Lung Nodule
Conditions
Keywords
Artificial Intelligence, Lung Nodule, Cone Beam CT, Lung Puncture, Images Reconstruction
Brief summary
The goal of this clinical trial is to learn if AI-based low dose CBCT reconstructed images can guide lung puncture effectively. The main questions it aims to answer are: 1. Does the AI-based low dose CBCT reconstruction model reconstruct high quality images? 2. Is it possible that low-dose CBCT reconstructed images can guide lung puncture procedures without compromising the efficiency of the procedure? Researchers will compare AI-based low dose CBCT reconstructed images to a placebo (conventional CBCT images) to see if AI-based low dose CBCT reconstructed image can guide lung puncture procedures without compromising the efficiency of the procedure. Participants will: 1. Undergo lung puncture under AI-based low dose CBCT reconstructed images guidance or under conventional CBCT images 2. Be followed up for 1 week postoperative to obtain patient complications
Interventions
OTHERAI-based low dose CBCT reconstructed images system
Participants in the experimental group undergo AI-based low dose (radiation dose is 1/6th of the dose used in the clinic) CBCT reconstructed images guided lung puncture procedures.
OTHERConventional CBCT images system
Participants in the experimental group undergo conventional CBCT images guided lung puncture procedures.
Sponsors
Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
PREVENTION
Masking
TRIPLE (Subject, Investigator, Outcomes Assessor)
Eligibility
Sex/Gender
ALL
Age
18 Years to 80 Years
Healthy volunteers
No
Inclusion criteria
* Participants who require CBCT-guided precutaneous lung puncture (PLP) and meet the clinical indications for the procedure. * Participants whose physical condition is suitable for PLP. * Participants are willing to sign informed consent.
Exclusion criteria
* Participants have metallic implants in the body, which severely affects the image quality. * Participants are pregnant or breastfeeding. * Participants are unwilling or unable to sign informed consent.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Number of puncture needles of participants undergoing percutaneous lung puncture procedures. | From enrollment to the end of the lung puncture procedure. | The number of punctures was defined as the number of punctures performed throughout the percutaneous lung puncture procedure. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Radiation Dose | From enrollment to the end of the lung puncture procedure. | Radiation dose was defined as the radiation dose generated throughout the percutaneous lung puncture procedure. |
| Intraoperative and postoperative complications of participants undergoing lung puncture procedures | From enrollment to the end of the lung puncture procedure at 1 week. | Intraoperative and postoperative complications were defined as those arising in parricipants during percutaneous lung puncture and within 7 days after puncture. |
Other
| Measure | Time frame | Description |
|---|---|---|
| Algorithmic performance (peak signal-to-noise ratio) | Through study completion, an average of 9 months. | Peak signal-to-noise ratio (PSNR) is a metric used to measure the quality of an image or video, assessing the degree of distortion by comparing the peak signal power to the mean square error (MSE) between the original signal and the compressed or processed signal. PSNR is measured in decibels (dB), with higher values indicating less distortion and better image or video quality. |
| Algorithmic performance (structural similarity) | Through study completion, an average of 9 months. | Structural similarity (SSIM) is a metric for evaluating the similarity of two images. SSIM value is between 0 and 1, the larger the value, the more similar the images are. |
Countries
China
Contacts
Primary ContactHuangxuan Zhao, PhD
Outcome results
None listed