Holoscan Sample App Data for Multi-AI Ultrasound Pipeline

This resource contains three ultrasound machine learning models developed by iCardio.ai to be used concurrently in the Multi-AI Inference pipeline released in the Holoscan SDK v0.4, as well as a sample video.

Models

Note: The provided models are in ONNX format. They will automatically be converted into TensorRT models (.engine) the first time they're processed by a Holoscan application.

Viewpoint Classifier

In clinical practice, echocardiograms are produced by placing a transducer at various points along a patient's body at different axis of rotation and degrees of skew. Used around the world, this standard procedure of generating an ultrasound examination produces over 20 \"views\" of the heart. Each view corresponds to various angles of the heart and contains expected cardiac anatomical components. Using this model, we can automatically determine the class of the given view, and therefore also determine the cardiac anatomical components contained therein. The determination of the view is the first step in upstream processing for both humans and machines.

The model output: The model output produces a confidence for each frame pertaining to 1 of 28 cardiac views as defined by the guidelines of the American Society of Echocardiography. The confidence of the most prominent class for each frame is averaged over the length of the video to arrive at a definitive perspective classification. For instance, a confidence of 100% for “PLAX Standard” suggests the model computes, based on each individual frame in that video, that the overall video is most-likely to be “PLAX Standard” perspective of the heart.

PLAX Chambers

This model identifies four crucial linear measurements of the heart. From top to bottom, the chamber model automatically generates an estimated caliper placement to measure the diameter of the Right Ventricle, the thickness of the Interventricular Septum, the diameter of the Left Ventricle, and the thickness of the Posterior Wall. These measurements are crucial in diagnosing the most common cardiac abnormalities or diseases. For instance, if it is determined that the diameter of the Left Ventricle is bigger than expected for that patient (after considering for gender and patient constitution), this can be a telling sign of diastolic dysfunction, or moreover, various forms of heart failure.

The model output: This model interprets the most likely pixel location of each class. The distance between the pixels can be assumed to be the length of the underlying anatomical component. Illustrating the distance between the points we can see the model calculate the Left and Right Ventricular chamber sizes through the extent of the cardiac cycle.

Aortic Stenosis

Aortic Stenosis is a well-studied heart disease affecting the function of the aortic valve. Aortic Stenosis can affect the flow of blood from the Left Ventricle into the rest of the body. A patient with severe aortic stenosis may suffer from cardiac dysfunction and therefore early detection of this disease is crucial. Aortic stenosis has been known to be underdiagnosed, resulting in the suboptimal treatment of many patients. The challenge is, determining aortic stenosis can involve the measurement of multiple parameters making the diagnosis potentially very tricky. Unlike traditional diagnosis of Aortic Stenosis, this innovative model provides a propensity for the presence of aortic stenosis directly from standard ultrasound images, making it amenable in the use of a variety of real-world settings.

Video Data

The sample data is an .avi video that consists of echocardiogram frames with features identifiable by the three models above.

Note: the .avi file must be converted into a GXF tensor file using the convert_video_to_gxf_entities.py script on GitHub to be used with the VideoStreamReplayer Holoscan operator.

License

Refer to the NVIDIA Holoscan Software license file supplied within for use of the models and data, and to the Holoscan SDK EULA.

