Affiliation:
1. Department of Biomedical Engineering University of Basel Allschwil Switzerland
2. Department of Dermatology University Hospital of Basel Basel Switzerland
3. Department of Gynecology University Hospital of Basel Basel Switzerland
Abstract
AbstractBackgroundVulvar lichen sclerosus (VLS) is a chronic inflammatory skin condition associated with significant impairment of quality of life and potential risk of malignant transformation. However, diagnosis of VLS is often delayed due to its variable clinical presentation and shame‐related late consultation. Machine learning (ML)‐trained image recognition software could potentially facilitate early diagnosis of VLS.ObjectiveTo develop a ML‐trained image‐based model for the detection of VLS.MethodsImages of both VLS and non‐VLS anogenital skin were collected, anonymized, and selected. In the VLS images, 10 typical skin signs (whitening, hyperkeratosis, purpura/ecchymosis, erosion/ulcers/excoriation, erythema, labial fusion, narrowing of the introitus, labia minora resorption, posterior commissure (fourchette) band formation and atrophic shiny skin) were manually labelled. A deep convolutional neural network was built using the training set as input data and then evaluated using the test set, where the developed algorithm was run three times and the results were then averaged.ResultsA total of 684 VLS images and 403 non‐VLS images (70% healthy vulva and 30% with other vulvar diseases) were included after the selection process. A deep learning algorithm was developed by training on 775 images (469 VLS and 306 non‐VLS) and testing on 312 images (215 VLS and 97 non‐VLS). This algorithm performed accurately in discriminating between VLS and non‐VLS cases (including healthy individuals and non‐VLS dermatoses), with mean values of 0.94, 0.99 and 0.95 for recall, precision and accuracy, respectively.ConclusionThis pilot project demonstrated that our image‐based deep learning model can effectively discriminate between VLS and non‐VLS skin, representing a promising tool for future use by clinicians and possibly patients. However, prospective studies are needed to validate the applicability and accuracy of our model in a real‐world setting.