# US 12,217,870 B2 · Methods and systems for detecting intravascular device failure

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- Number: US 12,217,870 B2
- Document ID: US12217870B2
- Status: Granted
- Kind: B2
- Application number: 18/378,725
- Filed: 2023-10-11
- Published: 2024-02-08
- Granted: 2025-02-04
- Earliest claimed: 2021-11-03
- Assignees: A Little Cold Gel, LLC
- Applicants: A Little Cold Gel, LLC
- Inventors: Amit Bahl; Steven Johnson
- Web page: https://patentgrep.com/patent/US12217870B2

## CPC

- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- A61B 8/0841: {for locating instruments}
- A61B 8/085: {for locating body or organic structures, e.g. tumours, calculi, blood vessels, nodules}
- A61B 8/12: in body cavities or body tracts, e.g. by using catheters
- A61B 8/5207: {involving processing of raw data to produce diagnostic data, e.g. for generating an image}
- A61B 8/5223: {for extracting a diagnostic or physiological parameter from medical diagnostic data (for algorithms to analyse biomedical images G06T7/0012)}
- A61M 5/16836: {by sensing tissue properties at the infusion site, e.g. for detecting infiltration}
- G16H 20/17: delivered via infusion or injection
- G16H 30/20: for handling medical images, e.g. DICOM, HL7 or PACS
- G16H 30/40: for processing medical images, e.g. editing
- G16H 40/67: for remote operation
- G16H 50/30: for calculating health indices; for individual health risk assessment
- G16H 50/70: for mining of medical data, e.g. analysing previous cases of other patients
- A61B 8/06: Measuring blood flow
- A61B 8/0883: {for diagnosis of the heart}
- A61B 8/0891: {for diagnosis of blood vessels}
- A61B 2090/378: {using ultrasound}
- A61M 2005/1588: {having means for monitoring, controlling or visual inspection, e.g. for patency check or for avoiding extravasation}
- A61M 2205/3375: Acoustical, e.g. ultrasonic, measuring means
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- A61M: DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY (introducing media into or onto the bodies of animals A61D7/00; means for inserting tampons A61F13/26; devices for administering food or medicines orally A61J; containers for collecting, storing or administering blood or medical fluids A61J1/05); DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY (surgery A61B; chemical aspects of surgical articles A61L); DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR {(Electrotherapy, e.g. producing anaesthesia by the use of alternating or intermittent currents A61N1/36021)}

## Abstract

A diagnostic system to aid in diagnosing conditions underneath a subject's skin that predict intravascular device failure is provided. The diagnostic system includes an ultrasound unit that uses ultrasonic energy to obtain images underneath the subject's skin surrounding the insertion site of an intravascular device. The ultrasound unit is in electronic communication with a computing device that collects and stores data generated by the ultrasound unit. The computing device utilizes machine learning or artificial intelligence techniques to identify conditions underneath the subject's skin that predict intravascular device failure, and through a user interface, indicates to the user that subcutaneous conditions predictive of intravascular device failure are present.

## Claims

### Claim 1

A method for diagnosing conditions predictive of intravascular device failure, comprising:
acquiring data characterizing an area of a subject's skin surrounding an insertion site of an intravascular device;
applying a trained machine learning computer implemented method to process the acquired data, wherein the trained machine learning computer implemental method is configured to develop knowledge of training data, the training data comprising at least one of an image or measurement from a plurality of test subjects of an area underneath the test subjects' skin surrounding an insertion site of an intravascular device and an indication comprising intravascular device failure or intravascular device success paired with the image or measurement received from the test subjects; and
providing an indication to a user whether the acquired data indicates impending intravascular device failure.

### Claim 2 (depends on 1)

The method of claim 1, wherein the data characterizing the area of the subject's skin comprises at least one of an image or measurement of the area surrounding the insertion site of the intravascular device.

### Claim 3 (depends on 2)

The method of claim 2, wherein the data characterizing the area of the subject's skin comprises at least one or a combination of: an image of the intravascular device; an image of a distance from the intravascular device to a wall of vasculature in which it is inserted; a measurement of a distance between the intravascular device and the wall of vasculature in which it is inserted; a measurement of a ratio of intravascular device diameter to vascular diameter; a measurement of a length of the intravascular device that resides within the vasculature; an image of an area inside the vasculature in which the intravascular device is inserted; an image of an area surrounding the vasculature in which the intravascular device is inserted; an image or measurement of an angle of insertion of the intravascular device; an image or measurement of an angle of a distal tip of the intravascular device against the wall of vasculature; an image or measurement of a thickness of the wall of vasculature; an image or measurement of a distance of the distal tip to the wall; an image or measurement of a degree of catheter kinking; an image or measurement of thrombus formation; or an image or measurement of subcutaneous edema formation.

