# US 2025/0152145 A1 · Ultrasound detection of clots in the bloodstream > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2025/0152145 A1 - Document ID: US20250152145A1 - Status: Published application - Kind: A1 - Application number: 19/002,387 - Filed: 2024-12-26 - Published: 2025-05-15 - Earliest claimed: 2021-08-04 - Assignees: California Institute of Technology - Applicants: California Institute of Technology - Inventors: Yaser S. ABU-MOSTAFA - Web page: https://patentgrep.com/patent/US20250152145A1 ## CPC - A61B 8/5223: {for extracting a diagnostic or physiological parameter from medical diagnostic data (for algorithms to analyse biomedical images G06T7/0012)} - A61B 8/085: {for locating body or organic structures, e.g. tumours, calculi, blood vessels, nodules} - A61B 8/0891: {for diagnosis of blood vessels} - A61B 8/488: {involving Doppler signals} - A61B 8/5207: {involving processing of raw data to produce diagnostic data, e.g. for generating an image} - A61B 8/06: Measuring blood flow - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) ## Abstract Systems and methods disclosed herein relate to the detection of irregular particles in a blood flow based on a determined relative speed of a particle suspended in a blood flow and/or other properties of a particle suspended in a blood flow including a particle's relative position within a blood vessel and a particles tendency to cluster with other particles suspended in a blood flow. Based on a determined relative speed and/or other relevant factors, the properties of irregular particles may also be measured, including the size, shape, and frequency of irregular particles in a blood flow. Machine learning techniques may be employed to determine patterns for the behavior of irregular particles suspended in a blood flow. These patterns may correspond to particular health risks and conditions. ## Claims ### Claim at position 1 -20. (canceled) ### Claim 21 A method, comprising: capturing, using an ultrasound image sensor, an image sequence of a blood flow in a target region of a blood vessel; measuring, based on the image sequence, a central frequency corresponding to a first Doppler frequency shift of the blood flow viewed with the ultrasound image sensor in the target region of the blood vessel, and a second Doppler frequency shift corresponding to one or more objects suspended within the blood flow viewed with the ultrasound image sensor; determining, based on the central frequency and the second Doppler frequency shift, a differential in speed between the one or more objects suspended within the blood flow and the blood flow; and detecting, based at least on the differential in speed, that the one or more objects correspond to one or more anomalies present in the blood flow. ### Claim 22 (depends on 21) The method of claim 21, wherein: detecting that the one or more objects correspond to the one or more anomalies present in the blood flow comprises: detecting, using a trained machine learning model, based at least on the differential in speed, that the one or more objects correspond to the one or more anomalies present in the blood flow; and the machine learning model is trained using a dataset comprising a plurality of image sequences of sample blood flows and corresponding known values including: differentials in speed between objects suspended within the sample blood flows and the sample blood flows. ### Claim 23 (depends on 21) The method of claim 21, further comprising: measuring, based on the image sequence, a relative position of the one or more objects within a cross section of the blood vessel. ### Claim 24 (depends on 23) The method of claim 23, wherein detecting that the one or more objects correspond to the one or more anomalies present in the blood flow comprises: detecting, based at least on the differential in speed and the relative position, that the one or more objects correspond to the one or more anomalies present in the blood flow. ### Claim 25 (depends on 24) The method of claim 24, wherein: detecting that the one or more objects correspond to the one or more anomalies present in the blood flow comprises: detecting, using a trained machine learning model, based at least on the differential in speed and the relative position, that the one or more objects correspond to the one or more anomalies present in the blood flow; and the machine learning model is trained using a dataset comprising a plurality of image sequences of sample blood flows and corresponding known values including: differentials in speed between objects suspended within the sample blood flows and the sample blood flows, and relative positions of the objects suspended within the sample blood flows. ### Claim 26 (depends on 25) The method of claim 25, wherein the machine learning model is a neural network. ### Claim 27 (depends on 23) The method of claim 23 wherein measuring, based on the image sequence, the relative position of the one or more objects within the cross section of the blood vessel comprises: measuring a location of the one or more objects within the cross section of the blood vessel, and measuring a distance between the one or more objects and one or more other objects present in the blood flow in the cross section. ### Claim 28 (depends on 25) The method of claim 25, further