A Case of Extensive Debilitating Generalized Morphea

Morphea Involving the Lips and Gingiva: A Rare Case Report

Morphea is a subtype of scleroderma that does not involve Raynaud's phenomenon or internal organ involvement. It is a connective tissue disease that features the excessive deposition of collagen in the dermis and subcutaneous tissue, leading to a thickening of the dermis and subcutaneous tissue, eventually forming a scar-like lesion. We represent a 19-year-old male Saudi patient displaying a white patch on the marginal gingiva of tooth #21 and multiple yellowish papules on the outer surface of the lip. Both teeth #21 and #22 have experienced recession and bone loss. The patient's clinical history and histopathology revealed characteristic features of localized scleroderma. A treatment was proposed involving immunosuppressants, methotrexate, and pimecrolimus cream along with topical corticosteroids and excimer laser therapy (308 nm). The patient followed the treatment plan for a full month and the white patch quickly improved for the patient. Afterward, the patient has been taking only methotrexate with a significant but gradual improvement. In this paper, we discuss the differential diagnosis to be considered and present an unusual occurrence of localized scleroderma in the oral cavity.

Diagnosis and Management of Cutaneous Manifestations of Autoimmune Connective Tissue Diseases

The cutaneous features of autoimmune connective tissue disease pose a unique challenge to patients and clinicians managing these conditions. In this review, we outline the key elements of diagnosis and treatment of cutaneous lupus erythematosus, dermatomyositis, systemic sclerosis, and morphea. This article also aims to present an update on gold standard as well as new and emerging therapies for these conditions. Overall, dermatologists can play a key role in diagnosing and treating autoimmune connective tissue diseases and this review intends to provide an up-to-date toolkit to guide clinical dermatologists in this endeavor.

Disparities in dermatology AI performance on a diverse, curated clinical image set

An estimated 3 billion people lack access to dermatological care globally. Artificial intelligence (AI) may aid in triaging skin diseases and identifying malignancies. However, most AI models have not been assessed on images of diverse skin tones or uncommon diseases. Thus, we created the Diverse Dermatology Images (DDI) dataset-the first publicly available, expertly curated, and pathologically confirmed image dataset with diverse skin tones. We show that state-of-the-art dermatology AI models exhibit substantial limitations on the DDI dataset, particularly on dark skin tones and uncommon diseases. We find that dermatologists, who often label AI datasets, also perform worse on images of dark skin tones and uncommon diseases. Fine-tuning AI models on the DDI images closes the performance gap between light and dark skin tones. These findings identify important weaknesses and biases in dermatology AI that should be addressed for reliable application to diverse patients and diseases.