5.27 - 8.20)
Ejection Fraction Apical (AP2) Single Plane
- Intraclass Correlation Coefficient (ICC)	High	0.92 (0.87 - 0.95)
- Root Mean Square Difference (RMSD)	Low	6.25 (5.33 - 8.82)
Overall Ejection Fraction Measurement (All Views)
- Intraclass Correlation Coefficient (ICC)	High	0.93 (0.91 - 0.95)
- Root Mean Square Difference (RMSD)	Low	5.90 (5.35 - 7.23)

For Lung Ultrasound (Lung AI - A-lines and B-lines detection/quantification):

Acceptance Criteria (Performance Metric)	Target	Reported Device Performance
A-lines Presence (Agreement)	High	Kappa = 0.84
B-lines Counts (Reliability)	High	ICC = 0.97

(Note: Specific quantitative targets for "High" ICC and "Low" RMSD/Kappa are not explicitly stated in the provided text, but the reported values demonstrate strong performance in common clinical contexts for these metrics.)

2. Sample Size Used for the Test Set and Data Provenance

LVEF AI (Cardiac Function): 151 subjects
Lung AI (Lung Function): 125 subjects

Data Provenance: The data was acquired during routine clinical practice from multiple clinical sites in metropolitan cities, ensuring diverse racial patient populations. The data encompassed diverse demographic variables, including gender, age (20-96 years), and BMI (15.3-52.8). The images were acquired from both cart-based and portable ultrasound devices. The test data was explicitly stated to be entirely separated from the training/validation datasets and not used for any part of the training. This suggests a retrospective collection of data designed for independent validation. The countries of origin are not specified, but "metropolitan cities with diverse racial patient populations" implies a multi-site, potentially multi-national, collection or at least a highly diverse domestic setting.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Their Qualifications

LVEF AI (Ejection Fraction): The ground truth was obtained as the average ejection fraction measurement of three experts.
Lung AI (A-line Presence): The ground truth was determined by consensus of two or more experts.
Lung AI (B-line Counts): The ground truth was determined as the average of B-line counts from three experts.

Qualifications of Experts: The document does not explicitly state the specific qualifications of these experts (e.g., number of years of experience, specific board certifications like radiologist or cardiologist). However, the context of "routine clinical practice" and "experts" implies highly qualified medical professionals experienced in interpreting cardiac and lung ultrasound images.

4. Adjudication Method for the Test Set

LVEF AI (Ejection Fraction): The adjudication method for the reference data (ground truth) was established by taking the average ejection fraction measurement of three experts. This implies a method akin to "average of multiple readers."
Lung AI (A-line Presence): The adjudication method for the ground truth was determined by consensus of two or more experts. This suggests a qualitative agreement, where at least two experts had to concur.
Lung AI (B-line Counts): The adjudication method for the ground truth was established by taking the average of B-line counts from three experts. (Similar to LVEF AI).

5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study was done

No, a Multi Reader Multi Case (MRMC) comparative effectiveness study was not done or reported in the provided text. The performance assessment focused on the standalone performance of the AI tool against expert-established ground truth, not on how human readers' performance improved with AI assistance.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) was done

Yes, a standalone performance study was done. The reported ICC, RMSD, and Kappa values directly assess the AI Platform's accuracy and reliability in generating measurements and detections independently, against the expert-derived ground truth. The statement that "Exo AI Platform does not perform any function that could not be accomplished by a trained user manually" also reinforces its role as an automated tool, evaluated on its own.

7. The Type of Ground Truth Used

The type of ground truth used was expert consensus / expert measurement.

For Cardiac Ejection Fraction: Average measurements from three experts.
For Lung A-line Presence: Consensus of two or more experts.
For Lung B-line Counts: Average counts from three experts.

8. The Sample Size for the Training Set

The sample size for the training set is not specified in the provided text. The document only explicitly mentions that the test data was entirely separated from the training/validation datasets.

9. How the Ground Truth for the Training Set Was Established

The document does not specify how the ground truth for the training set was established. It only mentions that the AI algorithms (Deep Convolutional Neural Networks) were "trained with clinical data."

Summary

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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).

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Your device is also subject to, among other requirements, the Quality System (QS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30, Design controls; 21 CFR 820.90, Nonconforming product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review, the QS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181).

Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR Part 803) for devices or postmarketing safety reporting (21 CFR Part 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.

Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.

For comprehensive regulatory information about 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

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Indications for Use

510(k) Number (if known) K232501

Device Name AI Platform (AIP001)

Indications for Use (Describe)

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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510(k) Summary

K232501

General Information

510(k) Sponsor	Exo Imaging
Address	4201 Burton DriveSanta Clara, CA 95054
Correspondence Person	Jacqueline Murray
Contact Information	jmurray@exo.incCell: +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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Device Description

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.

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 DeviceExo Al Platform	Predicate DeviceLVivo Software Application(K210053)	Reference DeviceLumify DiagnosticUltrasound System(K223771)
Image input	Complies with DICOMStandard	Same as subject device	Same as subject device
Scan type	Single and Multi-frameultrasound images	Same as subject device	Same as subject device
Feature/Function	Subject DeviceExo Al Platform	Predicate DeviceLVivo Software Application(K210053)	Reference DeviceLumify DiagnosticUltrasound System(K223771)
Image displaymode	Static	Same as subject device	Same as subject device
Image navigationand manipulationtools	Slice-scroll, pane layout,reset	Same as subject device	Same as subject device
Image review	Yes, capable of reviewingall frames of multi-frame(multi-slice) images	Same as subject device	Same as subject device
Principle ofOperation andTechnology	Ultrasound imageprocessing softwareimplementing artificialintelligence includingnon-adaptive machinelearning algorithms trainedwith clinical data intendedfor non-invasive analysis ofultrasound data	Same as subject device	Same as subject device
Al Algorithm	Deep Convolutional NeuralNetworks forSegmentation orLandmark Detection	Same as subject device	Same as subject device
ManualAdjustments orEditing by UserAllowed	Yes	Same as subject device	Same as subject device
Anatomical Sites	Heart, Lungs	Heart, Bladder	Lungs
Ejection FractionMeasurementViews	AP4, AP2, Bi-plane, PLAX	AP4, AP2, Bi-plane	No
Non Cardiacfunctions	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/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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Subgroup (View)	ICC (95% CI)	RMSD (95% CI)
Ejection Fraction ParasternalLong-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.

Regulation Number and Section

§ 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).