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

NCT06274502 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 41

Last updated 2024-03-06

No results posted yet for this study

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.

Conditions

  • Deep Learning

Interventions

OTHER

YOLOv8

After image post-processing, the You Only Look Once (YOLO) version 8 was selected as the target algorithm model.

Sponsors & Collaborators

  • Huazhong University of Science and Technology

    lead OTHER

Eligibility

Min Age
18 Years
Max Age
85 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2023-10-01
Primary Completion
2024-01-31
Completion
2024-01-31

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06274502 on ClinicalTrials.gov