Abstract: Described herein is an apparatus and method for determining a cardiac implant size. In some embodiments, an apparatus may include an ultrasonic imaging 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, using the ultrasonic imaging device, collect a plurality of ultrasonic images, using a 3D cardiac model generation machine learning model trained on a training dataset comprising example ultrasonic images correlated with example 3D cardiac models, generate a 3D cardiac model based on the plurality of ultrasonic images, generate at least a cardiac measurement based on the 3D cardiac model, determine a cardiac implant size based on the at least a cardiac measurement, and display to a user the cardiac implant size.

Abstract: An apparatus and method for subject monitoring and diagnosing with non-invasive measures are disclosed.

Abstract: A system and method for transesophageal echocardiogram-guided implantation of a stent are disclosed. The system includes at least a transesophageal echocardiogram (TEE) system, at least a display and at least a computing device configured to receive a plurality of ultrasound images, generate at least a three-dimensional (3D) cardiac model as a function of the plurality of ultrasound images, receive at least a 3D stent model, determine a view label for each of the plurality of ultrasound images and display the at least a 3D cardiac model and the at least a 3D stent model as a function of the view label.

Abstract: A system for transesophageal echocardiogram-guided implantation of a valve device, the system including at least a transesophageal echocardiogram system configured to detect at least an ultrasound image and at least a computing device configured to receive at least an ultrasound image, generate at least a 3D cardiac model representative of a heart of the patient as a function of at least an ultrasound image wherein the at least an ultrasound image includes a two-dimensional image of the heart of the patient and wherein the 3D cardiac model includes a three dimensional interpolation of the two-dimensional image, determine a valve model datum as a function of the 3D cardiac model, receive at least valve model representative of at least a cardiovascular device to be placed within the patient as a function of the valve model datum and display the at least a 3D cardiac model and at least a valve model.

Abstract: A system and method for transesophageal echocardiogram-guided implantation of a left atrial appendage closure device are disclosed. The system includes at least a transesophageal echocardiogram (TEE) system including at least an ultrasound sensor configured to be located within an esophagus of a patient and detect at least an ultrasound image as a function of cardiac tissue of the patient, at least a display and at least a computing device including a memory containing instructions configuring at least a processor to receive the at least an ultrasound image, generate at least a three-dimensional (3D) cardiac model representative of a heart of the patient as a function of the at least an ultrasound image, receive at least a 3D left atrial appendage closure (LAAC) device model representative of at least a LAAC device, generate a superimposed model and display, using the at least a display, the superimposed model.

Abstract: 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.

Abstract: An apparatus for prediction of repeat ablation efficacy, the apparatus including an electrocardiogram device, wherein the electrocardiogram device is configured to detect post-ablation arrhythmic electrocardiogram (ECG) data representative of a post-ablation arrythmia of a patient who has previously undergone an ablation procedure and a processor configured to receive, from the electrocardiogram device, the post-ablation arrhythmic ECG data predict, using a repeat-ablation efficacy machine-learning model, a determination of a pulmonary vein reconnection by identifying features within the post-ablation arrhythmic ECG data that are historically representative of pulmonary vein reconnection determinations and transmit, for display, the determination.

Abstract: In some embodiments, an apparatus for generating a three-dimensional (3D) model with an overlay may include 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 set of ultrasonic images of a structure; generate a set of shape parameters representing the structure's shape as a function of the set of ultrasonic images and a shape identification model trained on a training dataset comprising historical ultrasonic images correlated with historical computed tomography scan data; generate a 3D model of the structure based on the set of shape parameters; generate a map by determining a level of uncertainty at each location of a plurality of locations on the 3D model; and overlay the map onto the 3D model.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: An apparatus and method for determining a tissue susceptibility value from medical data. The apparatus includes an imaging device, wherein the imaging device is configured to detect an image of a subject, wherein the subject receives tissue ablation, a memory, and at least a processor communicatively connected to the memory, wherein the memory contains instructions configuring the at least a processor to receive the image of the subject, generate a tissue susceptibility prediction model, wherein the tissue susceptibility prediction model is configured to receive, as input, the image of the subject, and determine a tissue susceptibility value as a function of the image, wherein the tissue susceptibility value defines a susceptibility of a tissue to the tissue ablation, display, using a downstream device, the tissue susceptibility value through a graphical user interface.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: An apparatus and method for classifying a user to a cohort of retrospective users is disclosed. The apparatus includes at least a processor and a computer-readable storage medium communicatively connected to the at least a processor, wherein the computer-readable storage medium contains instructions configuring the at least processor to receive user data of a user, generate a vector embedding of the user data, generate a query input, generate a plurality of cohorts of retrospective users using cohort data extracted from a cohort database based on the query input, wherein generating the plurality of cohorts includes generating a set of vector embeddings of the cohort data, classify, based on the vector embedding and the set of vector embeddings, the user data to at least a cohort of the plurality of cohorts of the retrospective users, and output the at least a cohort through a user interface.

Abstract: 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.

Abstract: 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.