Health Condition 1: A300- Indeterminate leprosy Health Condition 2: A301- Tuberculoid leprosy Health Condition 3: A302- Borderline tuberculoid leprosy Health Condition 4: A303- Borderline leprosy Health Condition 5: A304- Borderline lepromatous leprosy Health Condition 6: A305- Lepromatous leprosy Health Condition 7: A308- Other forms of leprosy
Conditions
Interventions
Intervention1: Nil: Nil
Sponsors
Indian Association of Dermatologists, Venereologists and Leprologists
Eligibility
Inclusion criteria
Inclusion criteria: 1. Patients of all ages and genders with diverse types of leprosy such as the indeterminate, tuberculoid, borderline tuberculoid, borderline boderline, lepromatous leprosy and lepra reactions- type 1 and 2. 2. Patients consenting to participate in the study.
Exclusion criteria
Exclusion criteria: 1. Patients who were treated for leprosy with anti-leprosy drugs for more than 3 months. 2. Patients with pure neuritic leprosy and diffuse infiltration of LL leprosy.
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| To know whether the AI deep learning model achieves a minimum performance of more than 70 percent accuracy and area under ROC validate its feasibility for early detection of leprosy in Indian skin.Timepoint: At 18 months | — |
Secondary
| Measure | Time frame |
|---|---|
| To know whether the model will be able to acheive more than 80 to 90 percentage accuracy and area under curve which would indicate a robust model with strong clinical applicability and further justifying large scale implementationTimepoint: 1 year 6 months | — |
Countries
India
Contacts
Public ContactDr S Chidambara Murthy
Ballari Medical College and Research Centre, Ballari (formerly VIMS)
Outcome results
None listed