comprising, determining, using the trained machine learning model, based at least on the differential in speed and the relative position, a size of the one or more anomalies present in the blood flow. ### Claim 29 (depends on 25) The method of claim 25, further comprising, determining, using the trained machine learning model, based at least on the differential in speed and the relative position, a shape of the one or more anomalies present in the blood flow. ### Claim 30 (depends on 25) The method of claim 25, further comprising, determining, using the trained machine learning model, based at least on the differential in speed and the relative position, a frequency of the one or more anomalies present in the blood flow. ### Claim 31 (depends on 25) The method of claim 25, further comprising: confirming, using the trained machine learning model, that at least one of the one or more anomalies has a diameter as small as 90 microns. ### Claim 32 (depends on 23) The method of claim 23, wherein detecting that the one or more objects correspond to the one or more anomalies present in the blood flow comprises: detecting based at least on the differential in speed, the relative position, and one or more risk thresholds for the one or more anomalies associated with a threshold differential in speed or a threshold relative position, that the one or more objects correspond to the one or more anomalies present in the blood flow. ### Claim 33 (depends on 21) The method of claim 21, wherein detecting that the one or more objects correspond to the one or more anomalies present in the blood flow comprises: detecting based at least on the differential in speed, and one or more risk thresholds for the one or more anomalies associated with a threshold differential in speed, that the one or more objects correspond to the one or more anomalies present in the blood flow. ### Claim 34 (depends on 21) The method of claim 21, wherein the ultrasound image sensor uses Color Doppler to construct the image sequence. ### Claim 35 (depends on 21) The method of claim 21, further comprising: treating a medical condition corresponding to the one or more anomalies detected in the blood flow. ### Claim 36 (depends on 21) The method of claim 21, wherein the one or more anomalies are one or more blood clots. ### Claim 37 (depends on 36) The method of claim 36, wherein: detecting that the one or more objects correspond to one or more anomalies present in the blood flow comprises: detecting, using a trained machine learning model, based on the differential in speed, that the one or more blood clots are present in the blood flow; and the machine learning model is trained using a dataset comprising a plurality of image sequences of sample blood flows and corresponding known values including: differentials in speed between blood clots suspended within the sample blood flows and the sample blood flows; and locations of the blood clots suspended with the sample blood flows relative to blood vessels, and distances between the blood clots suspended with the sample blood flows and one or more other blood clots suspended within the sample blood flows. ### Claim 38 A system comprising: an ultrasound image sensor configured to capture an image sequence of a blood flow in a target region of a blood vessel; a non-transitory computer-readable medium having executable instructions stored thereon that, when executed by a processor, causes the system to perform operations comprising: measuring, based on the image sequence, a central frequency corresponding to a first Doppler frequency shift of the blood flow viewed with the ultrasound image sensor in the target region of the blood vessel, and a second Doppler frequency shift corresponding to one or more objects suspended within the blood flow viewed with the ultrasound image sensor; determining, based on the central frequency and the second Doppler frequency shift, a differential in speed between the one or more objects suspended within the blood flow and the blood flow; and detecting, based at least on the differential in speed, that the one or more objects correspond to one or more anomalies present in the blood flow. ### Claim 39 (depends on 38) The system of claim 38, wherein: detecting that the one or more objects correspond to the one or more anomalies present in the blood flow comprises: detecting, using a trained machine learning model, based at least on the differential in speed, that the one or more objects correspond to the one or more anomalies present in the blood flow; and the machine learning model is trained using a dataset comprising a plurality of image sequences of sample blood flows and corresponding known values including: differentials in speed between objects suspended within the sample blood flows and the sample blood flows. ### Claim 40 (depends on 38) The system of claim 38, wherein: the operations further comprise: measuring, based on the image sequence, a relative position of the one or more objects within a cross section of the blood vessel; and detecting that the one or more objects correspond to the one or more anomalies present in the blood flow comprises: detecting, based at least on the differential in speed and the relative position, that the one or more objects correspond to the one or more anomalies present in the blood flow.