Robotics & Machine Learning Daily News2024,Issue(Sep.17) :78-79.

University of Cyprus Reports Findings in Dermatitis (Feature-Based vs. Deep-Lear ning Fusion Methods for the In Vivo Detection of Radiation Dermatitis Using Opti cal Coherence Tomography, a Feasibility Study)

Robotics & Machine Learning Daily News2024,Issue(Sep.17) :78-79.

University of Cyprus Reports Findings in Dermatitis (Feature-Based vs. Deep-Lear ning Fusion Methods for the In Vivo Detection of Radiation Dermatitis Using Opti cal Coherence Tomography, a Feasibility Study)

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Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Skin Diseases and Cond itions - Dermatitis is the subject of a report. According to news reporting out of Nicosia, Cyprus, by NewsRx editors, research stated, “Acute radiation dermati tis (ARD) is a common and distressing issue for cancer patients undergoing radia tion therapy, leading to significant morbidity. Despite available treatments, AR D remains a distressing issue, necessitating further research to improve prevent ion and management strategies.” Funders for this research include European Commission, University of Cyprus. Our news journalists obtained a quote from the research from the University of C yprus, “Moreover, the lack of biomarkers for early quantitative assessment of AR D impedes progress in this area. This study aims to investigate the detection of ARD using intensity-based and novel features of Optical Coherence Tomography (O CT) images, combined with machine learning. Imaging sessions were conducted twic e weekly on twenty-two patients at six neck locations throughout their radiation treatment, with ARD severity graded by an expert oncologist. We compared a trad itional feature-based machine learning technique with a deep learning late-fusio n approach to classify normal skin vs. ARD using a dataset of 1487 images. The d ataset analysis demonstrates that the deep learning approach outperformed tradit ional machine learning, achieving an accuracy of 88%.”

Key words

Nicosia/Cyprus/Europe/Cyborgs/Dermat itis/Dermatology/Emerging Technologies/Health and Medicine/Imaging Technolog y/Machine Learning/Optical Coherence Tomography/Skin Diseases and Conditions/Skin and Connective Tissue Diseases and Conditions/Technology

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2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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