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NCT07452354
AI-Based Diabetic Foot Recurrence Cohort
trial testing Researchers predefined groups based on risk stratification to formulate personalized follow-up strategies. in Diabetic Foot Ulcer (DFU) in 200 participants. Not yet recruiting.
31 December 2027
Quick facts
| Lead sponsor | Peking University Third Hospital |
|---|---|
| Status | Not yet recruiting |
| Study type | OBSERVATIONAL |
| Enrollment | 200 |
| Start date | 15 March 2026 |
| Primary completion | 31 December 2027 |
| Estimated completion | 31 December 2028 |
| Sites | 1 location across China |
Drugs / interventions tested
- Researchers predefined groups based on risk stratification to formulate personalized follow-up strategies.
Conditions studied
- Diabetic Foot Ulcer (DFU) — all drugs for Diabetic Foot Ulcer (DFU) →
- Diabete Mellitus — all drugs for Diabete Mellitus →
- Diabetic Foot Ulcer Treatment — all drugs for Diabetic Foot Ulcer Treatment →
- Artificial Intelligence (AI) in Diagnosis — all drugs for Artificial Intelligence (AI) in Diagnosis →
Sponsor
Peking University Third Hospital
Who can join
18 and older, any sex, with Diabetic Foot Ulcer (DFU) or Diabete Mellitus. Patients with the condition only — healthy volunteers not accepted.
Sponsor's own description
Diabetic foot ulcer (DFU) is a major adverse outcome of diabetes, which itself is one of the most significant chronic diseases. The recurrence of DFU involves multiple risk factors, including altered foot loading patterns, patient compliance, family care capacity, blood glucose monitoring, degree of ischemia, and systemic disease control. Early identification of recurrence signs and timely follow-up interventions are crucial for improving prognosis, reducing disability rates, and lowering healthcare costs. However, traditional follow-up systems lack individualized strategies-such as risk stratification, inflexible follow-up intervals, and insufficient compliance management-often resulting in suboptimal outcomes. High-risk patients prone to recurrence may not be followed up frequently enough for early detection, while low-risk patients may undergo unnecessary visits, increasing burdens on both patients and healthcare providers. This inefficiency contributes significantly to the persistently high rates of disability and mortality among recurrent DFU patients. Establishing an individualized follow-up strategy for DFU, supported by advanced technology to address core bottlenecks such as delayed recurrence warnings and inadequate home-based management, represents an effective technical pathway to tackle these issues. Our center proposes to develop a dedicated DFU cohort with comprehensive active follow-up and a multimodal database encompassing well-defined indicators. We aim to explore a high-risk foot grading system for preventing DFU recurrence and design targeted follow-up protocols. By leveraging AI technology, we intend to build a wound warning system capable of identifying DFU recurrence. Furthermore, we seek to establish a telemedicine and AI-assisted, patient-centered home-based self-management framework for early warning and prevention of DFU recurrence.
Publications & conference data
No peer-reviewed publications indexed yet for this trial.
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Verify against primary sources
- ClinicalTrials.gov — authoritative US registry record
- WHO ICTRP — international registry index
- EU Clinical Trials Register
- Sponsor press releases (Google)
- Trial protocol + status: ClinicalTrials.gov NCT07452354 (US National Library of Medicine, public domain)
- Drug + disease cross-links: matched in real time against Drug Landscape's normalised drug + company + condition tables
- Sponsor: as reported to ClinicalTrials.gov by Peking University Third Hospital
- Last refreshed: 5 March 2026
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