Congenital Telangiectatic Erythema: Scoping Review.

Abstract

Background: Congenital telangiectatic erythema (CTE), also known as Bloom syndrome, is a rare autosomal recessive disorder characterized by below-average height, a narrow face, a red skin rash occurring on sun-exposed areas of the body, and an increased risk of cancer. CTE is one of many genodermatoses and photodermatoses associated with defects in DNA repair. CTE is caused by a mutation occurring in the BLM gene, which causes abnormal breaks in chromosomes.

Objective: We aimed to analyze the existing literature on CTE to provide additional insight into its heredity, the spectrum of clinical presentations, and the management of this disorder. In addition, the gaps in current research and the use of artificial intelligence to streamline clinical diagnosis and the management of CTE are outlined.

Methods: A literature search was conducted on PubMed, DOAJ, and Scopus using search terms such as "congenital telangiectatic erythema," "bloom syndrome," and "bloom-torre-machacek." Due to limited current literature, studies published from January 2000 to January 2023 were considered for this review. A total of 49 sources from the literature were analyzed.

Results: Through this scoping review, the researchers were able to identify several publications focusing on Bloom syndrome. Some common subject areas included the heredity of CTE, clinical presentations of CTE, and management of CTE. In addition, the literature on rare diseases shows the potential advancements in understanding and treatment with artificial intelligence. Future studies should address the causes of heterogeneity in presentation and examine potential therapeutic candidates for CTE and similarly presenting syndromes.

Conclusions: This review illuminated current advances in potential molecular targets or causative pathways in the development of CTE as well as clinical features including erythema, increased cancer risk, and growth abnormalities. Future studies should continue to explore innovations in this space, especially in regard to the use of artificial intelligence, including machine learning and deep learning, for the diagnosis and clinical management of rare diseases such as CTE.