(119 days)
Al Platform 2.0 is intended for noninvasive processing of ultrasound images to detect, measure, and calculate relevant medical parameters of structures and function of patients with suspected disease. In addition, it can provide Quality Score feedback to assist healthcare professionals, trained and qualified to conduct echocardiography and lung ultrasound scans in the current standard of care while acquiring ultrasound images. The device is intended to be used on images of adult patients.
Exo Al Platform 2.0 (AIP 2.0) 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. AIP 2.0 takes as an input in the Digital Imaging and Communications in Medicine (DICOM) format 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. In addition, it provides frame and clip quality score in real-time for the Left Ventricle from the four-chamber apical and parasternal long axis views of the heart and lung scans. In addition, the Al modules are provided as a software component to be integrated by another computer programmer into their legally marketed ultrasound imaging device. Essentially, the Algorithm and API, which are modules, are medical device accessories.
Key features of the software are
- Lung Al: An Al-assisted tool for suggesting the presence of lung structures and artifacts on ultrasound images, namely A-lines. Additionally, a per-frame and per-clip quality score is generated for each lung scan.
- Cardiac Al: An Al-assisted tool for the quantification of Left Ventricular Ejection Fraction (LVEF), Myocardium wall thickness (Interventricular Septum (IVSd), Posterior wall (PWd)), and IVC diameter on cardiac ultrasound images. Additionally, a per-frame and per-clip quality score is generated for each Apical and PLAX cardiac scan.
The provided text describes the acceptance criteria and the study that proves the device, AI Platform 2.0 (AIP002), meets these criteria for specific functionalities. This device is a software as a medical device (SaMD) intended for processing ultrasound images for adult patients, including detecting, measuring, and calculating medical parameters, and providing quality score feedback during image acquisition.
Here's a breakdown of the requested information:
1. A table of acceptance criteria and the reported device performance
The document specifies performance metrics for two main functionalities tested: Left Ventricle Wall Thickness and Inferior Vena Cava (IVC) measurements, and Quality AI (for frames and clips). The acceptance criteria are implicitly high correlation with expert measurements, indicated by high Interclass Correlation (ICC) values.
| Functionality/Measurement | Acceptance Criteria (Implicit) | Reported Device Performance (ICC with 95% CI) |
|---|---|---|
| LV Wall Thickness | High correlation with experts | |
| InterVentricular Septum (IVSd) | 0.93 (0.89 – 0.96) | |
| Posterior Wall (PWd) | 0.94 (0.89 – 0.97) | |
| Inferior Vena Cava (IVC) | High correlation with experts | |
| IVC Dmin | 0.93 (0.90 – 0.95) | |
| IVC Dmax | 0.94 (0.90 – 0.96) | |
| Quality AI | High agreement with experts | |
| Overall agreement (frames) | 0.94 (0.94 – 0.95) | |
| Overall agreement (clips) | 0.94 (0.92 – 0.95) | |
| Diagnostic Classification | >95% agreement with experts (ACEP score >=3) | 98.3% of clips rated ACEP >=3 by experts received at least "Minimum criteria met for diagnosis" by Clip Quality AI. 98.0% of scans considered "Minimal criteria met for diagnosis" or "good" by Quality AI were deemed diagnostic by experts (ACEP score of 3 or higher). |
2. Sample size used for the test set and the data provenance
- LV Wall Thickness and IVC measurements: 100 subjects.
- Quality AI (Section a): 184 patients, resulting in 226 clips (29,732 frames).
- Quality AI (Section b, real-time scanning): 396 lung and cardiac scans.
- Data Provenance: The test data encompassed diverse demographic variables (gender, age, ethnicity) from multiple sites in metropolitan cities with diverse racial patient populations. The text states the data was entirely separated from the training/tuning datasets. The studies were retrospective for the initial quality evaluation (comparing to previously acquired data rated by sonographers) and prospective for the real-time quality AI evaluation (data acquired while using the AI in real-time by users with varying experience).
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
- LV Wall Thickness and IVC measurements: Ground truth was established as the average measurement of three experts. Their specific qualifications (e.g., years of experience, specialty) are not explicitly stated beyond "experts."
- Quality AI (Section a): Ground truth was established by "experienced sonographers." Their number and specific qualifications are not detailed beyond "experienced."
- Quality AI (Section b, real-time scanning): Ground truth for diagnostic classification was established by "expert readers" (ACEP score of 3 or above). Their number and specific qualifications are not detailed beyond "expert readers."
