# US 2022/0037006 A1 · Method, apparatus and system for diagnosing status of radiotherapy equipment and storage medium

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- Number: US 2022/0037006 A1
- Document ID: US20220037006A1
- Status: Published application
- Kind: A1
- Application number: 17/276,463
- Filed: 2018-09-14
- Published: 2022-02-03
- Earliest claimed: 2018-09-14
- Assignees: Our United Corporation; Shenzhen Our New Medical Technologies Development Co., Ltd.
- Applicants: Our United Corporation; Shenzhen Our New Medical Technologies Development Co., Ltd.
- Inventors: Peng ZAN; Hao YAN
- Web page: https://patentgrep.com/patent/US20220037006A1

## CPC

- G16H 40/40: for the management of medical equipment or devices, e.g. scheduling maintenance or upgrades
- G05B 23/02: Electric testing or monitoring
- G05B 23/0275: {Fault isolation and identification, e.g. classify fault; estimate cause or root of failure}
- G06Q 10/20: Administration of product repair or maintenance
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G05B: CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS (systems for controlling or regulating non-electric variables G05D; systems for regulating electric or magnetic variables G05F; control devices or systems insofar as characterised by mechanical features only G05G)
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR

## Abstract

A method for diagnosing status of radiotherapy equipment includes: acquiring real-time detection data in an operating process of radiotherapy equipment; and processing the real-time detection data by using a status diagnosis model, and generating and outputting real-time status diagnosis data, wherein the real-time status diagnosis data includes at least one of real-time fault diagnosis data and real-time aging diagnosis data.

## Claims

### Claim 1

A method for diagnosing status of radiotherapy equipment, comprising:
acquiring real-time detection data in an operating process of the radiotherapy equipment;
generating real-time status diagnosis data by processing the real-time detection data by using a status diagnosis model, wherein the real-time status diagnosis data comprises at least one of real-time fault diagnosis data and real-time aging diagnosis data; and
outputting the real-time status diagnosis data.

### Claim 2 (depends on 1)

The method according to claim 1, wherein before acquiring the real-time detection data in the operating process of the radiotherapy equipment, the method further comprises:
acquiring a plurality of pieces of training sample data, wherein each of the plurality of pieces of training sample data comprises a set of detection data and a corresponding set of status diagnosis data, and the status diagnosis data comprises at least one of fault diagnosis data and aging diagnosis data; and
acquiring the status diagnosis model by deep learning on the acquired plurality of pieces of training sample data.

### Claim 3 (depends on 2)

The method according to claim 2, further comprising:
receiving revised training sample data, wherein the revised training sample data comprises revised data acquired by revising the real-time status diagnosis data and the real-time detection data corresponding to the real-time status diagnosis data;
setting a weight value of the revised training sample data to be greater than a predetermined weight value; and
updating the status diagnosis model by deep learning on the plurality of pieces of training sample data and the revised training sample data.

### Claim 4 (depends on 2)

The method according to claim 2, wherein
the radiotherapy equipment comprises various types of components;
acquiring the status diagnosis model by deep learning on the acquired plurality of pieces of training sample data comprises:
acquiring training sample data of each component in at least one component by classifying the plurality of pieces of training sample data based on a type of a component corresponding to detection data in each of the plurality of pieces of training sample data; and
acquiring a status diagnosis model of the each component by deep learning on the training sample data of each component; and
in response to acquiring real-time detection data of a target component, generating the real-time status diagnosis data by processing the real-time detection data by using the status diagnosis model comprises:
generating the real-time status diagnosis data by processing the real-time detection data of the target component by using a status diagnosis model of the target component.

### Claim 5 (depends on 2)

The method according to claim 2, wherein
acquiring the status diagnosis model by deep learning on the acquired plurality of pieces of training sample data comprises:
classifying the plurality of pieces of training sample data based on a type of detection data in each of the plurality of pieces of training sample data; and
acquiring a plurality of types of status diagnosis models by deep learning on each type of training sample data; and
generating the real-time status diagnosis data by processing the real-time detection data by using the status diagnosis model comprises:
determining a corresponding type of status diagnosis model based on a type of the acquired real-time detection data; and
generating the real-time status diagnosis data by processing the real-time detection data by using the corresponding type of status diagnosis model.

### Claim 6

A system for diagnosing status of radiotherapy equipment, comprising:
the radiotherapy equipment;
a detection apparatus, disposed in the radiotherapy equipment, and configured to acquire detection data in real time in an operating process of the radiotherapy equipment;
a status diagnosis server, connected to the detection apparatus, and configured to: acquire the real-time detection data acquired by the detection apparatus, and generate and output real-time status diagnosis data by processing the real-time detection data by using a status diagnosis model, wherein the real-time status diagnosis data comprises at least one of real-time fault diagnosis data and real-time aging diagnosis data; and
a remote maintenance platform, connected to the status diagnosis server, and configured to receive and display the real-time status diagnosis data, to instruct a maintainer to maintain the radiotherapy equipment based on the real-time status diagnosis data.

