Provided herein are methods of generating optimized models of vascular grafts for subjects in certain embodiments. Methods of treating subjects in need of vascular grafts are also provided. Related systems and computer program products are additionally provided.

Methods, systems, and devices for evaluating function of a heart are provided. The methods can include use of one or more imaging modalities to assess diastolic function. The methods, systems, and devices can employ various features of a cardiac volume curve to assess cardiac function. The methods, systems, and devices can be used to guide diagnosis, therapy, and/or training for sick or well patients. Methods, systems, and devices for assessing likelihood of hospital readmission for a heart failure patient are also provided.

A method of performing 3D vessel tree reconstruction includes providing segmented binary angiography images, applying a distance transform to the images, and generating distance transformed binary angiography images. The set of distance transformed binary angiography images are provided to a trained 3D vessel reconstruction machine learning model capable of reconstructing 3D vessels. The 3D vessel tree reconstruction machine learning model includes a multi-stage convolutional neural network comprising a multi-stage architecture with (i) a vessel centerline stage, and (ii) a radius reconstruction stage. Resultant 3D reconstructed vessel trees may be used in performing clinical assessment of coronary vessel health, and occlusion.

The present disclosure relates to a process that comprises, in at least one embodiment: accessing a camera via an application; prompting a user to induce blanching for a time period of 10 seconds; processing information from the camera to automatically detect one or more fingernails for each of the one or more fingers; accessing video data from the camera; computing a capillary refill time; causing a display screen to display the capillary refill time; and transmitting the capillary refill time to a computing device.

A method for measuring blood flow in an organ of a subject includes a step of administering an iodine-based contrast agent to the subject and acquiring preparatory computed tomography (CT) images of the organ, referred to as preparatory CT images. The distribution of the iodine-based contrast agent is then monitored using bolus tracking based on the preparatory CT images and the contrast injection duration to determine a time of maximum contrast enhancement. At or near this identified time, dual-energy computed tomography (CT) images of the organ are acquired, referred to as dual-energy CT images. A curve fitting function is applied to the bolus tracking and dual-energy CT images to calculate iodine concentration over time. Based on the calculated iodine concentration, the blood flow rate in the organ is quantified. A system implementing the method is also provided.

Systems and methods for establishing a patient's current or future clinical or lab values are provided. A neural network is trained on a dataset of medical images, such as ultrasound images, that are tagged with information concerning the lab values of people who were imaged to produce the medical images. The trained neural network can then be provided with medical images of a patient, and the neural network can then make a determination as to the patient's current or future clinical or lab values.

Methods and systems are disclosed for using medical imaging data to diagnose peripheral arterial disease. In a method, a plurality of artificial intelligence based neural network models are trained on medical imaging data of a large population of anonymous patients after labeling and structuring the data for training and testing purposes. Medical imaging data of a known patient is then processed by the plurality of pre-trained artificial intelligence based neural network models to diagnose peripheral arterial disease. A rule-based algorithm integrates the predictions made by the pre-trained neural network models. An inference engine analyzes the integrated predictions data for the known patient, detects any anomalies in the pixel intensities present in each medical image, and performs volumetric calculations. A report generation engine generates medical reports for the known patient. A visualization tool enables a clinician to display and view the results of the diagnoses superimposed on medical images.

A medical image-processing apparatus of an embodiment includes a processing circuitry. The processing circuitry acquires a plurality of medical images including a predetermined region and having different time phases. The processing circuitry sets a first region of interest and a second region of interest in each of the plurality of medical images. The processing circuitry derives a temporal change in first pixel values, which are pixel values on the first region of interest, on the basis of the first pixel values and derives a temporal change in second pixel values, which are pixel values on the second region of interest, on the basis of the second pixel values. The processing circuitry sets a first time window on the basis of the temporal change in the first pixel values and sets a second time window on the basis of the temporal change in the second pixel values.

The present invention provides a method for detecting tissue hemorrhage with image analysis. A host produces a plurality of hyperspectral image information according to a plurality of reference images. An image extraction unit extracts an input image to the host. The host transforms the input image according to the plurality of hyperspectral image information to produce a hyperspectral input image. The host produces an input image spectrum according to the hyperspectral input image. The host performs a feature operation on the input image spectrum according to a preset cell band corresponding to a surface cell of small intestine for generating a plurality of corresponding feature bands. The host performs at least one convolution operation on the plurality of feature bands according to a plurality of kernels for producing a convolution result. The host matches and compares the convolution result with at least one hyperspectral sample band of at least one hyperspectral sample spectrum of at least one hyperspectral sample image for producing at least one comparison result. Finally the host judges if hemorrhage occurs on the surface layer of small intestine according to the comparison result.

The invention relates to systems and methods for predicting ischemic brain tissue fate from multi-phase CT-angiography. More specifically, systems and methods are provided that enable meaningful prediction of core, penumbra and perfusion from mCTA images using software that has been trained via machine learning to interpret mCTA images.