Feature-Based vs. Deep-Learning Fusion Methods for the In Vivo Detection of Radiation Dermatitis Using Optical Coherence Tomography, a Feasibility Study.
Acute radiation dermatitis (ARD) is a common and distressing issue for cancer patients undergoing radiation therapy, leading to significant morbidity. Despite available treatments, ARD remains a distressing issue, necessitating further research to improve prevention and management strategies. Moreov...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 38; no. 2; pp. 1137 - 1147 |
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| Autores principales: | , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=184081744&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184081744 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Apr2025 vid: 38 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 184081744 184081744 184081744 10.1007/s10278-024-01241-4 184081744 ppf: 1137 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Feature-Based vs. Deep-Learning Fusion Methods for the In Vivo Detection of Radiation Dermatitis Using Optical Coherence Tomography, a Feasibility Study. aug: au: Photiou, Christos Cloconi, Constantina Strouthos, Iosif affil: https://ror.org/02qjrjx09 Department of Electrical and Computer Engineering, KIOS Research and Innovation Center of Excellence, University of Cyprus, Nicosia, Cyprus sug: subj: Radiodermatitis Diagnosis Radiodermatitis Physiopathology Tomography, Optical Coherence Methods Image Processing, Computer Assisted Machine Learning Sensitivity and Specificity Evaluation Human Deep Learning Adult Middle Age Aged Aged, 80 and Over Algorithms Descriptive Statistics In Vivo Studies Pilot Studies Funding Source Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over ab: Acute radiation dermatitis (ARD) is a common and distressing issue for cancer patients undergoing radiation therapy, leading to significant morbidity. Despite available treatments, ARD remains a distressing issue, necessitating further research to improve prevention and management strategies. Moreover, the lack of biomarkers for early quantitative assessment of ARD impedes progress in this area. This study aims to investigate the detection of ARD using intensity-based and novel features of Optical Coherence Tomography (OCT) images, combined with machine learning. Imaging sessions were conducted twice weekly on twenty-two patients at six neck locations throughout their radiation treatment, with ARD severity graded by an expert oncologist. We compared a traditional feature-based machine learning technique with a deep learning late-fusion approach to classify normal skin vs. ARD using a dataset of 1487 images. The dataset analysis demonstrates that the deep learning approach outperformed traditional machine learning, achieving an accuracy of 88%. These findings offer a promising foundation for future research aimed at developing a quantitative assessment tool to enhance the management of ARD. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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