Pediatric severity of alopecia tool

Bernardis, Elena; Nukpezah, Jonathan; Li, Ping; Christensen, Theresa; Castelo‐Soccio, Leslie

doi:10.1111/pde.13327

Cited by 8 publications

(4 citation statements)

References 4 publications

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“…Treatment failure was defined as inadequate regrowth or cessation of MTX due to adverse events. Current existing scoring systems for AA (SALT I, SALT II) were not used in the present study as they were not designed for paediatric alopecia assessments and have not been validated in the paediatric setting …”

Section: Methodsmentioning

confidence: 99%

Methotrexate in the treatment of paediatric alopecia areata: Retrospective case series and updated meta‐analysis

2019

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Background/Objectives: There is a paucity of data regarding the efficacy and safety of methotrexate (MTX) in childhood alopecia areata (AA), particularly in an Australasian cohort. We aimed to assess the efficacy and safety of MTX in childhood AA based on our institutional experience.Methods: A retrospective chart review was performed from patients treated by a consultant dermatologist. Consecutive children with AA treated with MTX seen between 2010 and 2019 were analysed. A meta-analysis of proportions was conducted for response rates, recurrences and complications of reported data in the literature.Results: Overall, 7 cases (70%) achieved treatment success whilst on MTX treatment. The remainder of cases were considered treatment failures. These were three children with partial regrowth with persistent patches on the scalp. No serious side effects were reported. Two cases experienced nausea or vomiting after MTX administration which was transient. Pooled meta-analysis showed success rates of 49.7%, recurrences in 30% and side effects in 15.7% of cases. Conclusions:Overall, this study shows that methotrexate for paediatric AA in an Australian context was beneficial for the majority of cases assessed and had minimal adverse effects. We propose that such treatment should not be ruled out in the paediatric population.

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Section: Methodsmentioning

confidence: 99%

Methotrexate in the treatment of paediatric alopecia areata: Retrospective case series and updated meta‐analysis

2019

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“…A previous study showed that children aged <12 have a percentage difference in the hair-bearing scalp from adults and concluded that children aged 2–11 years need a pediatric SALT scoring system. However, in the current manual SALT scoring method, a change of 2% might not affect the overall SALT score 16 .…”

Section: Discussionmentioning

confidence: 96%

Korean Consensus Criteria for the Severity Classification of Alopecia Areata

Jang,

Lee,

Park

et al. 2024

Ann Dermatol

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Background A set of criteria for severity classification is essential in alopecia areata (AA). Currently, no guidelines are universally accepted for defining AA severity. Objective This study aimed to establish a set of consensus criteria for classifying the severity of and identifying treatment refractoriness in AA. Methods A preliminary draft of the definition for moderate-to-severe AA was crafted based on available evidence, and members of the Korean Hair Research Society (KHRS) subsequently endorsed the recommendation through an online survey. Results In the first Delphi round, consensus was attained on 15 questions. After refining certain items in the second round, consensus was achieved on 23 out of 26 questions. The KHRS first defined AA severity using the severity of alopecia tool (SALT). SALT ≥50 was defined as severe, 20≤ SALT <50 as moderate, and SALT <20 as mild. Moderate AA was considered severe if it meets one or more of the following criteria: dermatology life quality index >10, presence of accompanying eyebrow or eyelash loss, positive hair loss activity, or treatment-refractory AA. Conclusion These consensus criteria can help clinicians accurately diagnose AA, provide appropriate treatment, and monitor its progression.

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“…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 score and its pediatric adaptation: 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).…”

Section: Methodsmentioning

confidence: 99%

Automating Hair Loss Labels for Universally Scoring Alopecia From Images

et al. 2023

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ImportanceClinical 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.ObjectiveTo develop and validate a new model, HairComb, to automatically compute the percentage hair loss from images regardless of alopecia subtype.Design, Setting, and ParticipantsIn 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 MeasuresScoring 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.ResultsThere 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 RelevanceIn 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.

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Pediatric severity of alopecia tool

Cited by 8 publications

References 4 publications

Methotrexate in the treatment of paediatric alopecia areata: Retrospective case series and updated meta‐analysis

Methotrexate in the treatment of paediatric alopecia areata: Retrospective case series and updated meta‐analysis

Korean Consensus Criteria for the Severity Classification of Alopecia Areata

Automating Hair Loss Labels for Universally Scoring Alopecia From Images

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