Diagnosis and therapy of actinic keratosis

Actinic keratosis (AK) is considered a chronic and recurring in situ skin neoplasia, with a possible transformation into invasive squamous cell carcinoma (SCC). Among others, predominant risk factors for development of AK are UV-light exposure and immunosuppression. Basal epidermal keratinocyte atypia (AK I) and proliferation (PRO score) seem to drive malignant transformation, rather than clinical appearance of AK (Olsen I–III). Due to the invasiveness of punch biopsy, those histological criteria are not regularly assessed. Non-invasive imaging techniques, such as optical coherence tomography (OCT), reflectance confocal microscopy (RCM) and line-field confocal OCT (LC-OCT) are helpful to distinguish complex cases of AK, Bowen's disease, and SCC. Moreover, LC-OCT can visualize the epidermis and the papillary dermis at cellular resolution, allowing real-time PRO score assessment. The decision-making for implementation of therapy is still based on clinical risk factors, ranging from lesion- to field-targeted and ablative to non-ablative regimens, but in approximately 85% of the cases a recurrence of AK can be observed after a 1-year follow-up. The possible beneficial use of imaging techniques for a non-invasive follow-up of AK to detect recurrence or invasive progression early on should be subject to critical evaluation in further studies

Differences in the annotation between facial images and videos for training an artificial intelligence for skin type determination

Background The Grand-AID research project, consisting of GRANDEL-The Beautyness Company, the dermatology department of Augsburg University Hospital and the Chair of IT Infrastructure for Translational Medical Research at Augsburg University, is currently researching the development of a digital skin consultation tool that uses artificial intelligence (AI) to analyze the user's skin and ultimately perform a personalized skin analysis and a customized skin care routine. Training the AI requires annotation of various skin features on facial images. The central question is whether videos are better suited than static images for assessing dynamic parameters such as wrinkles and elasticity. For this purpose, a pilot study was carried out in which the annotations on images and videos were compared. Materials and Methods Standardized image sequences as well as a video with facial expressions were taken from 25 healthy volunteers. Four raters with dermatological expertise annotated eight features (wrinkles, redness, shine, pores, pigmentation spots, dark circles, skin sagging, and blemished skin) with a semi-quantitative and a linear scale in a cross-over design to evaluate differences between the image modalities and between the raters. Results In the videos, most parameters tended to be assessed with higher scores than in the images, and in some cases significantly. Furthermore, there were significant differences between the raters. Conclusion The present study shows significant differences between the two evaluation methods using image or video analysis. In addition, the evaluation of the skin analysis depends on subjective criteria. Therefore, when training the AI, we recommend regular training of the annotating individuals and cross-validation of the annotation

Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma

Abstract Artificial intelligence (AI) systems have been shown to help dermatologists diagnose melanoma more accurately, however they lack transparency, hindering user acceptance. Explainable AI (XAI) methods can help to increase transparency, yet often lack precise, domain-specific explanations. Moreover, the impact of XAI methods on dermatologists’ decisions has not yet been evaluated. Building upon previous research, we introduce an XAI system that provides precise and domain-specific explanations alongside its differential diagnoses of melanomas and nevi. Through a three-phase study, we assess its impact on dermatologists’ diagnostic accuracy, diagnostic confidence, and trust in the XAI-support. Our results show strong alignment between XAI and dermatologist explanations. We also show that dermatologists’ confidence in their diagnoses, and their trust in the support system significantly increase with XAI compared to conventional AI. This study highlights dermatologists’ willingness to adopt such XAI systems, promoting future use in the clinic