Deep LearningNCT06274502
Automated Detection and Diagnosis of Pathological DRGs in PHN Patients Using Deep Learning and Magnetic Resonance
An observational study of Deep Learning, run by Huazhong University of Science and Technology at 2 sites in China.
Trial summary
Here, this study aimed to develop an automated system for detecting and diagnosing lesion DRGs in PHN patients based on deep learning. This study retrospectively analyzed the DRG images of all patients with postherpetic neuralgia who underwent magnetic resonance neuroimaging examinations in our radiology department… Read more about this study
- Who is it for?
- Ages 18 to 85 years
- Conditions the study is for
- Deep Learning
- What is being tested?
- YOLOv8
- How many people
- 41 participants
- Where
- 2 sitesChina
- Study dates
- 1 Oct 2023to 31 Jan 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 85 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patients with postherpetic neuralgia due who underwent magnetic resonance neuroimaging examinations in the Radiology Department at our hospital from January 2021 to February 2022.
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
2 sites in 1 country.
China2 sites
Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology
Wuhan, Hubei, 430030
Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology
Wuhan
About this study
Here, this study aimed to develop an automated system for detecting and diagnosing lesion DRGs in PHN patients based on deep learning. This study retrospectively analyzed the DRG images of all patients with postherpetic neuralgia who underwent magnetic resonance neuroimaging examinations in our radiology department from January 2021 to February 2022. After image post-processing, the You Only Look Once (YOLO) version 8 was selected as the target algorithm model. Model performance was evaluated using metrics such as precision, recall, Average Precision, mean average precision and F1 score.
Read the 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.
- Conditions
- Deep Learning
- Being tested
- YOLOv8
- Keywords
- deep learning, Magnetic resonance neurography, dorsal root ganglion, postherpetic neuralgia, YOLOv8
- Medical subject headings
- Neuralgia, Postherpetic
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
YOLOv8Other
After image post-processing, the You Only Look Once (YOLO) version 8 was selected as the target algorithm model.
What is being measured
The primary outcome is the main question the study is designed to answer.
Develop an automated system for detecting and diagnosing lesion DRGs in PHN patients based on deep learning
the You Only Look Once (YOLO) version 8 was selected as the target algorithm model
Measured: 202310-202402
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted23 Feb 2024
- Study start1 Oct 2023
- Primary completion31 Jan 2024
- Study completion31 Jan 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Deep LearningStudies of the same condition, open and under way.
- More trials run by Huazhong University of Science and TechnologyThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 41 participants (actual)
Sponsor and investigators
- Lead sponsor
- Huazhong University of Science and Technology
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Xianwei Zhang，MD, Huazhong University of Science and Technology
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06274502
- Sponsor's protocol ID
- deep learning
- Status last verified
- Mar 2024
- First submitted
- 16 Feb 2024
- Last updated
- 6 Mar 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 6 Mar 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Automated Detection and Diagnosis of Pathological DRGs in PHN Patients Using Deep Learning and Magnetic Resonance
- ClinicalTrials.gov ID
- NCT06274502
- Acronym / sponsor ID
- deep learning
- Conditions
- Deep Learning
- Keywords
- deep learning, Magnetic resonance neurography, dorsal root ganglion, postherpetic neuralgia, YOLOv8
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 41 participants (actual)
- Lead sponsor
- Huazhong University of Science and TechnologyUniversity, hospital or other
- Study start
- 1 Oct 2023 (actual)
- Primary completion
- 31 Jan 2024 (actual)
- Study completion
- 31 Jan 2024 (actual)
- First posted
- 23 Feb 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Here, this study aimed to develop an automated system for detecting and diagnosing lesion DRGs in PHN patients based on deep learning. This study retrospectively analyzed the DRG images of all patients with postherpetic neuralgia who underwent magnetic resonance neuroimaging examinations in our radiology department from January 2021 to February 2022. After image post-processing, the You Only Look Once (YOLO) version 8 was selected as the target algorithm model. Model performance was evaluated using metrics such as precision, recall, Average Precision, mean average precision and F1 score.
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 design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Ages
- 18 to 85 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients with postherpetic neuralgia due who underwent magnetic resonance neuroimaging examinations in the Radiology Department at our hospital from January 2021 to February 2022.
Key inclusion criteria (3 listed)
- 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.
Key exclusion criteria (5 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| YOLOv8After image post-processing, the You Only Look Once (YOLO) version 8 was selected as the target algorithm model. | Other | – |
Primary outcome
Develop an automated system for detecting and diagnosing lesion DRGs in PHN patients based on deep learning
Time frame: 202310-202402
the You Only Look Once (YOLO) version 8 was selected as the target algorithm model
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 6 Mar 2024.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Deep Learning, run by Huazhong University of Science and Technology at 2 sites in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 6 Mar 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Develop an automated system for detecting and diagnosing lesion DRGs in PHN patients based on deep learning (time frame: 202310-202402). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 6 Mar 2024.