Scleredema of Buschke associated with lichen sclerosus: Three cases

Scleredema adultorum of Buschke is a rare fibromucinous, scleroderma-like connective tissue disease most commonly found in a post-infectious setting or linked to hematological disorders or diabetes. Lichen sclerosus et atrophicus is an autoimmune condition only in 2.5% of cases localized exclusively at an extragenital site, occurring in up to 34% of patients in association with other autoimmune diseases such as vitiligo, thyroid disorders, alopecia areata, lichen planus, morphea, pernicious anemia and systemic lupus erythematosus. In particular, a stronger link with an autoimmune background in lichen sclerosus et atrophicus has been observed in women who showed higher prevalence for autoimmune conditions and circulating autoantibodies. Literature reveals a genetic susceptibility linked to specific HLA types. We report three patients who developed lichen sclerosus et atrophicus superimposed on skin involved by scleredema adultorum of Buschke. Although the association of lichen sclerosus et atrophicus with scleredema adultorum of Buschke could be coincidental, both diseases could be considered part of the spectrum of sclerodermoid disorders with common underlying pathogenetic mechanisms; which could explain the sequential or simultaneous occurrence of both lesions in our patients

Scleredema. A multicentre study of characteristics, comorbidities, course and therapy in 44 patients

BACKGROUND: The prognostic and therapeutic features of scleredema are poorly documented. OBJECTIVES: To describe the characteristics of patients with scleredema regarding demographics, clinical characteristics, comorbidities, therapeutic interventions and course. METHODS: We conducted a retrospective multicentre study. RESULTS: We identified 44 patients (26 men).The mean age at diagnosis was 53.8 years. The most common associated disorders were endocrine/metabolic diseases including 30 patients suffering from diabetes, mostly type 2 diabetes. Monoclonal gammopathies were confirmed in five cases. A preceding respiratory tract infection was not a feature. Treatments with different combination or sequential modalities were used with variable results. Phototherapy (UVA1 or PUVA) was the treatment associated with higher, although partial response. Systemic corticosteroids and immunosuppressive drugs were reserved to patients with severe disease in whom phototherapy had failed or for patients with multiple myeloma. Forty-one patients were followed up (mean period: 32.2 months).Thirty-nine patients are alive, 30 with and 9 without skin disease. Two patients died of cardiovascular complications due to myeloma and severe diabetes. CONCLUSIONS: Scleredema is a chronic debilitating disease associated with diabetes and metabolic syndrome, unresponsive to various treatments but not necessarily a life-threatening condition. Although there is no definitive treatment, phototherapy should be attempted first. Treatment of primary disease including strict glycaemic control combined with physical therapy should be also employed

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

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. © 2024, The Author(s).53 − 5400.1-007/