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They will automatically be converted into TensorRT models (.engine) the first time they're processed by a Holoscan application._\n\n### Viewpoint Classifier\n\nIn clinical practice, echocardiograms are produced by placing a transducer at various points along a patient's body at different axis of rotation and degrees of skew. Used around the world, this standard procedure of generating an ultrasound examination produces over 20 \"views\" of the heart. Each view corresponds to various angles of the heart and contains expected cardiac anatomical components. Using this model, we can automatically determine the class of the given view, and therefore also determine the cardiac anatomical components contained therein. The determination of the view is the first step in upstream processing for both humans and machines.\n\nThe model output: The model output produces a confidence for each frame pertaining to 1 of 28 cardiac views as defined by the guidelines of the American Society of Echocardiography. The confidence of the most prominent class for each frame is averaged over the length of the video to arrive at a definitive perspective classification. For instance, a confidence of 100% for “PLAX Standard” suggests the model computes, based on each individual frame in that video, that the overall video is most-likely to be “PLAX Standard” perspective of the heart.\n\n### PLAX Chambers\n\nThis model identifies four crucial linear measurements of the heart. From top to bottom, the chamber model automatically generates an estimated caliper placement to measure the diameter of the Right Ventricle, the thickness of the Interventricular Septum, the diameter of the Left Ventricle, and the thickness of the Posterior Wall. These measurements are crucial in diagnosing the most common cardiac abnormalities or diseases. For instance, if it is determined that the diameter of the Left Ventricle is bigger than expected for that patient (after considering for gender and patient constitution), this can be a telling sign of diastolic dysfunction, or moreover, various forms of heart failure.\n\nThe model output: This model interprets the most likely pixel location of each class. The distance between the pixels can be assumed to be the length of the underlying anatomical component. Illustrating the distance between the points we can see the model calculate the Left and Right Ventricular chamber sizes through the extent of the cardiac cycle.\n\n### Aortic Stenosis\n\nAortic Stenosis is a well-studied heart disease affecting the function of the aortic valve. Aortic Stenosis can affect the flow of blood from the Left Ventricle into the rest of the body. A patient with severe aortic stenosis may suffer from cardiac dysfunction and therefore early detection of this disease is crucial. Aortic stenosis has been known to be underdiagnosed, resulting in the suboptimal treatment of many patients. The challenge is, determining aortic stenosis can involve the measurement of multiple parameters making the diagnosis potentially very tricky. Unlike traditional diagnosis of Aortic Stenosis, this innovative model provides a propensity for the presence of aortic stenosis directly from standard ultrasound images, making it amenable in the use of a variety of real-world settings.\n\n## Video Data\n\nThe sample data is an .avi video that consists of echocardiogram frames with features identifiable by the three models above.\n\n_Note: the .avi file must be converted into a GXF tensor file using the `convert_video_to_gxf_entities.py` script on [GitHub](https://github.com/nvidia-holoscan/holoscan-sdk/tree/main/scripts#convert_video_to_gxf_entitiespy) to be used with the `VideoStreamReplayer` Holoscan operator._\n\n## License\n\nRefer to the NVIDIA Holoscan Software license file supplied within for use of the models and data, and to the [Holoscan SDK EULA](https://developer.nvidia.com/clara-holoscan-EULA).","displayName","Multi-AI Sample App Data",["D",1681919702465],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/clara-holoscan/resources/holoscan_multi_ai_ultrasound_sample_data","rawImageUrl","canGuestDownload","latestVersionSizeInBytes",607267935,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[346,347,348,349,350,351],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_314":375,"_353":376,"_355":377,"_357":358},{"_314":372,"_353":373,"_355":374,"_357":358},{"_314":369,"_353":370,"_355":371,"_357":358},{"_314":365,"_353":366,"_355":367,"_357":358},{"_314":361,"_353":362,"_355":363,"_357":358},{"_314":352,"_353":354,"_355":356,"_357":358},"ML","value","soln_ml","filter",{"_359":360,"_353":354},"category","label","field","technology","Holoscan","pltfm_clara_holoscan",{"_359":364,"_353":362},"platform","Healthcare","indus_healthcare",{"_359":368,"_353":366},"industry","DL","soln_dl",{"_359":360,"_353":370},"Clara","pltfm_clara",{"_359":364,"_353":373},"AI","soln_ai",{"_359":360,"_353":376},{"_212":1551,"_214":1552},"ngcApps",[1504],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1486":1487,"_1488":1489,"_1490":1491,"_1492":1493,"_1494":389,"_239":413,"_1495":1496,"_1484":56,"_1497":191,"_1498":191,"_1499":1500,"_1501":191,"_1502":1503},"amplitude",{"_1482":1483,"_1484":56,"_1085":-7,"_1485":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBZmFsc2UlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1476":1477,"_1478":1479,"_1480":1481},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.4.0","defaultSearchSuggestions",{"_1360":283,"_1361":1362,"_1363":1364,"_1365":1366},"announcements",[],"consentState",{"_451":1350,"_1351":191,"_1352":31,"_1353