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Development of AI for early detection of diabetic foot complications.

Design and Development of AI-based solution to predict the early stage of diabetic foot complications empowering diabetic foot care - NIL

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
CTRI
Registry ID
CTRI/2024/06/069160
Enrollment
100
Registered
2024-06-19
Start date
Unknown
Completion date
Unknown
Last updated
2024-07-22

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

Conditions

Health Condition 1: E11- Type 2 diabetes mellitus Health Condition 2: E116- Type 2 diabetes mellitus with other specified complications

Interventions

Intervention1: nil: nil Control Intervention1: nil: nil

Sponsors

Physiotherapy School & Centre Seth G S Medical College & KEMH
Lead Sponsor

Eligibility

Inclusion criteria

Inclusion criteria: People with type 2 diabetes mellitus People with type 2 diabetes mellitus and diagnosed with diabetic foot complications Normal Healthy individuals Both gender (Male/Female) 30-70 years

Exclusion criteria

Exclusion criteria: People with venous foot ulcerations People with bilateral Syme’s Amputation and above

Design outcomes

Primary

MeasureTime frame
Development of machine learning algorithm for early detection of diabetic foot complication.Timepoint: Baseline one time assessment—

Secondary

MeasureTime frame
App Usability questionnaireTimepoint: Post 3 weeks of application delivery to the user—

Countries

India

Contacts

Public ContactTaranga Joshi

Physiotherapy School and Centre Seth G S Medical College and KEMH

mpjiandani@gmail.com9820191106

Outcome results

None listed

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