Concordance of AI-derived quantitative measures and IGA scores demonstrates superior efficacy with faster onset for an investigational extended-release oral minoxidil tablet (VDPHL01) versus immediate-release oral minoxidil for androgenetic alopecia (AGA)
Updated on August 05, 2026
Key Points
Question Can a single algorithm automatically quantitate the extent of hair loss in several different types of alopecia (eg, alopecia areata, central centrifugal centripetal alopecia, female-pattern baldness)?
Findings In this research study to create a new algorithmic quantification system for all hair loss, first, it is shown that there is a correlation between existing scoring systems and the underlying hair loss percentage, and second, a new algorithm to quantify hair loss from images of scarring and nonscarring alopecia is presented. Third, this study demonstrates how this algorithm can measure the percentage of hair loss at every location on the scalp and predict hair loss scores tailored to specific subtypes; in images from 404 participants, this automated hair loss percentage showed more than 92% segmentation accuracy and predicted scores with errors comparable to human annotators.
Meaning The presented algorithm quantifies hair loss from photographic images independent of alopecia type.
Abstract
Importance Clinical estimation of hair density has an important role in assessing and tracking the severity and progression of alopecia, yet to the authors’ knowledge, no automation currently exists for this process. While some algorithms have been developed to assess alopecia presence on a binary level, their scope has been limited by focusing on a re-creation of the Severity of Alopecia Tool (SALT) score for alopecia areata (AA). Yet hair density loss is common to all alopecia forms, and an evaluation of that loss is used in established scoring systems for androgenetic alopecia (AGA), central centrifugal cicatricial alopecia (CCCA), and many more.
Objective To develop and validate a new model, HairComb, to automatically compute the percentage hair loss from images regardless of alopecia subtype.
Design, Setting, and Participants In this research study to create a new algorithmic quantification system for all hair loss, computational imaging analysis and algorithm design using retrospective image data collection were performed. This was a multicenter study, where images were collected at the Children’s Hospital of Philadelphia, University of Pennsylvania (Penn), and via a Penn Dermatology web interface. Images were collected from 2015 to 2021, and they were analyzed from 2019 to 2021.
Main Outcomes and Measures Scoring systems correlation analysis was measured by linear and logarithmic regressions. Algorithm performance was evaluated using image segmentation accuracy, density probability regression error, and average percentage hair loss error for labeled images, and Pearson correlation for manual scores.
Results There were 404 participants aged 2 years and older that were used for designing and validating HairComb. Scoring systems correlation analysis was performed for 250 participants (70.4% female; mean age, 35.3 years): 75 AGA, 66 AA, 50 CCCA, 27 other alopecia diagnoses (frontal fibrosing alopecia, lichen planopilaris, telogen effluvium, etc), and 32 unaffected scalps without alopecia. Scoring systems showed strong correlations with underlying percentage hair loss, with coefficient of determination R2 values of 0.793 and 0.804 with respect to log of percentage hair loss. Using HairComb, 92% accuracy, 5% regression error, 7% hair loss difference, and predicted scores with errors comparable to annotators were achieved.
Conclusions and Relevance In this research study,it is shown that an algorithm quantitating percentage hair loss may be applied to all forms of alopecia. A generalizable automated assessment of hair loss would provide a way to standardize measurements of hair loss across a range of conditions.
Introduction
Alopecia, or hair loss, is experienced in some form by most people during their lifetime. The condition manifests itself through a wide variety of patterns and etiologies. For example, female-pattern hair loss (FPHL) is a nonscarring hair loss due to follicular miniaturization that affects 30 million women in the US1; alopecia areata (AA) is an autoimmune disease with a lifetime incidence of 2%2; and central centrifugal cicatricial alopecia (CCCA) is a scarring alopecia that has been estimated to affect up to 15% of women of African descent.3–6 Underlying these disparate forms of alopecia is a common link: a change in hair density that tracks with the progression of the condition.7,8 Thus, both in the clinic and in clinical trials, hair density is commonly used by dermatologists and other specialists to monitor the progression of hair loss, either directly as a stand-alone hair loss metric or in the context of a visual scale.
Since the 1950s, a multitude of scales have been proposed and established to help quantify, diagnose, and track the stages of hair density loss.9 Designed to be used by clinicians during visual inspection, these scales consist of visual references focusing on gradual decreases in hair density,10–14 binary distinctions between normal and alopecic areas (ie, areas of lower hair density),15–17 or a combination of the 2.18–23 The assessed areas are then used to either directly quantify hair loss or to evaluate the location, shape, and pattern of involvement.
