Real-time closed-loop tissue-specific laser osteotomy using deep-learning-assisted optical coherence tomography

Yakub A Bayhaqi; Arsham Hamidi; Alexander A Navarini; Philippe C Cattin; Ferda Canbaz; Azhar Zam

doi:10.1364/BOE.486660

Real-time closed-loop tissue-specific laser osteotomy using deep-learning-assisted optical coherence tomography

Biomed Opt Express. 2023 May 31;14(6):2986-3002. doi: 10.1364/BOE.486660. eCollection 2023 Jun 1.

Authors

Yakub A Bayhaqi¹, Arsham Hamidi¹, Alexander A Navarini², Philippe C Cattin³, Ferda Canbaz¹, Azhar Zam^{1

4

5}

Affiliations

¹ Biomedical Laser and Optics Group (BLOG), Department of Biomedical Engineering, University of Basel, 4123 Allschwil, Switzerland.
² Digital Dermatology Group, Department of Biomedical Engineering, University of Basel, 4123 Allschwil, Switzerland.
³ Center for medical Image Analysis and Navigation (CIAN), