AIMC Topic: Skin Neoplasms
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Unravelling tumour cell diversity and prognostic signatures in cutaneous melanoma through machine learning analysis.
Journal of cellular and molecular medicine
Jul 1, 2024
Melanoma, a highly malignant tumour, presents significant challenges due to its cellular heterogeneity, yet research on this aspect in cutaneous melanoma remains limited. In this study, we utilized single-cell data from 92,521 cells to explore the tu...
Metabolomic profiling and accurate diagnosis of basal cell carcinoma by MALDI imaging and machine learning.
Experimental dermatology
Jul 1, 2024
Basal cell carcinoma (BCC), the most common keratinocyte cancer, presents a substantial public health challenge due to its high prevalence. Traditional diagnostic methods, which rely on visual examination and histopathological analysis, do not includ...
From data to diagnosis: skin cancer image datasets for artificial intelligence.
Clinical and experimental dermatology
Jun 25, 2024
Artificial intelligence (AI) solutions for skin cancer diagnosis continue to gain momentum, edging closer towards broad clinical use. These AI models, particularly deep-learning architectures, require large digital image datasets for development. Thi...
Artificial intelligence-empowered cellular morphometric risk score improves prognostic stratification of cutaneous squamous cell carcinoma.
Clinical and experimental dermatology
Jun 25, 2024
BACKGROUND: Risk stratification of cutaneous squamous cell carcinoma (cSCC) is essential for managing patients.
Artificial intelligence for automatic detection of basal cell carcinoma from frozen tissue tangential biopsies.
Clinical and experimental dermatology
Jun 25, 2024
Evaluation of basal cell carcinoma (BCC) involves tangential biopsies of a suspicious lesion that is sent for frozen sections and evaluated by a Mohs micrographic surgeon. Advances in artificial intelligence (AI) have made possible the development of...
Using artificial intelligence-based technologies to support the diagnosis and early detection of melanoma in primary care.
The British journal of dermatology
Jun 20, 2024
Evaluation of an artificial intelligence-based decision support for the detection of cutaneous melanoma in primary care: a prospective real-life clinical trial.
The British journal of dermatology
Jun 20, 2024
BACKGROUND: Use of artificial intelligence (AI), or machine learning, to assess dermoscopic images of skin lesions to detect melanoma has, in several retrospective studies, shown high levels of diagnostic accuracy on par with - or even outperforming ...
Unravelling the metabolic landscape of cutaneous melanoma: Insights from single-cell sequencing analysis and machine learning for prognostic assessment of lactate metabolism.
Experimental dermatology
Jun 1, 2024
This manuscript presents a comprehensive investigation into the role of lactate metabolism-related genes as potential prognostic markers in skin cutaneous melanoma (SKCM). Bulk-transcriptome data from The Cancer Genome Atlas (TCGA) and GSE19234, GSE2...
A novel deep learning framework for accurate melanoma diagnosis integrating imaging and genomic data for improved patient outcomes.
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)
Jun 1, 2024
BACKGROUND: Melanoma is one of the most malignant forms of skin cancer, with a high mortality rate in the advanced stages. Therefore, early and accurate detection of melanoma plays an important role in improving patients' prognosis. Biopsy is the tra...
Comprehensive analysis of clinical images contributions for melanoma classification using convolutional neural networks.
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)
May 1, 2024
BACKGROUND: Timely diagnosis plays a critical role in determining melanoma prognosis, prompting the development of deep learning models to aid clinicians. Questions persist regarding the efficacy of clinical images alone or in conjunction with dermos...
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