03486nas a2200445 4500000000100000008004100001260001600042100001600058700000900074700001300083700002000096700001400116700001700130700001400147700001200161700001200173700001300185700001300198700001400211700001300225700001200238700001400250700001200264700001200276700001600288700001300304700001800317700001300335700001500348700001600363700001200379700001400391700001300405245012700418856007200545300001100617490000600628520239200634022001403026 2022 d bElsevier BV1 aBarbieri RR1 aXu Y1 aSetian L1 aSouza-Santos PT1 aTrivedi A1 aCristofono J1 aBhering R1 aWhite K1 aSales A1 aMiller G1 aNery JAC1 aSharman M1 aBumann R1 aZhang S1 aGoldust M1 aSarno E1 aMirza F1 aCavaliero A1 aTimmer S1 aBonfiglioli E1 aSmith WC1 aScollard D1 aNavarini AA1 aAerts A1 aFerres JL1 aMoraes M00aReimagining leprosy elimination with AI analysis of a combination of skin lesion images with demographic and clinical data uhttps://www.sciencedirect.com/science/article/pii/S2667193X22000096 a1001920 v93 a

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.

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