Anumana, Inc.

Nference, Inc.

Uddeshya Upadhyay

Suthirth Vaidya

Arjun Puranik

Abhijith Chunduru

Sai Saketh Chennamsetty

Rakesh Barve

Ashim Prasad

Sairam Bade

Shashi Kant

Murali Aravamudan

Mayank Sharma

Samir Awasthi

Shiva Verma

Yash Mishra

Yash Gupta

Shayan Ghosh

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

An apparatus and method for unpaired time series to time series translation is disclosed. The apparatus comprises at least a processor configured to receive an automated analysis of a time series, convert that time series from its initial domain to a usable time series within another user-selected domain, then to validate the conversion against a confidence threshold.

Apparatus and method for unpaired time series data translation

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

A system for transforming electrocardiogram images for use in one or more machine learning models, the system including at least a processor configured to receive paired data including a plurality of ECG images correlated to a plurality of standardized images, wherein each ECG image of the plurality of ECG images is correlated with each standardized image of the plurality of standardized images, train an ECG transformation model as a function of the paired data, wherein training the transformation model includes adjusting one or more parameter values of the ECG transformation model as a function of a comparison between at least one predicted standardized image and at least one standardized image of the plurality of standardized images, receive non-conforming data, generate standardized data as a function of the ECG transformation model and the non-conforming data and train an ECG machine learning model as a function of the standardized data.

Systems and methods for transforming electrocardiogram images for use in one or more machine learning models

An apparatus (and/or method) for training machine-learning model to make determinations from mismatched channel signals, wherein the apparatus includes a processor and memory communicatively connected to the processor. The memory containing instructions configuring the processor to receive a first set of signals using a first number of channels, process the first set of signals to simulate a second set of signals from a second number of channels, train a signal conversion model using the first set of signals and the second set of signals, and output a set of converted signals having the first number of channels using the third set of signals and the trained signal conversion model.

Apparatus (and/or method) of training a machine-learning model to generate determinations using mismatched-channel signals

A system for training machine learning models with unlabeled electrocardiogram signals, the system including a memory containing instructions configurating a processor to receive a plurality of electrocardiogram (ECG) data in a textual format, create one or more overlapping temporal patches from the plurality of ECG data, mask at least one temporal patch from the one or more overlapping temporal patches, pretrain an ECG machine learning model to predict the at least one masked temporal patch from the one or more overlapping temporal patches, adjust one or more parameter values of the ECG machine learning model as a function of the at least one predicted masked temporal patch and the at least one masked temporal patch and train the ECG machine learning model as a function of the one or more parameter values and a labeled set of ECG training data.

Systems and methods for training machine learning models using unlabeled electrocardiogram data

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 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

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

Uddeshya Upadhyay