Anumana, Inc.

Nference, Inc.

Suthirth Vaidya

Abhijith Chunduru

Rakesh Barve

Arjun Puranik

Sai Saketh Chennamsetty

Uddeshya Upadhyay

Animesh Agarwal

Murali Aravamudan

Samir Awasthi

Maulik Nanavaty

Rohit Jain

Ashim Prasad

Shashi Kant

Sairam Bade

Shiva Verma

Yash Mishra

Deepak Anand

Dibakar Saha

Harish Kumar B V

Krishna Chaitanya Kaluva

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 and method for generating pseudo-EGM data from ECG data 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 generate EGM model training data, wherein generating the EGM model training data includes receiving the EGM model training data, wherein the EGM model training data includes exemplary ECG data correlated to exemplary EGM data and time synchronizing the exemplary ECG data and the exemplary EGM data, train an EGM machine-learning model using the EGM model training data, receive subject data, wherein the subject data includes subject ECG data and generate subject EGM data as a function of the subject ECG data using the trained EGM machine-learning model.

Apparatus and method for generating pseudo-electrogram (EGM) data from electrocardiogram (ECG) data

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 and method for prediction of pulmonary vein reconnection is disclosed. The apparatus includes an electrocardiogram device, 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 generate ablation evaluation training data, train an ablation evaluation machine-learning model using the ablation evaluation training data, receive, from the electrocardiogram device, the electrocardiogram data, and generate, using the ablation evaluation machine-learning model, ablation evaluation data of the patient, wherein generating the ablation evaluation data of the patient includes inputting, into the ablation evaluation machine-learning model, the electrocardiogram data and receiving as output, from the ablation evaluation machine-learning model, the ablation evaluation data of the patient.

Apparatus and methods for prediction of repeat ablation efficacy

Described herein are systems and methods for signal digitization. A system may include a camera; a network interface device; a user interface; and a computing device configured to, using the camera, capture an image of a signal; determine a signal metric as a function of the image of the signal; and using the user interface, display the signal metric to a user; wherein the system is communicatively connected to a repository of deidentified patient health information.

Systems and methods for signal digitization

An apparatus and method for generating a quality diagnostic of analog ECG (electrocardiogram) data. 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 ECG data, extract a plurality of ECG parameters from the ECG data, convert the ECG data to one or more digitized ECG signals, validate the digitized ECG signals, wherein validating includes classifying the plurality of digitized ECG signals to a plurality of preliminary ECG parameters and determining an accuracy status of the extracted plurality of ECG parameters by comparing the plurality of preliminary ECG parameters to the extracted plurality of ECG parameters, and generate a quality diagnostic of the ECG data based on validation of the digitized ECG signals.

Apparatus and method for generating a quality diagnostic of ECG (electrocardiogram) data

Apparatus for identification of medical features and methods used therein include an ultrasonic imaging system configured to detect at least a query image containing ultrasound data pertaining to a subject, an intervention system configured to perform a concurrent medical procedure as a function of the detection of the at least a query image, a display device, a processor, and a memory communicatively connected to the processor, wherein the memory contains instructions configuring the processor to receive, from the ultrasonic imaging system, the at least a query image, identify the pose of an anatomical structure as a function of the at least a query image, determine at least a medical feature as a function of the pose of the anatomical structure, and suggest, using the display device, a modification to the concurrent medical procedure as a function of the at least a medical feature.

Apparatus and methods for identification of medical features

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

Apparatus for identification of abnormal biomedical features within images of biomedical data and methods used therein are described. The apparatus includes an image capture device, a processor connected to the image capture device, a memory connected to the processor, and a display device connected to the processor. The image capture device is configured to capture an image of biomedical data. The memory contains instructions configuring the processor to receive the image, extract a plurality of biomedical features from the biomedical data, receive repository data from a medical repository as a function of the plurality of biomedical features, generate at least a distance metric as a function of the plurality of biomedical features and the repository data, and highlight at least a biomedical feature within the image as a function of the at least a distance metric.

Apparatus and methods for identifying abnormal biomedical features within images of biomedical data

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.

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Bengaluru, India

Suthirth Vaidya