4. Adjudication method for the test set
- LV Wall Thickness and IVC measurements: The adjudication method was taking the average measurement of three experts. This implies a form of consensus or central tendency for ground truth.
- Quality AI (Section a): Ground truth was based on "quality rating by experienced sonographers on each frame and the entire clip." It doesn't explicitly state an adjudication method beyond this, implying individual expert ratings were used or a single consensus was reached, but not a specific multi-reader adjudication process like 2+1 or 3+1.
- Quality AI (Section b): Ground truth was based on "ACEP quality of 3 or above by expert readers." Similar to Section a, a specific adjudication method beyond "expert readers" is not detailed.
5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance
The document does not explicitly describe a traditional MRMC comparative effectiveness study that directly quantifies the improvement of human readers with AI assistance versus without AI assistance.
The Quality AI section (b) indicates that 26 users (including 18 novice users) conducted 396 lung and cardiac scans using the real-time quality AI feedback. This suggests an evaluation of the AI's ability to guide users to acquire diagnostic quality images, which is an indirect measure of assisting human performance. However, it does not provide an effect size of how much human readers improve in their interpretation or diagnosis with AI assistance. The study focuses on the AI's ability to help users acquire diagnostic quality images.
6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done
Yes, standalone performance was evaluated for the following:
- Left Ventricle Wall Thickness and IVC measurements: The performance (ICC) was calculated directly between the AI's measurements and the expert-derived ground truth. This is a standalone performance metric.
- Quality AI (Section a): The overall agreement (ICC) between the Quality AI and quality ratings by experienced sonographers was calculated. This also represents standalone performance of the AI's quality assessment function.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
The ground truth used for the evaluated functionalities was expert consensus/measurement:
- LV Wall Thickness and IVC measurements: Average measurement of three experts.
- Quality AI: Quality ratings by experienced sonographers (Section a) and ACEP quality scores by expert readers (Section b).
No mention of pathology or outcomes data as ground truth.
8. The sample size for the training set
The document explicitly states: "The test data was entirely separated from the training/tuning datasets and was not used for any part of the training/tuning." However, it does not provide the specific sample size for the training set.
9. How the ground truth for the training set was established
The document does not explicitly describe how the ground truth for the training set was established. It only mentions that the AI models use "non-adaptive machine learning algorithms trained with clinical data." The Predetermined Change Control Plan also refers to "new training data" and augmenting the training dataset, but without details on ground truth establishment for these training datasets.
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August 5, 2024
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Exo Imaging Jacqueline Murray Senior Regulatory Affairs Specialist 4201 Burton Drive Santa Clara, California 95054
Re: K240953
Trade/Device Name: AI Platform 2.0 (AIP002) Regulation Number: 21 CFR 892.2050 Regulation Name: Medical Image Management And Processing System Regulatory Class: Class II Product Code: QIH Dated: July 2, 2024 Received: July 3, 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.
FDA's substantial equivalence determination also included the review and clearance of your Predetermined Change Control Plan (PCCP). Under section 515C(b)(1) of the Act, a new premarket notification is not required for a change to a device cleared under section 510(k) of the Act, if such change is consistent with an established PCCP granted pursuant to section 515C(b)(2) of the Act. Under 21 CFR 807.81(a)(3), a new premarket notification is required if there is a major change or modification in the intended use of a device,
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or if there is a change or modification in a device that could significantly affect the safety or effectiveness of the device, e.g., a significant change or modification in design, material, chemical composition, energy source, or manufacturing process. Accordingly, if deviations from the established PCCP result in a major change or modification in the intended use of the device, or result in a change or modification in the device that could significantly affect the safety or effectiveness of the a new premarket notification would be required consistent with section 515C(b)(1) of the Act and 21 CFR 807.81(a)(3). Failure to submit such a premarket submission would constitute adulteration and misbranding under sections 501(f)(1)(B) and 502(o) of the Act, respectively.
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).
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).
