AIMC Topic: Skin Diseases

Clear Filters Showing 51 to 60 of 184 articles

Understanding skin color bias in deep learning-based skin lesion segmentation.

Computer methods and programs in biomedicine
BACKGROUND: The field of dermatological image analysis using deep neural networks includes the semantic segmentation of skin lesions, pivotal for lesion analysis, pathology inference, and diagnoses. While biases in neural network-based dermatoscopic ...

A Narrative Review: Opportunities and Challenges in Artificial Intelligence Skin Image Analyses Using Total Body Photography.

The Journal of investigative dermatology
Artificial intelligence (AI) algorithms for skin lesion classification have reported accuracy at par with and even outperformance of expert dermatologists in experimental settings. However, the majority of algorithms do not represent real-world clini...

Revolutionizing diagnostic pathology: The emergence and impact of artificial intelligence-what doesn't kill you makes you stronger?

Clinics in dermatology
This study explored the integration and impact of artificial intelligence (AI) in diagnostic pathology, particularly dermatopathology, assessing its challenges and potential solutions for global health care enhancement. A comprehensive literature sea...

Dermatology and artificial intelligence.

Clinics in dermatology
Artificial Intelligence (AI) is a very powerful new tool that is destined to markedly advance many areas of dermatology, including cosmetic dermatology, oculoplastics, cancer detection and treatment, dermatopathlogy, and identification of pathogens. ...

Identifying risk factors of anti-TNF induced skin lesions and other adverse events in paediatric patients with inflammatory bowel disease.

Journal of pediatric gastroenterology and nutrition
OBJECTIVES: While higher infliximab (IFX) trough concentrations (TCs) are associated with better outcomes in patients with inflammatory bowel disease (IBD), they could pose a risk for adverse events (AEs), including IFX-induced skin lesions. Therefor...

Dermatological disease prediction and diagnosis system using deep learning.

Irish journal of medical science
The prevalence of skin illnesses is higher than that of other diseases. Fungal infection, bacteria, allergies, viruses, genetic factors, and environmental factors are among important causative factors that have continuously escalated the degree and i...

Deep learning for AI-based diagnosis of skin-related neglected tropical diseases: A pilot study.

PLoS neglected tropical diseases
BACKGROUND: Deep learning, which is a part of a broader concept of artificial intelligence (AI) and/or machine learning has achieved remarkable success in vision tasks. While there is growing interest in the use of this technology in diagnostic suppo...

Dermatologist versus artificial intelligence confidence in dermoscopy diagnosis: Complementary information that may affect decision-making.

Experimental dermatology
In dermatology, deep learning may be applied for skin lesion classification. However, for a given input image, a neural network only outputs a label, obtained using the class probabilities, which do not model uncertainty. Our group developed a novel ...

Deep Learning-Based Skin Lesion Multi-class Classification with Global Average Pooling Improvement.

Journal of digital imaging
Cancerous skin lesions are one of the deadliest diseases that have the ability in spreading across other body parts and organs. Conventionally, visual inspection and biopsy methods are widely used to detect skin cancers. However, these methods have s...

A survey on deep learning for skin lesion segmentation.

Medical image analysis
Skin cancer is a major public health problem that could benefit from computer-aided diagnosis to reduce the burden of this common disease. Skin lesion segmentation from images is an important step toward achieving this goal. However, the presence of ...