Images in Clinical Medicine from The New England Journal of Medicine — Scombroid Poisoning

Cutaneous and adnexal fungal infections are typically diagnosed with potassium hydroxide (KOH) skin scrapings, fungal cultures, and Periodic acid-Schiff (PAS) biopsy staining. All three current methods of fungal diagnosis require sample processing and turnover time which leads to a delay in diagnosis. Reflectance confocal microscopy (RCM) is a non-invasive, in vivo skin imaging technology that provides real-time dermatologic diagnoses. We present an updated systematic review of the applications of RCM in diagnosing fungal infections in an effort to explore the utility of RCM as an adjunct clinical tool in detecting cutaneous and adnexal fungi We systematically searched the MEDLINE (via PubMed) for studies published from January 2000 to October 2022 that described the utility of RCM in the setting of fungal infections. Of the 25 studies that met the inclusion criteria, 202 patients were included. The following information on the application of RCM in the setting of fungal infections was extracted from each study, if reported: study type, year published, number of patients included, diagnosis/diagnostic methods, and RCM description. Concordant within all included studies, fungal infections presented on RCM as bright, linear, branching, filamentous structures at the level of stratum corneum. A limitation of this review is that 11 of 25 studies were case reports (n = 1). Larger scale studies should be conducted to explore the utility of RCM in diagnosing fungal infections and to enrich the RCM descriptions of specific fungal conditions.

Skin cancer is one of the most prevalent cancers worldwide, with an increasing incidence. Skin cancer is typically classified as melanoma or non-melanoma skin cancer. Melanoma, despite being much less common than basal or squamous cell carcinomas, is the most deadly, with nearly 8,300 Americans expected to die from melanoma each year. Biopsies are currently the gold standard in diagnosing melanoma; however, they can be invasive and expensive and inaccessible to lower-income individuals. Currently, suspicious lesions are triaged with image-based technologies, such as dermatoscopy and confocal microscopy. While these techniques are useful, there is wide inter-user variability and minimal training for dermatology residents on how to properly use these devices. The use of artificial intelligence (AI)-based technologies in dermatology has emerged in recent years to assist in the diagnosis of melanoma that may be more accessible to all patients and more accurate than current methods of screening. This review explores the current status of the application of AI-based algorithms in the detection of melanoma, underscoring its potential to aid dermatologists in clinical practice. We will specifically focus on AI’s application in clinical imaging, dermoscopic evaluation, algorithms that can distinguish melanoma from non-melanoma skin cancers, and in-vivo skin imaging devices.