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Sincerely,
Saml for
Jessica Lamb, Ph.D. Assistant Director 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) K240953
Device Name AI Platform 2.0 (AIP002)
Indications for Use (Describe)
Al Platform 2.0 is intended for noninvasive processing of ultrasound images to detect, measure, and calculate relevant medical parameters of structures and function of patients with suspected disease. In addition, it can provide Quality Score feedback to assist healthcare professionals, trained and qualified to conduct echocardiography and lung ultrasound scans in the current standard of care while acquiring ultrasound images. The device is intended to be used on images of adult patients.
| Type of Use (Select one or both, as applicable) | |
|---|---|
| ☒ Prescription Use (Part 21 CER 801 Subpart D) | □ Over-The-Counter Use (21 CER 801 Subpart C) |
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K240953
510(k) Summary
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 | April 08, 2024 |
Proposed Device
| Proprietary Name | Al Platform 2.0 (AIP002) |
|---|---|
| Common Name | Al Platform 2.0 |
| Classification Name | Automated Radiological Image Processing Software |
| Regulation Number | 21 CFR 892.2050 |
| Product Code | QIH |
| Regulatory Class | II |
Predicate Device
| Proprietary Name | LVivo IQS |
|---|---|
| Premarket Notification | K222970 |
| Classification Name | Automated Radiological Image Processing Software |
| Regulation Number | 21 CFR 892.2050 |
| Product Code | QIH |
| Regulatory Class | II |
Reference Device 1
| Proprietary Name | Caption Guidance |
|---|---|
| Premarket Notification | DEN190040 |
| Classification Name | Image Acquisition And/Or Optimization Guided by ArtificialIntelligence |
| Regulation Number | 21 CFR 892.2100 |
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Image /page/5/Picture/1 description: The image shows the logo for EXO. On the left side of the logo, there is a cluster of blue and green dots arranged in a pattern. To the right of the dots, the word "EXO" is written in a dark gray sans-serif font. The letters are bold and spaced closely together.
| Product Code | QJU |
|---|---|
| Regulatory Class | II |
Reference Device 2
| Proprietary Name | AI Platform |
|---|---|
| Premarket Notification | K232501 |
| Classification Name | Automated Radiological Image Processing Software |
| Regulation Number | 21 CFR 892.2050 |
| Product Code | QIH |
| Regulatory Class | II |
Device Description
Exo Al Platform 2.0 (AIP 2.0) 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. AIP 2.0 takes as an input in the Digital Imaging and Communications in Medicine (DICOM) format 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. In addition, it provides frame and clip quality score in real-time for the Left Ventricle from the four-chamber apical and parasternal long axis views of the heart and lung scans. In addition, the Al modules are provided as a software component to be integrated by another computer programmer into their legally marketed ultrasound imaging device. Essentially, the Algorithm and API, which are modules, are medical device accessories.
Key features of the software are
- Lung Al: An Al-assisted tool for suggesting the presence of lung structures and artifacts on ● ultrasound images, namely A-lines. Additionally, a per-frame and per-clip quality score is generated for each lung scan.
- . Cardiac Al: An Al-assisted tool for the quantification of Left Ventricular Ejection Fraction (LVEF), Myocardium wall thickness (Interventricular Septum (IVSd), Posterior wall (PWd)), and IVC diameter on cardiac ultrasound images. Additionally, a per-frame and per-clip quality score is generated for each Apical and PLAX cardiac scan.
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Image /page/6/Picture/1 description: The image contains the logo for EXO. On the left side of the logo, there is a cluster of blue circles arranged in a pattern. To the right of the circles, the word "EXO" is written in a bold, sans-serif font. The color of the text is a dark gray or black.
Warning:
It's important to note that patient management decisions should not be made solely on the Al Platform 2.0 analysis results. Users are fully responsible for ensuring scan quality and making diagnoses. Images obtained through the use of Quality Al should be interpreted exclusively by certified healthcare professionals. It is also imperative that a qualified healthcare professional reviews the data, ensuring its adequacy and appropriateness for the intended diagnosis or management.
Indications for Use
Al Platform 2.0 is intended for noninvasive processing of ultrasound images to detect, measure, and calculate relevant medical parameters of structures and function of patients with suspected disease. In addition, it can provide Quality Score feedback to assist healthcare professionals, trained and qualified to conduct echocardiography and lung ultrasound scans in the current standard of care while acquiring ultrasound images. The device is intended to be used on images of adult patients.
