Methods for identifying indications for testing for Lyme related infection are described. A subject, presenting a sudden onset of at least one visual symptom known to be associated with Lyme related infection, may be screened for peri-papillary ischemia and, if peri-papillary ischemia is present, the subject may be recommended to undergo further testing for Lyme related infection.

Systems and methods are disclosed for assessing the quality of medical images of at least a portion of a patient's anatomy, using a computer system. One method includes receiving one or more images of at least a portion of the patient's anatomy; determining, using a processor of the computer system, one or more image properties of the received images; performing, using a processor of the computer system, anatomic localization or modeling of at least a portion of the patient's anatomy based on the received images; obtaining an identification of one or more image characteristics associated with an anatomic feature of the patient's anatomy based on the anatomic localization or modeling; and calculating, using a processor of the computer system, an image quality score based on the one or more image properties and the one or more image characteristics.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a respective response score for each of a plurality of patient categories. In one aspect, a method comprises: generating a drug signature for a drug; generating an embedding of the drug signature in a latent space; and processing: (i) the embedding of the drug signature in the latent space, and (ii) data defining a plurality of patient categories, to generate a plurality of response scores, wherein each response score corresponds to a respective patient category and characterizes a predicted response of patients included in the patient category to the drug.

An electronic device acquires pulse wave information indicating a pulse wave of a first portion of a body and pulse wave information indicating a pulse wave of a second portion of the body, based on video information of the body in each of a first video and a second video obtained by imaging the body. The device further acquires, based on a relationship between the pulse wave information of the first portion and the pulse wave information of the second portion acquired from the first video, and a relationship between the pulse wave information of the first portion and the pulse wave information of the second portion acquired from the second video, a measurement result indicating a degree of change in blood flow from when imaging the first video to when imaging the second video.

An ischemic stroke mimic is detected, or otherwise predicted, based on medical images acquired from a subject. Medical image data, which include medical images acquired from a head of the subject, are accessed with a computer system. A machine learning model (e.g., one or more deep convolutional neural networks) is trained on training data to estimate a probability of an acute intracranial abnormality being depicted in a medical image. Intracranial abnormality prediction data are generated by inputting the medical image data to the machine learning model. The intracranial abnormality prediction data include an intracranial abnormality probability score for each of the medical images in the medical image data. An ischemic stroke mimic classification for the medical image data is generated based on the intracranial abnormality prediction data, and may be displayed to a user with the computer system.

Provided herein are systems and methods for automated identification of volumes of interest in volumetric brain images using artificial intelligence (AI) enhanced imaging to diagnose and treat acute stroke. The methods can include receiving image data of a brain having header data and voxel values that represent an interruption in blood supply of the brain when imaged, extracting the header data from the image data, populating an array of cells with the voxel values, applying a segmenting analysis to the array to generate a segmented array, applying a morphological neighborhood analysis to the segmented array to generate a features relationship array, where the features relationship array includes features of interest in the brain indicative of stroke, identifying three-dimensional (3D) connected volumes of interest in the features relationship array, and generating output, for display at a user device, indicating the identified 3D volumes of interest.

Systems, methods, and devices for fluorescence imaging with increased dynamic range are disclosed. A system includes an emitter for emitting pulses of electromagnetic radiation and an image sensor comprising a pixel array for sensing reflected electromagnetic radiation, wherein the pixel array comprises a plurality of pixels each configurable as a short exposure pixel or a long exposure pixel. The system includes a controller comprising a processor in electrical communication with the image sensor and the emitter. The system is such that at least a portion of the pulses of electromagnetic radiation emitted by the emitter comprises one or more of electromagnetic radiation having a wavelength from about 770 nm to about 790 nm or electromagnetic radiation having a wavelength from about 795 nm to about 815 nm.

The present invention relates to a computer implemented method for estimating lung perfusion from CT images, comprising the steps of: providing a CT image of at least a part of the lung, in particular a CT scan taken at inspiration, and more in particular a non-contrast CT scan taken at inspiration; providing the CT image to a trained computer implemented algorithm to estimate lung perfusion based on the CT image, wherein the trained computer implemented algorithm is trained by providing a set of CT images from at least a part of the lung, in particular a CT scan taken at inspiration, and more in particular a non-contrast CT scan taken at inspiration; providing perfusion information corresponding to the CT image; and training the computer implemented algorithm to learn to estimate perfusion in a CT image based on the reference perfusion information provided during training.

Clinical assessment devices, systems, and methods are provided. A clinical assessment system, comprising a processor in communication with an imaging device, wherein the processor is configured to receive, from the imaging device, a sequence of image frames representative of a contrast agent perfused subjects tissue across a time period; classify the sequence of image frames into a plurality of first tissue classes and a plurality of second tissue classes based on a spatiotemporal correlation among the sequence of image frames by applying a predictive network to the sequence of image frames to produce a probability distribution for the plurality of first tissue classes and the plurality of second tissue classes; and output, to a display in communication with the processor, the probability distribution for the plurality of first tissue classes and the plurality of second tissue classes.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and/or dynamically identify one or more features, such as plaque and vessels, and/or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, perform computational fluid dynamics analysis, facilitate assessment of risk of heart disease and coronary artery disease, enhance drug development, determine a CAD risk factor goal, provide atherosclerosis and vascular morphology characterization, and determine indication of myocardial risk, and/or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and/or quantified parameters.