Deep learning with late fusion for the identification of early acute radiation dermatitis using in vivo optical coherence tomography (OCT) images of the human head and neck

Christos Photiou; Constantina Cloconi; Iosif Strouthos; Costas Pitris

doi:10.1117/12.3001617

12 March 2024 Deep learning with late fusion for the identification of early acute radiation dermatitis using in vivo optical coherence tomography (OCT) images of the human head and neck

Christos Photiou, Constantina Cloconi, Iosif Strouthos, Costas Pitris

Author Affiliations +

Proceedings Volume 12830, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVIII; 128300C (2024) https://doi.org/10.1117/12.3001617
Event: SPIE BiOS, 2024, San Francisco, California, United States

Conference Poster

Abstract

About half of all cancer patients receive radiation therapy throughout their illness, thus it continues to be a vital component of cancer treatment. However, a significant number of these patients suffer from radiation-induced skin damage or acute radiation dermatitis (ARD). Severe discomfort, difficulties with everyday tasks, a general decline in quality of life, and occasionally the need to forgo required radiation therapy are all side effects of ARD that have an adverse effect on survival rates. Unfortunately, research on the causes of ARD and prospective therapeutic methods has been hampered by the absence of biomarkers to quantitatively assess early changes related to ARD. In order to identify low-grade ARD, this study will use optical coherence tomography (OCT) images coupled with images from traditional image intensity and novel features. Twenty-two patients had imaging twice weekly during radiation therapy, producing a total of 1487 pictures. Each case's severity was assessed by an experienced oncologist. The preliminary results of the research show that a deep learning approach achieved an 88% accuracy in distinguishing between normal skin and early ARD. These findings provide a promising foundation for further studies aimed at creating a quantitative assessment tool to improve the management of ARD.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Christos Photiou, Constantina Cloconi, Iosif Strouthos, and Costas Pitris "Deep learning with late fusion for the identification of early acute radiation dermatitis using in vivo optical coherence tomography (OCT) images of the human head and neck", Proc. SPIE 12830, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVIII, 128300C (12 March 2024); https://doi.org/10.1117/12.3001617

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