Comparison of Technological Characteristics with the Predicate Device
| Feature/Function | Subject Device Exo Al Platform 2.0 | Predicate Device: LVivo IQS (K222970) | Reference Device 1: Caption Guidance (DEN190040) | Reference Device 2: Al Platform (K232501) |
|---|---|---|---|---|
| Scan type | Single and Multi-frame ultrasound images | Same as subject device | 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 | Same as subject device |
| Feature/Function | Subject DeviceExo Al Platform 2.0 | Predicate Device:LVivo IQS(K222970) | Reference Device1:Caption Guidance(DEN190040) | Reference Device2:Al Platform(K232501) |
| Al Algorithm | Deep ConvolutionalNeural Networks forSegmentation,Landmark Detectionand Classification | Same as subjectdevice | Same as subjectdevice | Same as subjectdevice |
| AnatomicalSites | Heart, Lungs | Heart, Bladder | Heart | Heart, Lungs |
| CardiacMeasurements | LVEFIVC Minimumdiameter oninspiration andMaximum diameteron expirationMyocardium wallthickness(InterventricularSeptum andPosterior wall) fromPlax view | LVEF, GLSRV size andfunction: FractionalArea Change(FAC), Free WallStrain (FWS),Tricuspid annularplane systolicexcursion (TAPSE) | No | LVEF |
| Non Cardiac Almodules | Absence/presence ofA-linesB-lines count | Bladder Volume | No | Absence / presence ofA-linesB-lines count |
| Real timefeedback onquality | Yes | Same as subjectdevice | Same as subjectdevice | No |
| Retrospectivelyrecording ofDiagnosticquality clip | Yes | No | Same as subjectDevice | No |
| Feature/Function | Subject DeviceExo Al Platform 2.0 | Predicate Device:LVivo IQS(K222970) | Reference Device1:Caption Guidance(DEN190040) | Reference Device2:Al Platform(K232501) |
| Al modules arean accessory tocompatiblegeneralpurposediagnosticultrasoundsystems | Yes | Same as subjectdevice | Same as subjectdevice | No |
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Image /page/7/Picture/1 description: The image shows the logo for EXO. On the left side of the logo, there is a cluster of blue and cyan circles arranged in a pattern. To the right of the circles, the word "EXO" is written in a dark gray sans-serif font.
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Image /page/8/Picture/1 description: The image shows the logo for EXO. On the left side of the logo, there is a cluster of blue and cyan circles arranged in a circular pattern. 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.
Performance Data
Safety and performance of the Al Platform 2.0 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 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 2.0 was successfully evaluated on test data encompassing diverse demographic variables, including gender, age, and ethnicity from multiple sites in metropolitan cities with diverse racial patient populations. The test data was entirely separated from the training/tuning datasets and was not used for any part of the training/tuning. We also established auditability measures, by assigning a unique identification number to each study and its corresponding images.
The left ventricle Wall thickness Al and IVC Al were each evaluated on scans of 100 subjects, on images acquired from cart-based and portable ultrasound devices. The ground truth for all measurements were obtained as the average measurement of three experts. Performance was assessed by calculating Inter class correlation (ICC) between the AI and the ground truth.
The performance of Al Platform 2.0 for wall thickness and IVC compared with reference data is summarized in Table 1 below:
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| Measurement | ICC (95% CI) | |
|---|---|---|
| LV Wall thickness | InterVentricular Septum (IVSd) | 0.93 (0.89 – 0.96) |
| Posterior Wall (PWd) | 0.94 (0.89 – 0.97) | |
| Inferior vena cava (IVC) | IVC Dmin | 0.93 (0.90 – 0.95) |
| IVC Dmax | 0.94 (0.90 – 0.96) |
Table 1: Summary of Al Platform performance for left ventricle wall thickness and IVC measurement
The validation of Quality Al consists of validating on:
- a. Data previously acquired from 184 patients from various ultrasound devices and various cardiac pathologies, compared to quality rating by experienced sonographers on each frame and the entire clip. Total of 226 clips (29,732 frames) were used in this test. The overall agreement between the Quality Al and quality rated by the experienced sonographers was ICC = 0.94 (95% Cl .94 - . 95) for frames and ICC = 0.94 (95% Cl .92 - . 95) for clips.
- b. Data acquired after using the image and clip Quality Al in real time while scanning Lung, Apical 4 chamber, and Parasternal Long Axis views of the heart. In total, 396 lung and cardiac scans were done by 26 users with a wide range of ultrasound experience in POC settings, including 18 novice users who received two hours of training. 98.3% of the clips rated as ACEP quality of 3 or above by expert readers, also received at least "Minimum criteria met for diagnosis" image quality by Clip Quality Al. Additionally, 98.0% of scans that were considered as "Minimal criteria met for diagnosis" or "good" by Quality Al were also deemed diagnostic by experts (ACEP score of 3 or higher).
Predetermined Change Control Plan
A Predetermined Change Control Plan (PCCP) is included with Al Platform 2.0. The PCCP specifies anticipated software modifications, implementation methods and validation criteria that will be used to implement the software modifications in a controlled manner and ensure the subject device remains as safe and effective as the predicate.
