- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT06274502
Automated Detection and Diagnosis of Pathological DRGs in PHN Patients Using Deep Learning and Magnetic Resonance
Study Overview
Detailed Description
Our previous research has confirmed differences in macroscopic and microscopic aspects between the imaging of lesioned dorsal root ganglia (DRG) in patients with postherpetic neuralgia (PHN) and healthy controls. Additionally, our study revealed that while lesioned skin localization is a classic method in clinical practice, there is still a certain rate of discrepancy with the lesioned DRG observed in magnetic resonance imaging (MRI). This suggests the significant value of MRI in diagnosing lesioned DRG in PHN patients. For patients with zoster sine herpete neuralgia, it is even more crucial to identify the lesioned DRG through MRI. However, due to the small size and varied morphology of DRG lesions, diagnosing lesioned DRG through MRI requires specialized knowledge in neuroanatomy and imaging, posing a challenge to clinical practitioners. Identifying lesioned DRG rapidly and accurately is crucial for interventional therapy, as it serves as an essential treatment target for neuropathic pain.
The YOLO (You Only Look Once) series of algorithms are currently widely used single-stage real-time object detection algorithms, including YOLOv1-YOLOv8. Due to their extremely high detection speed, they enable real-time object detection. YOLOv5 and YOLOv8 are now extensively employed in various applications such as autonomous driving, video surveillance, and object tracking. Moreover, the YOLO series is increasingly being applied in the medical field, including tumor and joint capsule lesion detection, demonstrating good accuracy, recall rates, and detection efficiency. This study aims to utilize the YOLOv8 algorithm to develop a fast and accurate object detection model, simultaneously evaluating its performance. It seeks to validate the feasibility and effectiveness of detecting lesioned dorsal root ganglia (DRG) in real-time postherpetic neuralgia using this model, providing a basis for early diagnosis for clinical practitioners and enabling rapid and precise localization of lesioned DRG.
Study Type
Enrollment (Actual)
Contacts and Locations
Study Locations
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Wuhan, China
- Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology
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Hubei
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Wuhan, Hubei, China, 430030
- Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology
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Participation Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
Inclusion Criteria:
- Patients aged 18 years or older;
- Patients with herpes zoster who continue to experience pain for over one month after the crust forms over the skin lesions;
- Clear MRI images showing evident dorsal root ganglia (DRG) lesions.
Exclusion Criteria:
- Patients with severe systemic, metabolic, or neurological diseases that can lead to polyneuropathy, such as multiple myeloma, diabetes, or thyroid diseases;
- Patients with a history of psychiatric disorders, other chronic pain conditions, or substance abuse;
- Patients with a history of thoracic or back surgeries and a history of pain;
- Presence of artifacts in the imaging or unclear image display;
- Target images obscured by other tissues.
Study Plan
How is the study designed?
Design Details
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
Develop an automated system for detecting and diagnosing lesion DRGs in PHN patients based on deep learning
Time Frame: 202310-202402
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the You Only Look Once (YOLO) version 8 was selected as the target algorithm model
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202310-202402
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Collaborators and Investigators
Study record dates
Study Major Dates
Study Start (Actual)
Primary Completion (Actual)
Study Completion (Actual)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Actual)
Study Record Updates
Last Update Posted (Estimated)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Keywords
Other Study ID Numbers
- deep learning
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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