The dermatology community conceptualizes each alopecia scale as a distinct tool, only to be applied in the context of 1 specific form of alopecia. For example, both the Severity of Alopecia Tool (SALT) score16 and its refinement, the Alopecia Density and Extent (ALODEX) score,20 are almost exclusively used for AA, even though both scales function as a direct measure of percentage hair loss—a metric with applicability to all alopecias. Even recently developed algorithms to automate the computation of the overall percentage of scalp area affected have been designed exclusively for 1 diagnosis (AA).24,25 Furthermore, scales that use gradual densities usually only divide severity levels into a few degrees (3 on average) due to the challenges of gauging alopecia extent by eye. A tool that could automate hair loss measurement in such a way that correlates closely with existing clinical scales would greatly diminish the time needed to assess the degree of hair loss and would do so in a more consistent and standardized manner.
In this article, we propose that a single quantification system that measures percentage hair loss would be useful across different types of hair loss. First, we demonstrate that given the same viewing conditions, a strong correlation can be found between distinct alopecia scoring systems and the underlying percentage hair loss. We then present a new automated algorithm, HairComb, to extract percentage hair loss information from images at every point of the scalp and show its applicability, as a tool on its own or as a first step for automating existing scoring systems.
In particular, we focus on 3 commonly used scoring systems: SALT score for AA,16 Sinclair 5-point mid-scalp clinical visual analog scale for FPHL,23 and Olsen Top Extent Scale for CCCA.19 While many other scales for types of alopecia exist, we selected these scales because each derives their values from visual examination of the (same) crown region of the head and have been clinically validated for grading photographs. Shown in Figure 1A are images of 3 types of alopecia (AA, FPHL, and CCCA), each scored according to the 3 scoring systems. These scales serve different purposes and have value independent of alopecia type: the SALT score, or percentage hair loss, gives an overall alopecia assessment; the Sinclair score focuses on the extent of the widening of the central part; and Olsen shifts the attention to the loss in the more pronounced area (anterior or vertex). Results from our proposed algorithm (Figure 1C) quantitate the distribution of local hair loss probability at every pixel and can be applied to any alopecia subtype.
Alopecia scoring systems have been traditionally designed and clinically validated for only 1 type of alopecia (eg, in A, from top to bottom, AA, alopecia areata; FPHL, female-pattern hair loss; and CCCA, central centrifugal cicatricial alopecia). Here, we score each image with the Olsen CCCA scale, the Sinclair scale for FPHL, and percentage hair loss—a proxy for the Severity of Alopecia Tool (SALT) score for AA, derived from the manually annotated labels shown in B, where blue is normal hair density, and yellow, areas of alopecia. Each scale provides value: percentage hair loss gives an overall alopecia assessment; Sinclair focuses on the extent of the widening of the part; and Olsen shifts the attention to the loss in the more pronounced area (anterior or vertex). In C, we show the output distribution of local hair loss probability at every pixel (from red, 100% alopecia, to blue, 100% normal density) computed by our new algorithm, HairComb. GT indicates ground truth.
Validating our method on images taken from 404 participants, we demonstrate that we can quantitate percentage hair loss over the entire scalp and predict scores with a high degree of accuracy, independent of alopecia type.
To design HairComb, we used an alopecia image data set of 1605 images taken from 404 participants. Of this set, 1484 images were taken from 293 participants seen within the dermatology clinics of the Children’s Hospital of Philadelphia and the Hospital of the University of Pennsylvania (Penn) (Table). Scalp images were captured and standardized following guidelines defined by the SALT score16 and its pediatric adaptation26 : for each participant, up to 4 views of the head (top, back, and lateral) were taken per time point. For the alopecia top scores analysis and automation, we added an additional 121 top (crown) view only images from 111 participants enrolled via Penn Dermatology’s alopecia monitoring interface, Trichy (Figure 2).27 While the first group of images was taken entirely in the dermatology clinics, the Trichy set consisted of a mixture of sources from both patients with alopecia and from the general population. Complete image acquisition and preprocessing details are in included in eMethods 1 in the Supplement.28 This study was approved by the hospitals’ respective institutional review boards and followed the CLEAR Derm Consensus Guidelines for evaluation of image-based artificial intelligence reports in dermatology.29 A mix of written and oral informed consent was obtained from all participants. Images were collected from 2015 to 2021, and they were analyzed from 2019 to 2021.
