In Lancet regional health. Americas
BACKGROUND : 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.
METHODS : Here we describe the accuracy of an AI-enabled image-based diagnosis assistant for leprosy, called AI4Leprosy, based on a combination of skin images and clinical data, collected following a standardized process. In a Brazilian leprosy national referral center, 222 patients with leprosy or other dermatological conditions were included, and the 1229 collected skin images and 585 sets of metadata are stored in an open-source dataset for other researchers to exploit.
FINDINGS : We used this dataset to test whether a CNN-based AI algorithm could contribute to leprosy diagnosis and employed three AI models, testing images and metadata both independently and in combination. AI modeling indicated that the most important clinical signs are thermal sensitivity loss, nodules and papules, feet paresthesia, number of lesions and gender, but also scaling surface and pruritus that were negatively associated with leprosy. Using elastic-net logistic regression provided a high classification accuracy (90%) and an area under curve (AUC) of 96.46% for leprosy diagnosis.
INTERPRETATION : Future validation of these models is underway, gathering larger datasets from populations of different skin types and collecting images with smartphone cameras to mimic real world settings. We hope that the results of our research will lead to clinical solutions that help accelerate global leprosy elimination.
FUNDING : This study was partially funded by Novartis Foundation and Microsoft (in-kind contribution).
Barbieri Raquel R, Xu Yixi, Setian Lucy, Souza-Santos Paulo Thiago, Trivedi Anusua, Cristofono Jim, Bhering Ricardo, White Kevin, Sales Anna M, Miller Geralyn, Nery José Augusto C, Sharman Michael, Bumann Richard, Zhang Shun, Goldust Mohamad, Sarno Euzenir N, Mirza Fareed, Cavaliero Arielle, Timmer Sander, Bonfiglioli Elena, Smith Cairns, Scollard David, Navarini Alexander A, Aerts Ann, Ferres Juan Lavista, Moraes Milton O
2022-May
AI, AI4leprosy, Artificial intelligence, Dermatology, Image-based diagnosis, Leprosy, Skin lesions
