How good is artificial intelligence (AI) at solving hairy problems? A review of AI applications in hair restoration and hair disorders

Gupta, Aditya K.; Ivanova, Iordanka A.; Renaud, Helen J.

doi:10.1111/dth.14811

Cited by 13 publications

(16 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Here, we leveraged deep clustering based on COM-Triplet loss to detect melanoma in a two-class problem. The presented approach might also be useful in the detection of other rare diseases based on dermoscopy such as nail [66] or hair disorders [67]. As skin diseases show varying prevalence, training datasets comprising multiple classes of skin disease can also possess similar imbalance problems.…”

Section: Discussionmentioning

confidence: 99%

Deep Clustering via Center-Oriented Margin Free-Triplet Loss for Skin Lesion Detection in Highly Imbalanced Datasets

Öztürk

Çukur

2022

IEEE J. Biomed. Health Inform.

View full text Add to dashboard Cite

Melanoma is a fatal skin cancer that is curable and has dramatically increasing survival rate when diagnosed at early stages. Learning-based methods hold significant promise for the detection of melanoma from dermoscopic images. However, since melanoma is a rare disease, existing databases of skin lesions predominantly contain highly imbalanced numbers of benign versus malignant samples. In turn, this imbalance introduces substantial bias in classification models due to the statistical dominance of the majority class. To address this issue, we introduce a deep clustering approach based on the latentspace embedding of dermoscopic images. Clustering is achieved using a novel center-oriented margin-free triplet loss (COM-Triplet) enforced on image embeddings from a convolutional neural network backbone. The proposed method aims to form maximally-separated cluster centers as opposed to minimizing classification error, so it is less sensitive to class imbalance. To avoid the need for labeled data, we further propose to implement COM-Triplet based on pseudo-labels generated by a Gaussian mixture model (GMM). Comprehensive experiments show that deep clustering with COM-Triplet loss outperforms clustering with triplet loss, and competing classifiers in both supervised and unsupervised settings.

show abstract

Section: Discussionmentioning

confidence: 99%

Deep Clustering via Center-Oriented Margin Free-Triplet Loss for Skin Lesion Detection in Highly Imbalanced Datasets

Öztürk

Çukur

2022

IEEE J. Biomed. Health Inform.

View full text Add to dashboard Cite

show abstract

“…According to yet another in‐depth research, the applications of deep learning for hair restoration are constantly evolving. If there are more accurate methods of measuring hair growth, it will be simpler to identify and treat hair disorders 32 . In order to be effective for diagnosis, deep learning libraries for identifying scalp dermoscopy images will need extensive labelling by hair specialists.…”

Section: Literature Reviewmentioning

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

Skin Research and Technology

View full text Add to dashboard Cite

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.

show abstract

“…The result of these improvements has come in the form of an innovative hair restoration method using robots for FUE-type hair transplantation. 10…”

Section: Ai In Haircare and Hair Transplantmentioning

confidence: 99%

“…It can also be used for hair restoration whereby it helps in hair detection and hair growth management through fully automated systems. The result of these improvements has come in the form of an innovative hair restoration method using robots for FUE‐type hair transplantation 10 …”

Section: Ai In Cosmetic Dermatologymentioning

confidence: 99%

What happens when simulations get real and cosmetic dermatology goes virtual?

Haykal¹,

Cartier

Crest

et al. 2023

J of Cosmetic Dermatology

View full text Add to dashboard Cite

How good is artificial intelligence (AI) at solving hairy problems? A review of AI applications in hair restoration and hair disorders

Cited by 13 publications

References 35 publications

Deep Clustering via Center-Oriented Margin Free-Triplet Loss for Skin Lesion Detection in Highly Imbalanced Datasets

Deep Clustering via Center-Oriented Margin Free-Triplet Loss for Skin Lesion Detection in Highly Imbalanced Datasets

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

What happens when simulations get real and cosmetic dermatology goes virtual?

Contact Info

Product

Resources

About