2 research outputs found
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Acquired lymphangiectasia: a rare mimic of genital warts
Acquired lymphangiectasia of the vulva is very uncommon. Owing to the non-specific papillomatous manifestation and the vast array of possible differential diagnoses, lymphangioma circumscriptum (LC) still presents a diagnostic challenge. In this report, we present a very rare form of acquired vulvar LC in a 71-year-old patient with a longstanding history of asymptomatic lesions over the labia majora that had been previously treated as genital warts. On examination, the patient had multiple clustered translucent papules up to 15mm in diameter, morphologically reminiscent of vesicles, that oozed clear fluid throughout her groin and swollen labia majora. The patient also suffered concomitant bilateral lower-extremity lymphedema. A skin biopsy showed multiple, irregular-shaped lumina containing eosinophilic material in the upper dermis. Dilated lymphatic channels were lined by a single layer of flattened endothelial cells and the overlying epidermis showed acanthosis, hyperkeratosis, focal mild pseudoepitheliomatous hyperplasia. There is still no consensus on the optimal management of LC. Our patient was referred to a plastic surgeon for further evaluation and treatment. Although there are a variety of therapeutic modalities for LC, positive results are few and relapses are observed
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