":31,"_1354":1355},"consentConfig",{"_433":434,"_435":436,"_437":438,"_439":440,"_441":442,"_443":444,"_445":446,"_447":448,"_449":450,"_451":452},"showContactAdmin",1783310667248,[],[],[427,428,429,430,431,432],{"_45":60,"_47":71,"_49":207,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":69,"_49":207,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":67,"_49":207,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":61,"_49":207,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":207,"_51":-5,"_53":-5,"_55":56},{"_45":46,"_47":48,"_49":207,"_51":-5,"_53":-5,"_55":56},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",[724,725,726,727,728,729,730,731,732,733,734,735,736,737,738,739,740,741,742,743,744],[689,690,691,692,693],{"_492":584,"_314":584,"_268":283,"_465":715},{"_492":550,"_314":550,"_268":283,"_465":710},{"_492":703,"_314":703,"_268":283,"_465":704},{"_492":699,"_314":699,"_268":283,"_465":700},{"_492":694,"_314":694,"_268":283,"_465":695},"nvcrm.my.site.com",[696],{"_83":697,"_498":694,"_499":31,"_268":698},"BrowserId","If you know anything about this cookie and its uses that are not specific to a particular site please add your contributions.","nvidia.partners",[701],{"_83":702,"_498":699,"_499":31,"_268":533},"edge","github.com",[705,706],{"_83":709,"_498":703,"_499":31,"_268":708},{"_83":707,"_498":703,"_499":31,"_268":708},"logged_in","This domain is owned by Github. 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It is not used to track users but rather to limit the number of requests that have to be made to doubleclick.net.","coveo_visitorId","This cookie name is associated with Coveo which is used to store a unique user ID for marketing purposes and to track the number of distinct users who submitted a query or clicked an item, counting them only once even if they did the same query or clicked the same item over the course of many visits.","_dd_s","Datadog Cookie used to group all events generated from a unique user session across multiple pages. It contains the current session ID, whether the session is excluded due to sampling, and the expiration date of the session. The cookie is extended for an extra 15 minutes every time the user interacts with the website, up to the maximum user session duration (4 hours).","__sec_tid","__sec__fid","crowdsource.nvidia.com","This cookie name is associated with 6sense which is used to collect visitor data related to the user’s visit to the website, such as number of visits, average time spent on the website and which pages have been loaded, for the purpose of serving targeted advertisements.This cookie contains no identifying information and is used for diagnostic purposes by Impact Radius, a service we use to track signup commissions for our affiliate program. We have categorized it under Functionality because it is necessary for the operation of our affiliate program.","__sec__ghost","_gd_svisitor","info.nvidia.com","nvweb_A","_ga","This cookie name is associated with Google Universal Analytics - which is a significant update to Google's more commonly used analytics service. This cookie is used to distinguish unique users by assigning a randomly generated number as a client identifier. It is included in each page request in a site and used to calculate visitor, session and campaign data for the sites analytics reports. By default it is set to expire after 2 years, although this is customisable by website owners.","research.nvidia.com","_uetvid","This is a cookie utilised by Microsoft Bing Ads and is a tracking cookie. It allows us to engage with a user that has previously visited our website.","s_pltp","This cookie is typically associated with Adobe Analytics (formerly SiteCatalyst). It is used to identify the previous link clicked within the site, which helps in analyzing site usage patterns.","s_ecid","Adobe Analytics cookie containing the the Adobe Experience Cloud 1st party visitor id if the Analytics AMCV visitor id is not found.","This cookie name is used as a long-lived unique identifier of the visitor on the website. It is either correlated to the 6suuid or a unique value, and can be changed during the lifetime of this cookie.This cookie contains no identifying information and is used for diagnostic purposes by Impact Radius, a service we use to track signup commissions for our affiliate program. We have categorized it under Functionality because it is necessary for the operation of our affiliate program.","s_ips","This cookie is associated with Adobe Analytics. The cookie is used for measuring a visitor’s scroll activity to see how much of a page they view before moving on to another page.","_gd_session","tp","This cookie is associated with Adobe Analytics site tracking. It registers if the website's scroll-depth detection is active. This function remembers how far the user has scrolled on the current session across the website's sub-pages.","dmn_chk_-xxxxxxxxxxxx","com","Posthog Analytics","_hly_vid","This cookie _hly_vid is used to record website and server performance with each visit to the site. The data is aggregated and analyzed; therefore, it remains anonymous upon use.","_cs_id","blogs.nvidia.com","_session_id","resources.nvidia.com","This cookie creates an interim session ID used as an in-session user ID.\n","s_cc","This cookie is associated with the Adobe Site Catalyst. It determines whether cookies are enabled in the web browser.","_hjSessionUser_xxxxxx","Hotjar cookie that is set when a user first lands on a page with the Hotjar script. It is used to persist the Hotjar User ID, unique to that site on the browser. 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