Abstract: An apparatus and method for detecting a level of cardiovascular disease. 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 voltage-time data, generate at least a feature vector from the voltage-time data by at least a feature model, input the at least feature vector into a cardiovascular classification model, generate at least a disease indication in a subject using the classification model, wherein the disease indication comprises a level of myocarditis, and display the at least a disease indication.

Abstract: System for observing medical conditions and methods used therein include a processor and a memory connected to the processor, wherein the memory contains instructions configuring the processor to receive, from a data repository, a plurality of reference electronic health records and a plurality of reference cardiac data elements, generate medical training data, train at least an observation machine learning model using the generated medical training data, receive a query pertaining to a subject, wherein the query includes at least a query cardiac data element and at least a query electronic health record, and output at least an observation outcome as a function of the query using the at least an observation machine learning model.

Abstract: An apparatus of generating a three-dimensional (3D) model of a patient's organ, comprising a processor and a memory containing instructions configuring the processor to receive a first set of images of a patient's organ, determine a first set of shape parameters as a function of the first set of images, generate a first 3D model of the patient's organ as a function of the first set of shape parameters, calculate a level of uncertainty at each location on the first 3D model of the patient's organ, receive a second set of images of the patient's organ corresponding to a high uncertainty region of the first 3D model, and determine a second set of shape parameters as a function of the first set of images and the second set of images, and generate a second 3D model of the patient's organ as a function of the second set of shape parameters.

Abstract: A system for determining position signals of electrodes using a retrained machine-learning model includes at least a catheter including a plurality of electrodes configured to collect a plurality of potential signals and a magnetic sensor configured to collect magnetic data, and at least a computing device including a memory. The processor receives a first training set, wherein the first training set includes patient-agnostic data; receives a second training set, wherein the second training set includes patient-specific data, trains a mapping machine-learning model using the first training set, retrains the mapping machine-learning model using the second training set, receives at least a first signal, wherein the first signal includes a potential signal of the plurality of potential signal and the magnetic data, and generates, using the retrained machine-learning model, as a function of the at least a first signal, a first position signal for an electrode of the plurality of electrodes.

Abstract: An apparatus for the generation of a medical report is disclosed. The apparatus includes at least processor and a memory communicatively connected to the processor. The memory instructs the processor to receive a user query. The memory instructs the processor to receive a user profile comprising a plurality of medical tests. The memory instructs the processor to generate testing data as a function of the plurality of medical tests using an encoder. The memory instructs the processor to generate textual data that is representative of the testing data using a querying transformer model (Q-former). The memory instructs the processor to generate a medical report as a function of the user query and the textual data using a report large language model (LLM).

Abstract: 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.

Abstract: 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.

Abstract: 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.

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 for generating an electrocardiogram verification set is disclosed. The apparatus includes processor and a memory communicatively connected to the processor. The memory instructs the processor to receive digital ECG data. The memory instructs the processor to convert the digital ECG data into analog ECG data. The memory instructs the processor to generate an ECG validation set as a function of the analog ECG data. The memory instructs the processor to validate a diagnostic machine learning model as a function of the ECG validation set. Validating the diagnostic machine learning model includes iteratively training the diagnostic machine learning model using diagnostic training data. Validating the diagnostic machine learning model includes generating performance data based on the ECG validation set. Validating the diagnostic machine learning model includes accepting the diagnostic machine learning model as a function of a comparison between the performance data and a validation threshold.

Abstract: An apparatus for generating a diagnostic label is disclosed. The apparatus includes at least a processor and memory communicatively connected to the at least a processor. The memory instructs the processor to receive a plurality of electrocardiogram signals and a plurality of electronic health records from a user. The memory instructs the processor to generate a plurality of structured electronic health records using the plurality of electronic health records. The memory instructs the processor to generate a plurality of representations as a function of the plurality of electrocardiogram signals and the plurality of structured electronic health records using a representation machine learning model. The memory instructs the processor to generate a diagnostic label as a function of the plurality of representations. The memory instructs the processor to display the diagnostic label using a display device.

Abstract: 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.

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: An apparatus for generating annotations for electronic records is disclosed. The apparatus includes a processor and a memory containing instructions configuring the processor to receive unstructured data for a plurality of electronic records and generate a plurality of machine learning models (MLMs), wherein each MLM of the plurality of MLMs is trained using a machine learning algorithm. The processor is further configured to generate at least one annotation for the unstructured data using each MLM of the plurality of MLM and structure the unstructured data as structured data as a function of the generated annotations.

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: Disclosed systems, methods, and computer readable media can diagnose a health condition based on patient time series data. For example, a method for diagnosing a health condition based on patient time series data includes identifying a training set of health records comprising a first set of patient time series data, training a neural network using the training set of health records, and executing the trained neural network model to diagnose a health condition based on a second set of patient time series data. In further examples, the first set of patient time series data and the second set of patient time series data can each comprise electrocardiogram data and the health condition can comprise pulmonary hypertension.

Abstract: An apparatus for standardization of electrocardiogram signal images, the apparatus having an imaging device, 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 an overlay image from the imaging device, identify a captured fixed background image and a captured primary image within the overlay image, wherein the captured primary image includes a plurality of electrocardiogram signals, compare the captured fixed background image to one or more image quality thresholds, determine an image quality score of the primary image as a function of the captured fixed background image, and output one or more image modification datum as a function of the image quality score.

Abstract: The present disclosure provides user-interface methods and systems for submitting search requests to search engines and presenting search results therefrom customized using content preferences learned about a user, comprising sending query information to at least two search engines, including a query identifying desired content, and user information, including context information describing the environment in which the query information is being sent, and a user signature representing content preferences learned about the user; receiving at least one set of a search result and auxiliary information from the at least one search engine in response to sending the query information, including information describing attributes of the search result that led to the search result being chosen by the at least one search engine; ordering the at least one search result based at least in part on the auxiliary information; and presenting the ordered search results to the user.

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: An apparatus for synthetizing medical images, wherein the apparatus includes a process and a memory containing instructions configuring the processor to receive an organ model related to a patient's organ, identify a region of interest within the organ model, wherein identifying the region of interest includes locating at least a point of view on the organ 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 organ 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 organ model.