Diagnosis and differential diagnosis of tertiary androgenetic alopecia with severe alopecia areata based on high‐resolution MRI

BackgroundHair and scalp disorders present a significant challenge in dermatology due to their clinical diversity and overlapping symptoms, often leading to misdiagnoses. Traditional diagnostic methods rely heavily on clinical expertise and are limited by subjectivity and accessibility, necessitating more advanced and accessible diagnostic tools. Artificial intelligence (AI) and deep learning offer a promising solution for more accurate and efficient diagnosis.MethodsThe research employs a modified Xception model incorporating ReLU activation, dense layers, global average pooling, regularization and dropout layers. This deep learning approach is evaluated against existing models like VGG19, Inception, ResNet, and DenseNet for its efficacy in accurately diagnosing various hair and scalp disorders.ResultsThe model achieved a 92% accuracy rate, significantly outperforming the comparative models, with accuracies ranging from 50% to 80%. Explainable AI techniques like Gradient‐weighted Class Activation Mapping (Grad‐CAM) and Saliency Map provided deeper insights into the model's decision‐making process.ConclusionThis study emphasizes the potential of AI in dermatology, particularly in accurately diagnosing hair and scalp disorders. The superior accuracy and interpretability of the model represents a significant advancement in dermatological diagnostics, promising more reliable and accessible diagnostic methods.

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Leveraging deep neural networks to uncover unprecedented levels of precision in the diagnosis of hair and scalp disorders

Chowdhury,

Sultan,

Jahan

et al. 2024

“…Magnetic resonance imaging had been utilized to noninvasively analyze anatomical changes in the scalp and hair follicles in androgenetic alopecia and alopecia areata. 6,7 A previous study sought to construct a facial map based on six biophysical parameters in 20 volunteers, aged 24−83 years. 1 When the participants were divided into groups with mean ages in their 30s and 70s, the younger group displayed significantly higher TEWL and stratum corneum hydration, while exhibiting lower pH levels.…”

Section: Introductionmentioning

confidence: 99%

Age‐related changes in scalp biophysical parameters: A comparative analysis of the 20s and 50s age groups

Lee

Kim

Park

et al. 2023

BackgroundAge‐related changes in scalp parameters affect hair quality and scalp condition. However, detailed data on biophysical parameters of the scalp across age groups remain scarce. We aimed to investigate the differences in scalp parameters between individuals in their 20s and 50s and analyze their sex‐specific variations.Materials and methodsTwo hundred participants (160 women and 40 men) were equally divided into 20s and 50s age groups. Biophysical parameters of the scalp, including elasticity, pH, trans‐epidermal water loss (TEWL), sebum production, desquamation, firmness, redness, and yellowness, were measured in the vertex, occipital, and temporal regions. Hair density and thickness were measured in the temporal region. The accumulation of advanced glycation end products (AGEs) in the skin was noninvasively measured in a subset of 60 women.ResultsSkin firmness and redness increased with age in women, whereas yellowness increased with age in both sexes. Sebum production and pH levels were significantly lower in the 50s age group than in the 20s age group, particularly in women. TEWL was lower in men in their 50s than in those in their 20s, particularly in the occipital region. A significant reduction in hair density was observed in the 50s age group in both sexes. AGE accumulation in the skin increased with age and was correlated with scalp skin yellowness.ConclusionAge‐related changes in scalp parameters have important implications for hair health and scalp condition. These findings emphasize the importance of considering age and sex when developing hair care strategies.

“…Ye et al. investigated high‐resolution magnetic resonance imaging (MRI) and suggested the subcutaneous tissue layer thickness as an important factor to discriminate the tertiary androgenetic alopecia and AA 8 …”

Section: Introductionmentioning

confidence: 99%

“…7 Ye et al investigated high-resolution magnetic resonance imaging (MRI) and suggested the subcutaneous tissue layer thickness as an important factor to discriminate the tertiary androgenetic alopecia and AA. 8 This study attempted to validate the SALT score through planimetric surface area measurement of alopecic patches, suggesting an objective quantitative model to evaluate the severity of alopecia areata and further presenting the SALT score-specific surface area cutoff value.…”

Section: Introductionmentioning

confidence: 99%

Two‐dimensional planimetry for alopecia areata severity evaluation compared with severity of alopecia tool: A pilot study

Seol,

Hong,

Ahn

et al. 2023

BackgroundSeverity of Alopecia Tool (SALT) is widely used to assess the severity of alopecia areata (AA). However, physician‐related subjectivity exists in SALT scoring (S1–5), especially with initial inspection in the clinical practice. This study investigated two‐dimensional planimetric method to calculate actual surface area of AA, validating SALT scoring.Materials and methodsSALT score was measured twice in each patient based on "initial" inspection in the clinic (SALT‐I) and retrospective assessment of the "photograph" (SALT‐P). Planimetric surface area was calculated by Image J program. Subgroup analysis was performed depending on the agreement between SALT‐I and ‐P; score was described in the order of SALT‐I and SALT‐P.ResultsA total of 93 subjects were enrolled. Planimetric surface area (cm2) of SALT‐I was 2.5–74.9 (S1), 48.8–100.6 (S2), 83.6–205.4 (S3), and 282–367.9 (S4), while SALT‐P was 2.5–59.2 (S1), 41.6–205.4 (S2), 48.8–183.2 (S3), and 282–367.9 (S4). In subgroup analysis, SALT‐I and SALT‐P agreed group showed planimetric surface area (cm2) as 2.5–59.2 (S1‐1), 64.2–100.6 (S2‐2), 168.3–183 (S3‐3), and 282.6–367.9 (S4‐4). Disagreed group showed the value as 54.7 (S1‐2), 41.6–74.9 (S2‐1), 83.6–205.4 (S2‐3), and 48.8–88.6 (S3‐2).ConclusionSALT‐P was more clearly correlated with actual surface area than SALT‐I. Planimetric surface area measurement could be used as a supplementary method especially in the S1 to S3, suggesting 60 cm2, 100 cm2, and 200 cm2 as objective cutoff values to differentiate S1, S2, and S3.