The Predictive Value of Midscalp Hair Density and Terminal Hair Percentage in the Severity Evaluation of FPHL Assessed by Trichoscan in a Sample of Chinese Population

BACKGROUND: Female pattern hair loss (FPHL) is the most common hair loss disorder in women, which may impair patient’s self-identity. It presents as a diffuse reduction in hair density at the mid and frontal regions of the scalp with preservation of the frontal hairline. TrichoScan can be used as a diagnostic tool and for monitoring patients in male pattern hair loss, but little is known in FPHL with Chinese population. OBJECTIVE: To explore the predictive value of various TrichoScan characteristics at different areas of the scalp in the diagnosis and evaluation of FPHL. MATERIALS AND METHODS: A total of 261 FPHL subjects were divided into three groups according to Ludwig classification. Together with 108 healthy controls, they were evaluated by computer-assisted TrichoScan. Various hair parameters were analyzed in the frontal, midscalp, vertex, parietal, and occipital areas of the scalp. RESULTS: Declining hair densities, terminal hair percentage, hair follicular unit density, mean hair diameter, and average hair/unit combined with increased intermediate hair percentage, and vellus hair percentage were observed across all areas of the scalp for FPHL subjects. Hair density and terminal hair percentage in the midscalp area were the factors most affecting Ludwig classification. The two parameters combined achieved 85.12% correct classification rate and an area under the ROC curve of 0.88 in the diagnosis of FPHL. Additionally, we reported a unique type of FPHL which mostly affected the parietal area, occurring with or without traditional midline changes. CONCLUSION: FPHL in a Chinese population presented characteristics of reduced hair density and miniaturization of hair follicles in the midline scalp and to a lesser extent in the parietal area. Hair density and terminal hair percentage in the midscalp exhibited the greatest predictive value of Ludwig classification. Our results may further provide the basis for quantitative classification of FPHL.