CompletedNot Applicable

Automated Detection and Diagnosis of Pathological DRGs in PHN Patients Using Deep Learning and Magnetic Resonance

China41 participantsStarted 2023-10-01

Plain-language 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 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.

Who can participate

Age range18 Years – 85 Years

SexALL

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AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.

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.

What they're measuring

Develop an automated system for detecting and diagnosing lesion DRGs in PHN patients based on deep learning

Timeframe: 202310-202402

Trial details

NCT IDNCT06274502

SponsorHuazhong University of Science and Technology

Sponsor typeOTHER

Study typeOBSERVATIONAL

Primary completion2024-01-31