The PCCP includes a specific list of anticipated software modifications which are summarized in the table below, as well as a Modification protocol describing the verification and validation activities that will support these planned modifications. The modification protocol incorporates impact assessment considerations and specifies requirements for data management, including data sources, collection, storage, and sequestration, as well as documentation and data segregation/re-use practices.
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Image /page/10/Picture/1 description: The image shows a logo with two distinct parts. On the left, there is a cluster of blue-green gradient dots arranged in a circular or network-like pattern. To the right of the dots, 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 or innovation-related brand.
Specific test methods are specified in the PCCP to establish substantial equivalence relative to Al Platform 2.0, and include sample size determination, and acceptance criteria. Original and additional validation data will be analyzed to ensure the datasets are representative of the intended use population. To ensure validation test datasets are representative of the intended use population, each will meet minimum demographic requirements.
A summary of these software modifications, test methods, necessary performance requirements, and impact assessment, are presented in the table below:
| Modification | Rationale | Testing Methods | Impact Assessment |
|---|---|---|---|
| (A) Modificationto architecture,pre/postprocessing | The Al models inAl Platform 2.0may be modifiedin a focused andbound manner, toimprove accuracy,efficiency, andadaptability whilemaintaining thesafety and efficacyof the device. | Substantial equivalencein performance matricesas compared to the AlPlatform 2.0.Non-inferiority margins inalignment with FDArecommendations will beused to ensure the newdevice remains safe andeffective without anysuch change comparedwith the original deviceand the last modifiedversion of the device. | Improved accuracy and/or efficiency metrics forAl model performance.Benefit-Risk Analysis:Benefit: Enhanced performance; Improvedefficiency and scalability.Risk: Reduction in clinical performanceRisk Mitigation:The risks of overfitting and generalizability aremitigated by conducting cross-validation, dataaugmentation, and hyper parameter tuning ininternal training procedures.The final validation is performed usingnon-inferiority analysis according to theestablished clinical protocol to ensure baselineperformance is maintained and improvementsare realized before modifications are committedand released. |
| (B) Introductionof new trainingdata | The trainingdataset of the Almodels may beaugmented with abroader and morediverse range ofimaging data toenhance modelrobustness,reduce bias,improvegeneralizability,and respondeffectively toreal-worldfeedback. | Substantial equivalencein performance matricesas compared to the AlPlatform 2.0.Non-inferiority margins inalignment with FDArecommendations will beused to ensure the newdevice remains safe andeffective without anysuch change comparedwith the original deviceand the last modifiedversion of the device | Improved model generalizability for Al modeloutputs displayed to users.Benefit-Risk Analysis:Benefit: Improved model robustness, reducedbias, enhanced generalizability, and respondingto real world feedback.Risk: Reduction in clinical performanceRisk Mitigation:The risks of overfitting and generalizability aremitigated by conducting cross-validation, dataaugmentation, and hyper parameter tuning ininternal training procedures.The final validation is performed usingnon-inferiority analysis according to theestablished clinical protocol to ensure baseline |
| performance is maintained and improvementsare realized before modifications are committedand released. |
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Image /page/11/Picture/1 description: The image shows the logo for EXO. The logo consists of a pattern of blue circles on the left and the word "EXO" in dark gray on the right. The circles are arranged in a grid-like pattern, with some circles closer together than others. The word "EXO" is written in a sans-serif font.
The input and output data by the network will remain unchanged and model re-training will be conducted manually, not automatically.
Exo shall communicate Al Platform 2.0 software changes, performance changes, and labeling changes to end-users via customer and software update notifications.
The benefits and risks of each of the above modifications, independently and combined, including risks of social harm have been assessed, and the overall collective impact of implementing modifications remains low.
The activities proposed within the Modification Protocol will continue to reasonably ensure the safety and effectiveness of the device, and all modifications will maintain the Al Platform device within its stated intended use and indications for use.
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.
The clinical performance of the Al Platform 2.0 product was successfully evaluated on test data encompassing diverse demographic variables, including gender, age and ethnicity from multiple clinical sites in metropolitan cities with diverse racial patient populations.
The A-Line, B-line and LVEF algorithms did not change during the development of the Al Platform 2.0 product and remain as per the cleared algorithms in K232501.
The activities proposed within the Modification Protocol will continue to reasonably ensure the safety and effectiveness of the product, and all modifications will maintain the Al Platform device within its stated intended use and indications for use.
§ 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).