AIMC Topic: Skin

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Dermoscopy diagnosis of cancerous lesions utilizing dual deep learning algorithms via visual and audio (sonification) outputs: Laboratory and prospective observational studies.

EBioMedicine
BACKGROUND: Early diagnosis of skin cancer lesions by dermoscopy, the gold standard in dermatological imaging, calls for a diagnostic upscale. The aim of the study was to improve the accuracy of dermoscopic skin cancer diagnosis through use of novel ...

Identification of a closed cutaneous injury after mechanical trauma caused by collision.

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)
PURPOSE: Robotics has evolved rapidly in terms of mechanical design and control in the past few years. Collaborative robots that have direct contact with humans are being introduced in various fields, including industrial and medical services. Becaus...

Infrared Spectral Characteristics of Electrical Injuries on Swine Skin Caused by Different Voltages Based on Machine Learning Algorithms.

Fa yi xue za zhi
OBJECTIVES: To explore infrared spectrum characteristics of different voltages induced electrical injuries on swine skin by using Fourier transform infrared-microspectroscopy (FTIR-MSP) combined with machine learning algorithms, thus to provide a ref...

Automated detection of erythema migrans and other confounding skin lesions via deep learning.

Computers in biology and medicine
Lyme disease can lead to neurological, cardiac, and rheumatologic complications when untreated. Timely recognition of the erythema migrans rash of acute Lyme disease by patients and clinicians is crucial to early diagnosis and treatment. Our objectiv...

Artificial Intelligence Based Skin Classification Using GMM.

Journal of medical systems
This study describes the usage of neural community based on the texture evaluation of pores and skin a variety of similarities in their signs, inclusive of Measles (rubella), German measles (rubella), and Chickenpox etc. In fashionable, these illness...

Domain-specific classification-pretrained fully convolutional network encoders for skin lesion segmentation.

Computers in biology and medicine
BACKGROUND AND OBJECTIVE: Fully convolutional neural networks have been shown to perform well for automated skin lesion segmentation on digital dermatoscopic images. Our concept is that transferring encoder weights from a network trained on a classif...

Diagnostic accuracy of content-based dermatoscopic image retrieval with deep classification features.

The British journal of dermatology
BACKGROUND: Automated classification of medical images through neural networks can reach high accuracy rates but lacks interpretability.

Drug Repurposing Prediction for Immune-Mediated Cutaneous Diseases using a Word-Embedding-Based Machine Learning Approach.

The Journal of investigative dermatology
Immune-mediated diseases affect more than 20% of the population, and many autoimmune diseases affect the skin. Drug repurposing (or repositioning) is a cost-effective approach for finding drugs that can be used to treat diseases for which they are cu...

Skin surface lipid differences in sensitive skin caused by psychological stress and distinguished by support vector machine.

Journal of cosmetic dermatology
BACKGROUND: Sensitive skin refers to skin that tends to be highly responsive to stimuli such as physical, chemical, and psychological. Psychological factors play an important role in the pathology of sensitive skin.

Deep-learning-based, computer-aided classifier developed with a small dataset of clinical images surpasses board-certified dermatologists in skin tumour diagnosis.

The British journal of dermatology
BACKGROUND: Application of deep-learning technology to skin cancer classification can potentially improve the sensitivity and specificity of skin cancer screening, but the number of training images required for such a system is thought to be extremel...