Veveo, Inc.

Anumana, Inc.

Nference, Inc.

Adeia Guides Inc.

Rovi Guides, Inc.

Mayo Foundation for Medical Education and Research

Rakesh Barve

Murali Aravamudan

Sashikumar Venkataraman

Ajit Rajasekharan

Pankaj Garg

Kajamalai Gopalaswamy Ramakrishnan

Vineet Agarwal

Suthirth Vaidya

Girish S Welling

K China Srinivasulu Reddy

Sankar Ardhanari

Abhijith Chunduru

Ahmed Nizam Mohaideen Pathurudeen

Ashim Prasad

Ganesh Ramamoorthy

Animesh Agarwal

Uddeshya Upadhyay

Shashi Kant

Arjun Puranik

Pranav Rajanala

Venkataramanan Soundararajan

Keyur Faldu

Sai Saketh Chennamsetty

Apurv Anand

Tyler Wagner

Samir Awasthi

Sairam Bade

Abhijit Satchidanand Savarkar

Mangesh Dattatray Pujari

Akash Anand

Soumyadip Majumder

Manish Mohan Sharma

Yash Mishra

Melwin Babu

Shamim A Naqvi

Karthik Murugadoss

Deepak Anand

Yash Gupta

Suthirth Vaidya Subramany

Rahul Agrawal

Daren Gill

Rohit Jain

Karthik K Bharadwaj

Shiva Verma

Yogisha Heggadahalli Jayendra

Corinne Carpenter

Katherine Carlson

Maulik Nanavaty

Sughosh Indurkar

Sravan Kumar Lalam

Praveen Kumar M

Paul A Friedman

Sidharth Abrol

Praveen Kumar

Itzhak Zachi Attia

Akshat Agarwal

Aravind Srinivasan

Mayank Sharma

Ravi Krishna

Peter A Noseworthy

Mohan Krishna Ranganathan

Durgaprasad Dodle

Purushottam Sinha

Shahir Asfahan

Wui Ip

Hari Krishna Kunderu

Harish Kumar B V

Kirnesh Nandan

Shayan Ghosh

Kajamalai G Ramakrishnan

Michiel Jm Niesen

Venkataraman Soundarajan

John Rincón-Hekking

Joerg Herrmann

Keyur Vallabhbhai Faldu

Shashank Jaiswal

Shiv Pratap Singh

Girish Welling

A system for sweep trajectory guidance may include a catheter coupled to at least a transducer, wherein the at least a transducer is configured to receive ultrasound data, at least a localization system configured to detect probe position data as a function of a location of the catheter, at least a processor communicatively connected to the transducer, and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to receive, by the at least a processor, the ultrasound data and the probe position data, generate, using a trained sweep trajectory simulation model, a movement vector as a function of the ultrasound data and the probe position data, determine, using the movement vector, real-time guidance data, and display the real-time guidance data to a user through at least a user interface.

System and method for sweep trajectory guidance

An apparatus and method for generating output data as a function of digital image data and an analysis module. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor. The memory instructs the processor to present a graphical user interface comprising medical time-series data, receive a capture event as a function of a first user command, generate, as a function of the capture event, digital image data, wherein the digital image data comprises a window of the graphical user interface, verify, using user confirmation, the window of the graphical user interface, process, using one or more analysis models, the digital image data, wherein processing the digital image data comprises extracting at least a cardiac metric from the digital image data, and generate, using the analysis module, output data based on the at least a cardiac metric extracted from the digital image data.

Apparatus and method for generating output data as a function of digital image data and an analysis module

A system for tracking cardiac values including at least a processor configured to receive electrocardiogram (ECG) input data associated with a patient wherein the ECG input data includes ECG signals, input the ECG input data into one or more cardiac panels, wherein each cardiac panel of the one or more cardiac panels is configured to calculate a cardiac value associated with a heart condition and each cardiac panel comprises at least one ECG machine-learning model configured to receive ECG input data and output cardiac values, generate the cardiac values from the cardiac panels as a function of the ECG input data wherein at least one cardiac value of the one or more cardiac values includes a probability of the patient satisfying at least one ejection fraction level threshold and the one or more ECG machine-learning models and display the cardiac values through a graphical user interface.

