Artificial Intelligence in Cosmetic Dermatology: A Systematic Literature Review

Vatiwutipong, Pat; Vachmanus, Sirawich; Noraset, Thanapon; Tuarob, Suppawong

doi:10.1109/access.2023.3295001

Cited by 13 publications

(4 citation statements)

References 127 publications

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“…AI technologies have been extensively applied in medical fields including diagnosis, treatment, surgery, screening, and epidemiology analysis due to their effectiveness and usefulness [14,[30][31][32][33][34][35][36][37]. However, they have some potential concerns in clinical practice.…”

Section: Discussionmentioning

confidence: 99%

Intelligent Healthcare Platform for Diagnosis of Scalp and Hair Disorders

Ha,

Go,

Choi

2024

Applied Sciences

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Various scalp and hair disorders distress numerous people. Severe scalp hair disorders have an adverse effect on appearance, self-confidence, and quality of life. Therefore, early and exact diagnosis of various scalp hair disorders is important for timely treatment. However, conventional manual examination method is time-consuming, objective, and labor-intensive. The presented study proposes an intelligent healthcare platform for identifying severity levels of six common scalp hair disorders such as dryness, oiliness, erythema, folliculitis, dandruff, and hair loss. To establish a suitable scalp image classification model, we tested three deep learning models (ResNet-152, EfficientNet-B6, and ViT-B/16). Among the three tested deep learning models, the ViT-B/16 model exhibited the best classification performance with an average accuracy of 78.31%. In addition, the attention rollout method was applied to explain the decision of the trained ViT-B/16 model and highlight approximate lesion areas with no additional annotation procedure. Finally, Scalp checker software was developed based on the trained ViT-B/16 model and the attention rollout method. Accordingly, this proposed platform facilitates objective monitoring states of the scalp and early diagnosis of hairy scalp problems.

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Section: Discussionmentioning

confidence: 99%

Intelligent Healthcare Platform for Diagnosis of Scalp and Hair Disorders

Ha,

Go,

Choi

2024

Applied Sciences

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“…With all the challenges Xiqu performers suffer from wearing face paint, skincare has also become an important part of the post-show experience. Skincare has a strong connection with computer-aided tools [3,73] in the intersection of medicine and HCI communities, researchers have made contributions to (1) cosmetic product development [62,65,90],…”

Section: Computer-aided Skincare Tools In Hci Communitymentioning

confidence: 99%

“It is hard to remove from my eye”: Design Makeup Residue Visualization System for Chinese Traditional Opera (Xiqu) Performers

Xiong,

Fu,

Zhu

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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Chinese traditional opera (Xiqu) performers often experience skin problems due to the long-term use of heavy-metal-laden face paints. To explore the current skincare challenges encountered by Xiqu performers, we conducted an online survey (N=136) and semistructured interviews (N=15) as a formative study. We found that incomplete makeup removal is the leading cause of human-induced skin problems, especially the difficulty in removing eye makeup. Therefore, we proposed EyeVis, a prototype that can visualize the residual eye makeup and record the time make-up was worn by Xiqu performers. We conducted a 7-day deployment study (N=12) to evaluate EyeVis. Results indicate that EyeVis helps to increase Xiqu performers' awareness about removing makeup, as well as boosting their confidence and security in skincare. Overall, this work also provides implications for studying the work of people who wear makeup on a daily basis, and helps to promote and preserve the intangible cultural heritage of practitioners.CCS Concepts: • Human-centered computing → Empirical studies in HCI; Assistive technologies.

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“…Various studies have provided a comprehensive view of the current state and future directions of artificial intelligence (AI), focusing on skin disease diagnosis and cosmetic dermatology. Research studies by Mohanty, et al [2], Kumar, et al [3], and Vatiwutipong, et al [4] collectively highlighted the diverse and evolving applications of AI in dermatology. Mohanty, Sutherland, Bezbradica and Javidnia [2] focused on overcoming data scarcity in skin disease diagnosis using advanced techniques like generative adversarial networks and meta-learning, addressing a core challenge also pertinent to Kumar, Koul, Singla and Ijaz [3] in diagnosing various diseases, including skin conditions, emphasizing the necessity of diverse data for accurate diagnosis.…”

Section: Introductionmentioning

confidence: 99%

“…Mohanty, Sutherland, Bezbradica and Javidnia [2] focused on overcoming data scarcity in skin disease diagnosis using advanced techniques like generative adversarial networks and meta-learning, addressing a core challenge also pertinent to Kumar, Koul, Singla and Ijaz [3] in diagnosing various diseases, including skin conditions, emphasizing the necessity of diverse data for accurate diagnosis. Meanwhile, Vatiwutipong, Vachmanus, Noraset and Tuarob [4] expanded the scope to cosmetic dermatology, demonstrating AI's role in areas ranging 2 from product development to treatment prediction. Research challenges in the context of skin disease diagnosis include image preprocessing, and the improvement of methods for tasks like classification, detection, segmentation, multi-task modeling, and dataset characterization [5].…”

Section: Introductionmentioning

confidence: 99%

SkinLiTE: Lightweight Supervised Contrastive Learning Model for Enhanced Skin Lesion Detection and Disease Typification in Dermoscopic Images

Alzahrani

2024

Preprint

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This study introduces SkinLiTE, a lightweight supervised contrastive learning model, tailored to enhance the detection and typification of skin lesions in dermoscopic images. The core of SkinLiTE lies in its unique integration of supervised and contrastive learning approaches, which leverages labeled data to learn generalizable representations. This approach is particularly adept at handling the complexities and imbalances inherent in skin lesions datasets. The methodology encompasses a two-phase learning process. In the first phase, SkinLiTE utilizes an encoder network and a projection head to transform and project dermoscopic images into a feature space where contrastive loss is applied, focusing on minimizing intra-class variations while maximizing inter-class differences. The second phase freezes the encoder's weights, leveraging the learned representations for classification through a series of dense and dropout layers. The model was evaluated using three datasets from Skin Cancer ISIC 2019-2020, covering a wide range of skin conditions. SkinLiTE demonstrated superior performance across various metrics—accuracy, AUC , and F1 score—particularly when compared with traditional supervised learning models. Notably, SkinLiTE achieved an accuracy of 0.9087 using AugMix augmentation for binary classification of skin lesions. It also showed comparable results with the state-of-the-art approaches of ISIC challenge without relying on external data, underscoring its efficacy and efficiency. The results highlight the potential of SkinLiTE as a significant step forward in the field of dermatological AI, offering a robust, efficient, and accurate tool for skin lesion detection and classification. Its lightweight architecture and ability to handle imbalanced datasets make it particularly suited for integration into Internet of Medical Things environments, paving the way for enhanced remote patient monitoring and diagnostic capabilities. This research contributes to the evolving landscape of AI in healthcare, demonstrating the impact of innovative learning methodologies in medical image analysis.

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Artificial Intelligence in Cosmetic Dermatology: A Systematic Literature Review

Cited by 13 publications

References 127 publications

Intelligent Healthcare Platform for Diagnosis of Scalp and Hair Disorders

Intelligent Healthcare Platform for Diagnosis of Scalp and Hair Disorders

“It is hard to remove from my eye”: Design Makeup Residue Visualization System for Chinese Traditional Opera (Xiqu) Performers

SkinLiTE: Lightweight Supervised Contrastive Learning Model for Enhanced Skin Lesion Detection and Disease Typification in Dermoscopic Images

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