# US 2025/0095145 A1 · Endoscopic examination support apparatus, endoscopic examination support method, and recording medium

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- Number: US 2025/0095145 A1
- Document ID: US20250095145A1
- Status: Published application
- Kind: A1
- Application number: 18/559,635
- Filed: 2023-01-11
- Published: 2025-03-20
- Earliest claimed: 2023-01-11
- Assignees: NEC Corporation
- Applicants: NEC Corporation
- Inventors: Kenichi KAMIJO; Yuji IWADATE; Takuma IGARASHI
- Web page: https://patentgrep.com/patent/US20250095145A1

## CPC

- A61B 1/000094: {extracting biological structures}
- A61B 1/000096: {using artificial intelligence}
- A61B 1/0005: {combining images e.g. side-by-side, superimposed or tiled}
- G06T 7/0012: {Biomedical image inspection}
- G06V 10/82: using neural networks
- G16H 30/40: for processing medical images, e.g. editing
- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- G06T 2207/10068: Endoscopic image
- G06T 2207/20081: Training; Learning
- G06T 2207/30028: Colon; Small intestine
- G06T 2207/30096: Tumor; Lesion
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA

## Abstract

In the endoscopic examination support apparatus, the image acquisition means acquires an endoscopic image taken by an endoscope. The first lesion detection means detects a lesion candidate from the endoscopic image, using a machine learning model that learned a relationship between the lesion candidate and a normal state of a large intestine. The second lesion detection means detects a lesion candidate from the endoscopic image, using a machine learning model that learned a relationship between the lesion candidate and a predetermined state of the large intestine. The output means outputs at least one of a detection result of the first lesion detection means and a detection result of the second lesion detection means.

## Claims

### Claim 1

An endoscopic examination support apparatus comprising:
a memory configured to store instructions; and
a processor configured to execute the instructions to:
acquire an endoscopic image taken by an endoscope;
detect a lesion candidate from the endoscopic image, using a first machine learning model that learned a relationship between the lesion candidate and a normal state of a large intestine;
detect a lesion candidate from the endoscopic image, using a second machine learning model that learned a relationship between the lesion candidate and a predetermined state of the large intestine; and
output at least one of a detection result obtained using the first machine learning model and a detection result obtained using the second machine learning model
wherein the predetermined state of the large intestine is an inflammation state, and
wherein the processor detects the lesion candidate using the second machine learning model that learned a relationship between the lesion candidate and an inflammation state of the large intestine.

### Claim 2 (depends on 1)

The endoscopic examination support apparatus according to claim 1, wherein the processor selects and outputs one of the detection result obtained using the first machine learning model and the detection result obtained using the second machine learning model having higher confidence.

### Claim 3 (depends on 1)

The endoscope examination support apparatus according to claim 1,
wherein the processor is further configured to execute the instructions to estimate a part of the large intestine,
wherein the processor selects one of the detection result obtained using the first machine learning model and the detection result obtained using the second machine learning model according to the estimated part of the large intestine.

### Claim 4 (depends on 3)

The endoscope examination support apparatus according to claim 3,
wherein the processor is further configured to execute the instructions to acquire a previous examination result,
wherein the previous examination result includes information of the lesion candidate detected in an inflammation area, and
wherein the processor selects and outputs one of the detection result obtained using the first machine learning model and the detection result obtained using the second machine learning model based on the estimated part of the large intestine and the previous examination result.

### Claim 5 (depends on 1)

The endoscopic examination support apparatus according to claim 1,
wherein the processor is further to execute the instructions to:
select one of the first machine learning model and the second machine learning model to detect the lesion candidate; and
estimate a part of the large intestine,
wherein the processor selects one of the first machine learning model and the second machine learning model to detect the lesion candidate based on the estimated part of the large intestine.

### Claim 6 (depends on 5)

The endoscopic examination support apparatus according to claim 5,
wherein the processor is further configured to execute the instructions to acquire a previous examination result,
wherein the previous examination result includes information of the lesion candidates detected in an inflammation area, and
wherein the processor causes one of the first machine learning model and the second machine learning model to perform detection based on the estimated part of the large intestine and the previous examination result.

### Claim 7

(canceled)

### Claim 8 (depends on 1)

The endoscope examination support apparatus according to claim 1,
wherein the processor is further configured to execute the instructions to estimate an inflammation degree of the large intestine based on the endoscopic image,
wherein the processor selects and outputs one of the detection result obtained using the first machine learning model and the detection result obtained using the second machine learning model according to the estimated inflammation degree of the large intestine.

### Claim 9 (depends on 1)

The endoscopic examination support apparatus according to claim 1,
wherein the predetermined state of the large intestine is a state in which there is a residue in the large intestine, and
wherein the processor detects the lesion candidate using a the second machine learning model that learned a relationship between the lesion candidate and a state of the large intestine with a residue.

### Claim 10 (depends on 1)

The endoscopic examination support apparatus according to claim 1,
wherein the predetermined state of the large intestine is a state in which there is a diverticulum in the large intestine, and
wherein the processor detects the lesion candidate using a the second machine learning model that learned a relationship between the lesion candidate and a state of the large intestine with a diverticulum.

### Claim 11

An endoscopic examination support method comprising:
acquiring an endoscopic image taken by an endoscope;
detecting a lesion candidate from the endoscopic image, using a first machine learning model that learned a relationship between the lesion candidate and a normal state of a large intestine;
detecting a lesion candidate from the endoscopic image, using a second machine learning model that learned a relationship between the lesion candidate and a predetermined state of the large intestine; and
outputting at least one of a detection result obtained using the first machine learning model and a detection result obtained using the second machine learning model
wherein the predetermined state of the large intestine is an inflammation state, and
wherein the second machine learning model is a model that learned a relationship between the lesion candidate and an inflammation state of the large intestine.

### Claim 12

A non-transitory computer-readable recording medium storing a program, the program causing a computer to perform processing of:
acquiring an endoscopic image taken by an endoscope;
detecting a lesion candidate from the endoscopic image, using a first machine learning model that learned a relationship between the lesion candidate and a normal state of a large intestine;
detecting a lesion candidate from the endoscopic image, using a second machine learning model that learned a relationship between the lesion candidate and a predetermined state of the large intestine; and
outputting at least one of a detection result obtained using the first machine learning model and a detection result obtained using the second machine learning model
wherein the predetermined state of the large intestine is an inflammation state, and
wherein the second machine learning model is a model that learned a relationship between the lesion candidate and an inflammation state of the large intestine.

### Claim 13 (depends on 1)

The endoscopic examination support apparatus according to claim 1, wherein the processor outputs the detection result as a guide to a position of the lesion candidate for supporting decision making of a doctor.