Anumana, Inc.

Nference, Inc.

Abhijith Chunduru

Suthirth Vaidya

Arjun Puranik

Sai Saketh Chennamsetty

Uddeshya Upadhyay

Rakesh Barve

Murali Aravamudan

Animesh Agarwal

Rohit Jain

Samir Awasthi

Deepak Anand

Dibakar Saha

Maulik Nanavaty

Krishna Chaitanya Kaluva

Harish Kumar B V

Suthirth Vaidya Subramany

A system and method for generating canonical views for volumetric imaging data are disclosed. The system includes at least a processor and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to receive volumetric imaging data, segment the at least one organ structure of the volumetric imaging data into a plurality of organ components, compute, for each organ component, one or more eigenvectors and corresponding eigenvalues, divide each organ component into a plurality of three-dimensional octants, receive a user input, wherein the user input includes a selection of a reference field of view, determine which of the plurality of three-dimensional octants are intersected by the reference field of view and output the volumetric imaging data with the canonical view.

System and method for generating canonical views for volumetric imaging data

An apparatus and method for generating a three-dimensional (3D) model of cardiac anatomy including an overlay. The apparatus includes at least a processor configured receive a set of images of a cardiac anatomy pertaining to a subject, generate a set of shape parameters based on the set of images, wherein generating the set of shape parameters includes receiving cardiac geometry training data including a plurality of image sets as input correlated to a plurality of shape parameter sets as output, training a shape identification model using the cardiac geometry training data, and generating the set of shape parameters using the shape identification model, generate a 3D model of the cardiac anatomy based on the set of shape parameters, generate a map by determine a level of uncertainty at each location of a plurality of locations on the generated 3D model, and overlay the map onto the 3D model.

Apparatus and method for generating a three-dimensional (3D) model of cardiac anatomy with an overlay

Apparatus for visualization within a three-dimensional (3D) model and methods used therein are described, wherein the apparatus includes a processor and a memory communicatively connected to the processor, wherein the memory includes instructions configuring the processor to receive a query image, extract neural network encodings from the received query image, query a synthetic image repository for at least a matching synthetic image, and display an estimated position and orientation within the 3D model, wherein the synthetic image repository includes a plurality of synthetic images and their extracted neural network encodings, each synthetic image therein corresponds to a slice extracted at a specific position and orientation in the 3D model, and querying the synthetic image repository includes comparing the extracted neural network encodings between the query image and synthetic images.

Apparatus and methods for visualization within a three-dimensional model using neural networks

Apparatus and method for object pose estimation are disclosed. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to receive a plurality of sets of echo data, wherein the plurality of sets of echo data is configured for generation of a plurality of echo depth maps, segment the plurality of echo depth maps, determine a depth datum related to pixels of an object of interest as a function of the plurality of segmented echo depth maps, generate a three dimensional (3D) point cloud related to the object of interest as a function of the depth datum and generate a pose datum of the object of interest as a function of the 3D point cloud.

Apparatus and method for object pose estimation in a medical image

An apparatus for generating a three-dimensional (3D) model of cardiac anatomy via machine-learning, wherein the apparatus includes a process and a memory containing instructions configuring the processor to receive a set of images of a cardiac anatomy pertaining to a subject, generate an 3D data structure representing the cardiac anatomy as a function of the set of images using a cardiac anatomy modeling model, generate an initial 3D model of the cardiac anatomy, refine the generated initial 3D model of the cardiac anatomy as a function of the 3D data structure representing the cardiac anatomy, and generate a subsequent 3D model of the cardiac anatomy as a function of the refinement.

Apparatus and methods for generating a three-dimensional (3D) model of cardiac anatomy via machine-learning

An apparatus for synthetizing medical images, wherein the apparatus includes a process and a memory containing instructions configuring the processor to receive a heart model related to a patient's heart, identify a region of interest within the heart model, wherein identifying the region of interest includes locating at least a point of view on the heart model and determining a view angle corresponding to the at least a point of view, wherein the at least a point of view and the corresponding view angle define at least one field of view that include at least a portion of the heart model, and generate at least a medical image as a function of the region of interest using an image generator, wherein the at least a medical image captures an anatomical structure of the at least a portion of the heart model.

Apparatus and methods for synthetizing medical images

Apparatus for generating electro-anatomical mapping and methods used therein include a processor and a memory connected to the processor, wherein the memory contains instructions configuring the processor to receive input data, generate, using at least a machine learning model, an electro-anatomical mapping as a function of the input data, and display the electro-anatomical mapping using a user interface, wherein receiving the input data includes receiving, from an imaging device, at least a medical image and receiving, from a signal capturing device, at least an electrogram, wherein the at least a machine learning model is trained using electro-anatomical mapping training data including exemplary medical images and exemplary electrograms as input correlated to exemplary electro-anatomical mappings as output.

Apparatus and methods for generating electro-anatomical mapping

Described herein are systems and methods for responding to user input using an agent orchestrator. A system may include a cardiac catheter, a user interface, and a computing device configured to receive, from the user interface, a first user input, receive, from the catheter, the procedure data, determine, using an agent orchestrator, a first agent selection datum, wherein determining the first agent selection datum includes generating the first agent selection datum as a function of the first user input using a trained agent selection machine learning model, using a first agent corresponding to the first agent selection datum, determine a first agent output, wherein determining the first agent output includes inputting into the first agent the procedure data, and receiving, as an output from the first agent, the first agent output, and display, using the user interface, the first agent output.

System and method for responding to a user input using an agent orchestrator

Apparatus and method for generating a three-dimensional (3D) model of cardiac anatomy based on model uncertainty

An apparatus and method for locating a position of an electrode on an organ model. The apparatus includes a memory communicatively connected to at least a processor, wherein the memory contains instructions configuring the at least a processor to receive an organ model configured to digitally represent an organ, receive a set of sensor data from at least a sensor including an ultrasound sensor, determine an electrode position within the organ model as a function of the set of sensor data using a position machine-learning module, wherein determining the electrode position includes determining a model position within the organ model as a function of the set of sensor data and determining the electrode position within the model position of the organ model as a function of the set of sensor data and add a visual marker onto the electrode position in the model position of the organ model.

Apparatus and method for locating a position of an electrode on an organ model

An apparatus and method for de-identification of medical images including at least a processor and a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to receive a series of images, comprising metadata, and a plurality of image slices; select a sampling strategy as a function of the metadata, wherein the sampling strategy identifies a subset of image slices of the plurality of image slices to sample; apply a text classifier to the subset of image slices, wherein the text classifier is configured to identify a presence of textual information on the subset of image slices; determine at least one relationship between the subset of images slices including providing an output as a function of the presence of the textual information, the metadata, and a position of each image slice.

Apparatus for and method of de-identification of medical images

Apparatus for visualization within a three-dimensional (3D) model and methods used therein are described, wherein the apparatus includes a processor and a memory communicatively connected to the processor, wherein the memory includes instructions configuring the processor to receive a query image, extract neural network encodings from the received query image, query a synthetic image repository for at least a matching synthetic image, and display an estimated region of interest within the 3D model, wherein the synthetic image repository includes a plurality of synthetic images and their extracted neural network encodings, each synthetic image therein corresponds to a region of interest in the 3D model, and querying the synthetic image repository includes comparing the extracted neural network encodings between the query image and synthetic images.

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Bengaluru, India

Abhijith Chunduru