AIMC Topic: Skin

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Line-field confocal optical coherence tomography coupled with artificial intelligence algorithms as tool to investigate wound healing: A prospective, randomized, single-blinded pilot study.

Journal of the European Academy of Dermatology and Venereology : JEADV
BACKGROUND: Ablative fractional photothermolysis serves as an excellent in vivo model for studying wound healing. The advent of non-invasive imaging devices, such as line-field confocal optical coherence tomography (LC-OCT), enhances this model by en...

Leveraging machine learning to predict drug permeation: impact of menthol and limonene as enhancers.

Molecular diversity
The present study aimed to develop robust machine learning (ML) models to predict the skin permeability of poorly water-soluble drugs in the presence of menthol and limonene as penetration enhancers (PEs). The ML models were also applied in virtual s...

Development of machine learning models for the prediction of the skin sensitization potential of cosmetic compounds.

PeerJ
BACKGROUND: To enhance the accuracy of allergen detection in cosmetic compounds, we developed a co-culture system that combines HaCaT keratinocytes (transfected with a luciferase plasmid driven by the AKR1C2 promoter) and THP-1 cells for machine lear...

Dual scale light weight cross attention transformer for skin lesion classification.

PloS one
Skin cancer is rapidly growing globally. In the past decade, an automated diagnosis system has been developed using image processing and machine learning. The machine learning methods require hand-crafted features, which may affect performance. Recen...

Transfer Contrastive Learning for Raman Spectroscopy Skin Cancer Tissue Classification.

IEEE journal of biomedical and health informatics
Using Raman spectroscopy (RS) signals for skin cancer tissue classification has recently drawn significant attention, because of its non-invasive optical technique, which uses molecular structures and conformations within biological tissue for diagno...

AFCF-Net: A novel U-Net based asymmetric feature calibration and fusion network for skin lesion image segmentation.

PloS one
Skin lesion segmentation plays a pivotal role in the diagnosis and treatment of skin diseases. By using deep neural networks to segment lesion areas, doctors can more accurately assess the severity of health-related conditions of patients and promptl...

Skin Phototype Classification with Machine Learning Based on Broadband Optical Measurements.

Sensors (Basel, Switzerland)
The Fitzpatrick Skin Phototype Classification (FSPC) scale is widely used to categorize skin types but has limitations such as the underrepresentation of darker skin phototypes, low classification resolution, and subjectivity. These limitations may c...

Mormyroidea-inspired electronic skin for active non-contact three-dimensional tracking and sensing.

Nature communications
The capacity to discern and locate positions in three-dimensional space is crucial for human-machine interfaces and robotic perception. However, current soft electronics can only obtain two-dimensional spatial locations through physical contact. In t...

Double-Condensing Attention Condenser: Leveraging Attention in Deep Learning to Detect Skin Cancer from Skin Lesion Images.

Sensors (Basel, Switzerland)
Skin cancer is the most common type of cancer in the United States and is estimated to affect one in five Americans. Recent advances have demonstrated strong performance on skin cancer detection, as exemplified by state of the art performance in the ...