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Inform. med."],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>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.<\/jats:p>","DOI":"10.1007\/s10278-024-01241-4","type":"journal-article","created":{"date-parts":[[2024,9,4]],"date-time":"2024-09-04T16:03:59Z","timestamp":1725465839000},"page":"1137-1146","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Feature-Based vs. Deep-Learning Fusion Methods for the In Vivo Detection of Radiation Dermatitis Using Optical Coherence Tomography, a Feasibility Study"],"prefix":"10.1007","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5843-4288","authenticated-orcid":false,"given":"Christos","family":"Photiou","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9126-8082","authenticated-orcid":false,"given":"Constantina","family":"Cloconi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6229-5471","authenticated-orcid":false,"given":"Iosif","family":"Strouthos","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,9,4]]},"reference":[{"key":"1241_CR1","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1007\/s40257-016-0186-4","volume":"17","author":"M Singh","year":"2016","unstructured":"Singh M, Afsaneh A., Rebecca W., and Sadanori A (2016) Radiodermatitis: A Review of Our Current Understanding. 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Approval was granted by the Bioethics Committee of Cyprus (Jan 11 2021 \/ No: \u0395\u0395\u0392\u039a\/\u0395\u03a0\/2020\/61).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"Informed consent was obtained from all individual participants included in the study. The authors affirm that human research participants provided informed consent for publication of the image in Fig.\u00a0A,","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"The authors have no relevant financial interests in this article and no potential conflicts of interest to disclose.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}