A Three-Step Diagnostic Algorithm for Alopecia: Pattern Analysis in Trichoscopy
Alexander Katoulis, Georgia Pappa, Dimitrios Sgouros, Effie Markou, Αντώνιος Κανελλέας, Evangelia Bozi, Demetrios Ioannides, Lidia Rudnicka
Abstract
Background/Objectives: Alopecia is a common and distressing hair loss condition that poses a major diagnostic challenge. While histopathology is the gold standard, its invasive nature limits its routine use. Trichoscopy, a non-invasive imaging technique, has shown promises in diagnosing and differentiating the various alopecia subtypes. However, existing diagnostic algorithms primarily rely on dermoscopic findings. To address this, we developed a novel, three-step algorithm that integrates clinical and trichoscopic features and employs pattern analysis as a diagnostic tool. Methods: A comprehensive literature review was conducted to identify key trichoscopic features associated with different alopecia types. The gathered data were used as a base for the description of trichoscopic features and patterns for each subtype of alopecia, either scarring or non-scarring. Results: The proposed algorithm is analyzed into three steps. In the first step, alopecia is categorized by distribution into: patchy, patterned, or diffuse. In the second step, it distinguishes between scarring and non-scarring alopecia based on the absence or presence of follicular ostia, respectively. Lastly, in the third step, alopecias are distinguished based on specific trichoscopic clues, allowing for the identification of distinct trichoscopic patterns. Conclusions: The three-step diagnostic algorithm for alopecia, utilizing clinical and dermoscopic findings, performs a pattern analysis in trichoscopy, leading to a dermoscopic diagnosis with great confidence, and minimizing the need for invasive diagnostic procedures.