Systems and methods for tracking cardiac values

An apparatus and method for automatically extracting canonical views from ultrasound imaging data. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor. The memory instructs the processor to receive the ultrasound data from the at least a transducer, classify, using an extractor, a plurality of canonical views, wherein classifying the plurality of canonical views comprises identifying a plurality of initial canonical views, generating a plurality of comparisons of the plurality of initial canonical views to a plurality of reference canonical views, and classifying the plurality of initial canonical views as matching canonical views and non-matching canonical views as a function of the plurality of comparisons, extract a matched canonical view, from the initial canonical views that are classified as matching canonical views as a function of a similarity metric, and display the matched canonical view.

Apparatus and method for automatically extracting canonical views from ultrasound imaging data

An apparatus and method for static image of time series measured data to time series translation is disclosed. The apparatus comprises at least a processor configured to receive a static image of time series measured data, convert that static image 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 time series data format conversion and analysis

An apparatus and method for training a machine learning model to augment signal data and image data. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor. The memory instructs the processor to receive a signal data. The memory instructs the processor to generate a digital image, wherein the digital image comprises the signal data. The memory instructs the processor to transmit the digital image to an image processing module, wherein the image processing module produces an augmented image. The memory instructs the processor to transmit the signal data to a signal processing module, wherein the signal processing module produces the augmented image. The memory instructs the processor to train a machine learning model using the augmented image.

Apparatus and method for training a machine learning model to augment signal data and image data

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

A system for training multimodal self-supervised models, the system including a computing device configured to receive a multimodal dataset encode each data entry of a plurality of data entries into a single data format to create an encoded multimodal dataset including one or more encoded modalities, identify one or more embedded pairs as a function of the one or more encoded modalities, wherein each embedded pair of the one or more embedded pairs is associated with a separate patient of the plurality of patients, map the embedded pairs within a shared embedding space, pretrain an ECG machine learning model as a function of the mapping by adjusting one or more parameter values of the ECG machine learning model, receive labeled training data and train the pretrained ECG machine learning model as a function of the labeled training data.

Systems and methods for training multimodal self-supervised models

An apparatus and method for optimizing an operation using a deviation are disclosed. 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 a plurality of operation notes associated with a plurality of operators and a plurality of operations, label the plurality of operation notes to at least an operation label, identify at least an operation deviation, using a language processing module, in a portion of the plurality of operation notes having the at least a matching operation label as a function of at least an operation factor, determine a relevance datum of the at least an operation deviation, determine an effectiveness datum of the at least an operation deviation and generate modified operation data as a function of the effectiveness datum and the relevance datum of the at least an operation deviation.

Apparatus and method for optimizing an operation using a deviation

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

A system and method of processing a search request is provided. Identification of a desired content item is based on comparing a topic of the search request to previous user input. The method includes providing access to a set of content items with metadata that describes the corresponding content items and providing information about previous searches. The method further includes receiving a present input from the user and determining a relatedness measure between the information about the previous searches and an element of the present input. If the relatedness measure is high, the method also includes selecting a subset of content items based on comparing the present input and information about the previous searches with the metadata that describes the subset of content items. Otherwise, the method includes selecting a subset of content items based on comparing the present input with the metadata that describes the subset of content items.

Method for adaptive conversation state management with filtering operators applied dynamically as part of a conversational interface

Apparatus and method for data analysis for unmet need identification, solution distillation and data structure generation are disclosed. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor. The memory contains instructions configuring the at least a processor to receive a plurality of sets of note data, determine a plurality of operational language elements from the plurality of sets of note data, wherein the plurality of operational language elements includes an unmet need language element, generate an element control datum as a function of the unmet need language element of the plurality of operational language elements, classify the plurality of operational language elements into one or more element groups, generate a note data structure as a function of the one or more element groups and the element control datum, and display the note data structure using a graphical user interface.

Apparatus and method for note data analysis to identify unmet needs and generation of data structures

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

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Bengaluru, India

Rakesh Barve