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 invidual frame in that video, that the overall video is most-likely to be “PLAX Standard” perspective of the heart.
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 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.
The package contains two folders:
modelsfolder with the source models in ONNX format, and .engine files for the TensorRT models optimized for the Clara Developer Kits.
videofolder with the recorded video data, both in
avi(source) and converted to the NVIDIA GXF tensor format.
/ ├── NVIDIA-Clara-Holoscan-SDK-EULA.pdf ├── NVIDIA Holoscan Software License (3Oct2022).docx ├── logo.txt ├── models │ ├── aortic_stenosis.onnx │ ├── b_mode_perspective.onnx │ ├── plax_chamber.onnx │ └── engine │ ├── icardio_aortic_stenosis.QuadroRTX6000.7.5.72.trt.engine.fp32 │ ├── icardio_bmode_perspective.QuadroRTX6000.7.5.72.trt.engine.fp32 │ └── icardio_plax_chamber.QuadroRTX6000.7.5.72.trt.engine.fp32 └── video ├── icardio_input1.avi ├── tensor.gxf_entities └── tensor.gxf_index
Refer to the NVIDIA Holoscan Software license file supplied within for use of the models and data, and to the Holoscan SDK EULA.