Evaluation of the Effectiveness of Predicting the Integrity of Interlobar Fissures Based on Chest Image AI Technology

NCT ID: NCT05774730

Last Updated: 2023-03-21

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

UNKNOWN

Total Enrollment

40 participants

Study Classification

OBSERVATIONAL

Study Start Date

2023-03-26

Study Completion Date

2024-12-31

Brief Summary

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The goal of observational study is to evaluate effectiveness of predicting the integrity of interlobar fissures based on chest image AI technology in patients with Chronic Obstructive Pulmonary Disease who will undergo lung volume reduction surgery with endobronchial valve implantation. The main question it aims to answer is: evaluation of the effectiveness of predicting the integrity of interlobar fissures based on chest image AI technology.

Participants will be evaluated by lung CT (quantitative analysis based on chest image AI technology and artificial analysis) and imported Chartis detection system.

Detailed Description

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Conditions

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Chronic Obstructive Pulmonary Disease

Study Design

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Observational Model Type

CASE_CONTROL

Study Time Perspective

PROSPECTIVE

Study Groups

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Emphysema quantitative analysis softwarebased on chest image AI technology

Emphysema quantitative analysis software based on chest image AI technology

Intervention Type OTHER

The participants would undergo lung CT, and the integrity of interlobar fissure will be quantitatively analyses by software based on chest image AI technology.

artificial analysis

artificial analysis of chest image

Intervention Type OTHER

The participants would undergo lung CT, and the integrity of interlobar fissure will be artificially analyses.

imported Chartis detection system

imported Chartis detection system

Intervention Type PROCEDURE

The participants would undergo imported Chartis detection system.

Interventions

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Emphysema quantitative analysis software based on chest image AI technology

The participants would undergo lung CT, and the integrity of interlobar fissure will be quantitatively analyses by software based on chest image AI technology.

Intervention Type OTHER

artificial analysis of chest image

The participants would undergo lung CT, and the integrity of interlobar fissure will be artificially analyses.

Intervention Type OTHER

imported Chartis detection system

The participants would undergo imported Chartis detection system.

Intervention Type PROCEDURE

Eligibility Criteria

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Inclusion Criteria

* Patients with chronic obstructive pulmonary disease who meet the GOLD diagnostic criteria
* FEV1 ≤ 45% pred and FEV1/FVC\<70%
* TLC\>100% pred and RV\>175% pred
* CAT≥18
* \>50% of emphysema destruction
* Smoking prohibition\>6 months
* Sign the informed consent form

Exclusion Criteria

* PaCO2\>8.0 kPa, or PaO2\<6.0 kPa
* 6-minute walking test\<160m
* Obvious chronic bronchitis, bronchiectasis or other infectious lung diseases
* Three hospitalizations due to pulmonary infection in the past 12 months before the baseline assessment
* Previous lobectomy, LVRS or lung transplantation
* LVEF\<45% and or RVSP\>50mmHg
* Anticoagulant therapy that cannot be stopped before surgery
* The patient has obvious immune deficiency
* Participated in other lung drug studies within 30 days before this study
* Pulmonary nodules requiring intervention
* Any disease or condition that interferes with the completion of the initial or subsequent assessment
Minimum Eligible Age

18 Years

Maximum Eligible Age

90 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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China-Japan Friendship Hospital

OTHER

Sponsor Role lead

Responsible Party

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Gang Hou

Principal Investigator

Responsibility Role PRINCIPAL_INVESTIGATOR

Other Identifiers

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2022-NHLHCRF-LX-01-0202-2

Identifier Type: -

Identifier Source: org_study_id

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