### Claim 7 (depends on 6)

The system according to claim 6, wherein the status diagnosis server is further configured to:
acquire a plurality of pieces of training sample data, wherein each of the plurality of pieces of training sample data comprises a set of detection data and a corresponding set of status diagnosis data, and the status diagnosis data comprises at least one of fault diagnosis data and aging diagnosis data; and
acquire the status diagnosis model by deep learning on the acquired plurality of pieces of training sample data.

### Claim 8 (depends on 7)

The system according to claim 7, wherein the status diagnosis server is further configured to:
receive revised training sample data, wherein the revised training sample data comprises revised data acquired by revising the real-time status diagnosis data and the real-time detection data corresponding to the real-time status diagnosis data;
set a weight value of the revised training sample data to be greater than a predetermined weight value; and
update the status diagnosis model by deep learning on the plurality of pieces of training sample data and the revised training sample data.

### Claim 9 (depends on 7)

The system according to claim 7, wherein
the radiotherapy equipment comprises various types of components;
the status diagnosis server is further configured to:
acquire training sample data of each component in at least one component by classifying the plurality of pieces of training sample data based on a type of a component corresponding to detection data in each of the plurality of pieces of training sample data; and
acquire a status diagnosis model of the each component by deep learning on the training sample data of each component; and
in response to acquiring real-time detection data of a target component, the status diagnosis server is further configured to:
generate the real-time status diagnosis data by processing the real-time detection data of the target component by using a status diagnosis model of the target component.

### Claim 10 (depends on 7)

The system according to claim 7, wherein
the status diagnosis server is further configured to:
classify the plurality of pieces of training sample data based on a type of detection data in each of the plurality of pieces of training sample data;
acquire a plurality of types of status diagnosis models by deep learning on each type of training sample data;
determine a corresponding type of status diagnosis model based on a type of the acquired real-time detection data; and
generate the real-time status diagnosis data by processing the real-time detection data by using the corresponding type of status diagnosis model.

### Claim 11 (depends on 6)

The system according to claim 6, wherein the radiotherapy equipment is disposed at a radiotherapy center, and the remote maintenance platform is disposed at an equipment maintenance center; and the status diagnosis server is disposed at one of the radiotherapy center, the equipment maintenance center, and a cloud computing center.

### Claim 12

(canceled)

### Claim 13

A device for diagnosing status of radiotherapy equipment, comprising: a processor, and a memory storing a computer program runnable by the processor, wherein the processor, when running the computer program, is caused to perform a method for diagnosing status of radiotherapy equipment, and the method comprises:
acquiring real-time detection data in an operating process of the radiotherapy equipment;
generating real-time status diagnosis data by processing the real-time detection data by using a stats diagnosis model, wherein the real-time status diagnosis data comprises at least one of real-time fault diagnosis data and real-time aging diagnosis data; and
outputting the real-time status diagnosis data.

### Claim 14 (depends on 1)

A non-transitory computer-readable storage medium storing at least one instruction, wherein the non-transitory computer-readable storage medium, when running on a computer, causes the computer to perform the method for diagnosing status of radiotherapy equipment of claim 1.

### Claim 15 (depends on 13)

The device according to claim 13, wherein before acquiring the real-time detection data in the operating process of the radiotherapy equipment, the method further comprises:
acquiring a plurality of pieces of training sample data, wherein each of the plurality of pieces of training sample data comprises a set of detection data and a corresponding set of status diagnosis data, and the status diagnosis data comprises at least one of fault diagnosis data and aging diagnosis data; and
acquiring the status diagnosis model by deep learning on the acquired plurality of pieces of training sample data.

### Claim 16 (depends on 15)

The device according to claim 15, wherein the method further comprises:
receiving revised training sample data, wherein the revised training sample data comprises revised data acquired by revising the real-time status diagnosis data and the real-time detection data corresponding to the real-time status diagnosis data;
setting a weight value of the revised training sample data to be greater than a predetermined weight value; and
updating the status diagnosis model by deep learning on the plurality of pieces of training sample data and the revised training sample data.

### Claim 17 (depends on 15)

The device according to claim 15, wherein
the radiotherapy equipment comprises various types of components;
acquiring the status diagnosis model by deep learning on the acquired plurality of pieces of training sample data comprises:
acquiring training sample data of each component in at least one component by classifying the plurality of pieces of training sample data based on a type of a component corresponding to detection data in each of the plurality of pieces of training sample data; and
acquiring a status diagnosis model of the each component by deep learning on the training sample data of each component; and
in response to acquiring real-time detection data of a target component, generating the real-time status diagnosis data by processing the real-time detection data by using the status diagnosis model comprises:
generating the real-time status diagnosis data by processing the real-time detection data of the target component by using a status diagnosis model of the target component.

### Claim 18 (depends on 15)

The device according to claim 15, wherein
acquiring the status diagnosis model by deep learning on the acquired plurality of pieces of training sample data comprises:
classifying the plurality of pieces of training sample data based on a type of detection data in each of the plurality of pieces of training sample data; and
acquiring a plurality of types of status diagnosis models by deep learning on each type of training sample data; and
generating the real-time status diagnosis data by processing the real-time detection data by using the status diagnosis model comprises:
determining a corresponding type of status diagnosis model based on a type of the acquired real-time detection data; and
generating the real-time status diagnosis data by processing the real-time detection data by using the corresponding type of status diagnosis model.