### Claim 4 (depends on 1)

The method of claim 1, wherein the intravascular device comprises a peripheral intravenous catheter, an arterial catheter, a peripherally inserted central catheter (PICC), a midline catheter, an extended dwell catheter, a central venous catheter (CVC), a hemodialysis catheter, an ECMO cannulation, a Reboa catheter, or an intra-aortic balloon pump.

### Claim 5 (depends on 1)

The method of claim 1, wherein the data is acquired by applying ultrasonic energy from an ultrasound unit to the area of the subject's skin.

### Claim 6 (depends on 1)

The method of claim 1, wherein the training data are received from a plurality of test subjects that experience intravascular device failure and from a plurality of test subjects that experience successful intravascular device operation.

### Claim 7 (depends on 1)

The method of claim 1, wherein the trained machine learning computer-implemented method comprises at least one of a deep learning network or a convolutional neural network that includes a plurality of convolutional layers.

### Claim 8

A system for diagnosing conditions predictive of intravascular device failure, comprising:
an imaging device configured to capture data characterizing an area of a subject's skin surrounding an insertion site of an intravascular device;
a computing device communicatively coupled to the imaging device, the computing device comprising a processor, a memory, and a computer program stored in the memory, the computer program including instructions configured to, when executed by the processor,
apply artificial intelligence to process at least one image or measurement taken by the imaging device; and
provide an indication to a user of the system whether the at least one image or measurement indicates impending intravascular device failure.

### Claim 9 (depends on 8)

The system of claim 8, wherein the imaging device is an ultrasound unit configured to apply ultrasonic energy.

### Claim 10 (depends on 8)

The system of claim 8, wherein the data characterizing the area of the subject's skin comprises at least one or a combination of: an image of the intravascular device; an image of a distance from the intravascular device to a wall of vasculature in which it is inserted; a measurement of a distance between the intravascular device and the wall of vasculature in which it is inserted; a measurement of a ratio of intravascular device diameter to vascular diameter; a measurement of a length of the intravascular device that resides within the vasculature; an image of an area inside the vasculature in which the intravascular device is inserted; an image of an area surrounding the vasculature in which the intravascular device is inserted; an image or measurement of an angle of insertion of the intravascular device; an image or measurement of an angle of a distal tip of the intravascular device against the wall of vasculature; an image or measurement of a thickness of the wall of vasculature; an image or measurement of a distance of the distal tip to the wall; an image or measurement of a degree of catheter kinking; an image or measurement of thrombus formation; or an image or measurement of subcutaneous edema formation.

### Claim 11 (depends on 8)

The system of claim 8, further comprising a display device configured to display the indication to the user.

### Claim 12 (depends on 8)

The method of claim 8, wherein the artificial intelligence is a trained machine learning computer implemented method.

### Claim 13 (depends on 12)

The method of claim 12, wherein the trained machine learning computer-implemented method comprises at least one of a deep learning network or a convolutional neural network that includes a plurality of convolutional layers.

### Claim 14 (depends on 12)

The method of claim 12, wherein the trained machine learning computer-implemented method is configured to receive and to develop knowledge of ultrasound training data.

### Claim 15 (depends on 14)

The method of claim 14, wherein the ultrasound training data comprises at least one of images or measurements of the area underneath the subject's skin surrounding the insertion site of the intravascular device and an indication comprising at least one of intravascular device failure or intravascular device success paired with images or measurements received from test subjects.

### Claim 16 (depends on 14)

The method of claim 14, wherein the ultrasound training data are received from a plurality of subjects that experience intravascular device failure and from a plurality of subjects that experience successful intravascular device operation.

### Claim 17 (depends on 14)

The method of claim 14, wherein the knowledge developed by the trained machine learning computer-implemented method comprises at least one of information permitting classification of types of alterations underneath the subject's skin that lead to intravascular device failure, information permitting classification of optimal placement of the intravascular device underneath the subject's skin, or information permitting classification of an optimal rotation or angle of the intravascular device underneath the subject's skin.