02187nas a2200373   4500000000100000008004100001260003200042653002000074653001700094653001200111653002300123100001600146700001400162700002700176700002000203700002300223700001000246700001200256700001500268700001300283700001600296700001700312700001200329700002200341700002500363700001400388700001600402245011100418856006300529300001200592520116600604022002501770020001801795       2024                            d  bSpringer Nature Switzerland10aTeledermatology10aTelemedicine10aDataset10aSub-Saharan Africa1 aGottfrois P1 aGröger F1 aAndriambololoniaina FH1 aAmruthalingam L1 aGonzalez-Jimenez A1 aHsu C1 aKessy A1 aLionetti S1 aMavura D1 aNg’ambi W1 aNgongonda DF1 aPouly M1 aRakotoarisaona MF1 aRapelanoro Rabenja F1 aTraoré I1 aNavarini AA00aPASSION for Dermatology: Bridging the Diversity Gap with Pigmented Skin Images from Sub-Saharan Africa  uhttps://papers.miccai.org/miccai-2024/paper/3722_paper.pdf  a703-7123 a<p>Africa faces a huge shortage of dermatologists, with less than one per million people. This is in stark contrast to the high demand for dermatologic care, with 80% of the paediatric population suffering from largely untreated skin conditions. The integration of AI into healthcare sparks significant hope for treatment accessibility, especially through the development of AI-supported teledermatology. Current AI models are predominantly trained on white-skinned patients and do not generalize well enough to pigmented patients. The PASSION project aims to address this issue by collecting images of skin diseases in Sub-Saharan countries with the aim of open-sourcing this data. This dataset is the first of its kind, consisting of 1,653 patients for a total of 4,901 images. The images are representative of telemedicine settings and encompass the most common paediatric conditions: eczema, fungals, scabies, and impetigo. We also provide a baseline machine learning model trained on the dataset and a detailed performance analysis for the subpopulations represented in the dataset. The project website can be found at https://passionderm.github.io/.</p>
  a0302-9743, 1611-3349  a9783031723834