Deep learning-enabled fluorescence imaging for oral cancer margin classification in preclinical models
Hikaru Kurosawa, Natalie Won, Jay S. Wunder, Sujit Patil, Mandolin Bartling, Esmat Najjar, Sharon Tzelnick, Brian C. Wilson, Jonathan C. Irish, Michael J. Daly
Abstract
Significance: ) while preserving postoperative functionality. Inadequate margins most frequently occur at the deep surgical margins, where tumors are located beneath the tissue surface; however, current fluorescent optical imaging systems are limited by their inability to quantify subsurface structures. Combining structured light techniques with deep learning may enable intraoperative margin assessment of 3D surgical specimens. Aim: A deep learning (DL)-enabled spatial frequency domain imaging (SFDI) system is investigated to provide subsurface depth quantification of fluorescent inclusions. Approach: animal tissue with fluorescent inclusions. Results: . Conclusions: images demonstrates promise in providing margin assessment of oral cancer tumors.