Rethinking Pattern Hair Loss Classification in the Era of Trichoscopy and Artificial Intelligence

This study demonstrates that an AI-based grading framework using a novel area ratio metric improves the accuracy and consistency of male pattern hair loss classification, especially in advanced grades, compared to traditional methods.

This study highlights the potential of novel non-invasive testing techniques to enhance the diagnosis, treatment, and care of scalp hair diseases, urging future research to improve their accuracy and efficiency.

This study examined trichoscopic findings in androgenetic alopecia, reporting that hair diameter variability, vellus hairs, and the peripilar sign are valuable indicators for diagnosing this condition.

This study found that trichoscopy is an effective method for monitoring treatment response in androgenetic alopecia when conducted by doctors experienced in hair diseases, showing excellent agreement with TrichoScan measurements for various hair parameters.

This study reviewed 39 studies with 3204 patients to identify characteristic trichoscopic findings in alopecia areata, highlighting yellow and black dots, broken, short vellus, and tapering hairs as key indicators. It found no single diagnostic trichoscopic finding but observed trichoscopy's utility in monitoring treatment response.

This study developed new graded visual scales integrating macroscopic and trichoscopic images for assessing the severity of androgenetic alopecia in men and women, potentially aiding in more objective disease evaluation.

This review highlights current and future AI applications in hair restoration and diagnosis of hair disorders, including automated systems for hair detection and self-diagnosis, emphasizing the need for experts to understand their benefits and limitations.

This chapter outlines various methods for evaluating alopecia but observes that most have limitations hindering their clinical use, emphasizing the need for more precise and practical tools for assessing alopecia in patients.

This review discusses various classification systems for male-pattern hair loss, comparing their detail, practicality, and reproducibility, and reports no new clinical outcomes.

This review discusses various classification systems for patterned hair loss in both sexes and reports no new clinical findings, highlighting the Hamilton-Norwood and Ludwig systems as the most commonly used.

This study found that the BASP classification system for pattern hair loss showed better reproducibility and repeatability compared to the Norwood-Hamilton classification.