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An observational study to develop and validate real-time anatomical recognition tools for deep neural networks and their surrounding tissues based on deep learning

An observational study to develop and validate real-time anatomical recognition tools for deep neural networks and their surrounding tissues based on deep learning

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2500095389
Enrollment
Unknown
Registered
2025-01-07
Start date
2025-03-05
Completion date
Unknown
Last updated
2025-05-12

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Perioperative pain

Interventions

observation group:None

Sponsors

Renmin Hospital of Wuhan Univercity
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to No maximum

Inclusion criteria

Inclusion criteria: Patients who understand the treatment plan and sign the informed consent form; age>=18 years old, male and female; Those who have lumbar or pelvic MRI or CT examinations and can provide DICOM format image data;

Exclusion criteria

Exclusion criteria: Patients who are allergic to ultrasound coupling agents; Pregnant or breastfeeding; Participants in other clinical trials in the past 3 months; Patients with anatomical abnormalities in the intended observation area/Patients with a history of previous surgery; Patients who cannot lie on their side for ultrasound examination;

Design outcomes

Primary

MeasureTime frame
Accuracy rating of segmentation and fusion;

Secondary

MeasureTime frame
IOU;Haussdorf distance;Expert satisfaction score;Accuracy of block view;Dice;

Countries

China

Contacts

Public ContactZhongyuan Xia

Renmin Hospital of Wuhan University

xiazhongyuan2005@aliyun.com+86 138 0862 8560

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 4, 2026