# US 12,002,584 B2 · System to detect dielectric changes in matter

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- Number: US 12,002,584 B2
- Document ID: US12002584B2
- Status: Granted
- Kind: B2
- Application number: 17/370,983
- Filed: 2021-07-08
- Published: 2021-10-28
- Granted: 2024-06-04
- Earliest claimed: 2019-01-11
- Assignees: New York University
- Applicants: New York University
- Inventors: Leeor Alon; Seena Dehkharghani
- Web page: https://patentgrep.com/patent/US12002584B2

## CPC

- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- A61B 5/0042: {for the brain}
- A61B 5/0507: using microwaves or terahertz waves
- A61B 5/4064: {Evaluating the brain (for intracranial pressure A61B5/031; for cerebral blood gases A61B5/14553; using EEG A61B5/369)}
- A61B 5/7267: {involving training the classification device}
- G06N 3/0455: Auto-encoder networks; Encoder-decoder networks
- G06N 3/0499: Feedforward networks
- G06N 3/08: Learning methods
- G06N 3/09: Supervised learning
- G16H 10/60: for patient-specific data, e.g. for electronic patient records
- G16H 30/20: for handling medical images, e.g. DICOM, HL7 or PACS
- G16H 30/40: for processing medical images, e.g. editing
- G16H 40/63: for local operation
- G16H 70/60: relating to pathologies
- 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)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

Systems and methods for detecting dielectric changes in matter are provided. The system includes a data collection array to collect microwave scattering data and a machine learning device. The machine learning device is configured to receive the microwave scattering data, patient information, and imaging modality data corresponding to at least one of a presence of disease, absence of disease, or one or more disease features; analyze the microwave scattering data, patient information, and imaging modality data; output at least one of a predicted disease state and predicted features based on the analyzed microwave scattering data, patient information, and imaging modality data; compare the imaging modality data corresponding to the at least one of a presence of disease, absence of disease, or one or more disease features to the predicted disease state or predicted features; and use the comparison as an input into the machine learning device.

## Claims

### Claim 1

A system of detecting dielectric changes in matter, comprising:
a data collection array to collect microwave scattering data;
a machine learning device configured to:
receive the microwave scattering data, patient information, and imaging modality data corresponding to at least one of a presence of disease, absence of disease, or one or more disease features;
analyze the microwave scattering data, patient information, and imaging modality data;
output at least one of a predicted disease state and predicted features based on the analyzed microwave scattering data, patient information, and imaging modality data;
compare the imaging modality data corresponding to the at least one of the presence of disease, absence of disease, or one or more disease features to the predicted disease state or predicted features;
use the comparison as an input into the machine learning device.

### Claim 2 (depends on 1)

The system of claim 1, wherein the imaging modality data comprises at least one of MR data, CT data, and PET data.

### Claim 3 (depends on 1)

The system of claim 1, wherein the machine learning device is a deep neural network machine learning device.

### Claim 4 (depends on 1)

The system of claim 1, wherein the data collection array comprises at least one of wide-band antennas, field programmable gate arrays, software defined radio, and network analyzers.

### Claim 5 (depends on 1)

The system of claim 1, wherein the predicted disease state is a presence of macroscopic arrangements in dielectric properties.

### Claim 6 (depends on 1)

The system of claim 1, wherein the patient information comprises at least one of patient medical history, age, dimensions of body, and sex.

### Claim 7 (depends on 1)

The system of claim 1, wherein the machine learning device is configured to output the at least one of the predicted disease state and predicted features based on one or more broadband microwave frequencies.

### Claim 8 (depends on 1)

The system of claim 1, wherein the machine learning device comprises a classification network and a discrimination network.

### Claim 9

A method of detecting dielectric changes in matter, comprising:
obtaining microwave scattering data from a data collection array;
receiving, by a machine learning device, the microwave scattering data, patient information, and imaging modality data corresponding to at least one of a presence of disease, absence of disease, or one or more disease features;
analyzing, by the machine learning device, the microwave scattering data, patient information, and imaging modality data;
outputting, by the machine learning device, at least one of a predicted disease state and predicted features based on the analyzed microwave scattering data, patient information, and imaging modality data;
comparing, by the machine learning device, the imaging modality data corresponding to the at least one of the presence of disease, absence of disease, or one or more disease features to at least one of the predicted disease state and predicted features; and
using, by the machine learning device, the comparison as an input into the machine learning device.

### Claim 10 (depends on 9)

The method of claim 9, further comprising using correlations between imaging modality data and microwave tomography to output the at least one of the predicted disease state and predicted features.

### Claim 11 (depends on 9)

The method of claim 9, wherein the microwave scattering data comprises at least one of incidence S-parameter data and scattering S-parameter data.

### Claim 12 (depends on 9)

The method of claim 9, wherein the imaging modality data comprises at least one of MR data, CT data, and PET data.

### Claim 13 (depends on 9)

The method of claim 9, wherein the machine learning device is a deep neural network machine learning device.

### Claim 14 (depends on 9)

The method of claim 9, wherein the data collection array comprises at least one of wide-band antennas, field programmable gate arrays, software defined radio, and network analyzers.

### Claim 15 (depends on 9)

The method of claim 9, wherein the at least one of the predicted disease state and predicted features is a presence of hemorrhage.

### Claim 16 (depends on 9)

The method of claim 9, wherein the patient information comprises at least one of patient medical history, age, dimensions of body, and sex.

### Claim 17 (depends on 9)

The method of claim 9, further comprising outputting the at least one of the predicted disease state and predicted features based on one or more broadband microwave frequencies.