ArticleSkin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI)2024
Leveraging deep neural networks to uncover unprecedented levels of precision in the diagnosis of hair and scalp disorders.
Article in Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed, 19 citations in OpenAlex.
- Artificial Intelligence and Digital Technologies in Prakriti Assessment: Toward Standardized Evidence-Based Ayurvedic Practice.Journal of Ayurveda and integrative medicine · 2026Review
- Publicly Available Large Language Models for Trichoscopy: A Head-to-Head Comparison with Dermatologists.Diagnostics (Basel, Switzerland) · 2026Article
- Research and application of digital technology in the diagnosis and treatment of androgenetic alopecia.Frontiers in digital health · 2026Review
- EnSLDe: an enhanced short-range and long-range dependent system for brain tumor classification.Frontiers in oncology · 2025Article
- HairSentinel: a time-aware anomaly detection framework for forecasting hairfall trends using temporal fusion transformers.Frontiers in artificial intelligence · 2025Article
- Leveraging deep neural networks to uncover unprecedented levels of precision in the diagnosis of hair and scalp disorders.Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) · 2024Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 3 countries.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.