Reimagining Leprosy Elimination with AI Analysis of a Combination of Skin Lesion Images with Demographic and Clinical Data
- Raquel R Barbieri ,
- Yixi Xu ,
- Lucy Setian ,
- Paulo Thiago Souza-Santos ,
- Anusua Trivedi ,
- Jim Cristofono ,
- Ricardo Bhering ,
- Kevin White ,
- Anna M Sales ,
- Geralyn Miller ,
- José Augusto C Nery ,
- Michael Sharman ,
- Richard Bumann ,
- Shun Zhang ,
- Mohamad Goldust ,
- Euzenir N Sarno ,
- Fareed Mirza ,
- Arielle Cavaliero ,
- Sander Timmer ,
- Elena Bonfiglioli ,
- Cairns Smith ,
- David Scollard ,
- Alexander A Navarini ,
- Ann Aerts ,
- Juan M. Lavista Ferres ,
- Milton O Moraes
Science Direct |
Leprosy is an infectious disease that mostly affects underserved populations. Although it has been largely eliminated, still about 200’000 new patients are diagnosed annually. In the absence of a diagnostic test, clinical diagnosis is often delayed, potentially leading to irreversible neurological damage and its resulting stigma, as well as continued transmission. Accelerating diagnosis could significantly contribute to advancing global leprosy elimination. Digital and Artificial Intelligence (AI) driven technology has shown potential to augment health workers abilities in making faster and more accurate diagnosis, especially when using images such as in the fields of dermatology or ophthalmology. That made us start the quest for an AI-driven diagnosis assistant for leprosy, based on skin images.