5 citations
,
October 2023 in “International Journal on Recent and Innovation Trends in Computing and Communication” In this study, researchers developed a novel image processing method using a multi-class support vector machine that achieved an 89.3% accuracy in classifying alopecia areata and related conditions, outperforming existing models in classification accuracy.
7 citations
,
October 2023 in “Journal of Intelligent & Fuzzy Systems” This study proposed and tested an Ensemble Pre-Learned Deep Learning and Optimized Long Short-Term Memory (EPL-OLSTM) model for classifying Alopecia Areata, achieving a 93.1% accuracy in differentiating healthy from varying severity levels of AA scalp hair using specific datasets.
August 2023 in “Skin Research and Technology” This study found that using planimetric surface area measurement might be a useful supplementary method to improve the accuracy of SALT scoring in alopecia areata, particularly for scores S1 to S3.
5 citations
,
June 2023 in “Engineering Technology & Applied Science Research” This study developed a new neural network model (AA-GAN-AB-MTEDeep) to enhance Alopecia Areata classification using synthetic scalp images, achieving an accuracy of 96.94%.
May 2023 in “Indian journal of science and technology” This study found that an Attention-based Balanced Multi-Task Deep learning system achieved a 95.11% accuracy in classifying Alopecia Areata conditions using hair and scalp images, outperforming classical methods.
The authors of this study developed a novel CNN architecture aimed at improving detection of Alopecia Areata through image-based datasets, achieving a top accuracy of 98% compared to four other machine learning models.
91 citations
,
May 2023 in “Journal of Cutaneous Medicine and Surgery” Alopecia Areata affects 2% globally, with treatments like essential oils, garlic, and JAK inhibitors showing promise, but more research is needed.
9 citations
,
February 2023 This study found that a Faster Residual Convolutional Neural Network model achieved an accuracy of 84.3% in recognizing alopecia areata and various scalp conditions from image databases.
1 citations
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December 2022 in “JAMA Dermatology” This study found that the HairComb algorithm achieved high accuracy in quantifying percentage hair loss across various types of alopecia, suggesting its potential for standardized automated assessments.
January 2022 in “Journal of Pharmaceutical Negative Results” This study found that a VGG-SVM model using machine learning techniques achieved 98.31% accuracy in distinguishing alopecia areata from healthy hair based on image datasets.
290 citations
,
August 2021 in “Clinical Reviews in Allergy & Immunology” JAK inhibitors show promise for treating alopecia areata, but more research is needed.
8 citations
,
August 2021 in “Computational and Mathematical Methods in Medicine” This article proposes a machine learning framework for classifying healthy hair and alopecia areata using image processing and classification techniques, but does not report new clinical findings.
15 citations
,
August 2020 in “Indonesian Journal of Electrical Engineering and Computer Science” This study found that a pre-trained image processing technique accurately classified scalp conditions with 85% accuracy, suggesting potential for automated diagnosis and treatment selection.
20 citations
,
December 2017 in “Journal of Investigative Dermatology Symposium Proceedings” This article presents a computer imaging algorithm that may automate and enhance the Severity of Alopecia Tool scoring for alopecia areata through texture analysis of pediatric images.
66 citations
,
May 2011 in “Dermatologic therapy” This report discusses guidelines for designing alopecia clinical trials that account for variables affecting results, but it presents no new efficacy findings.
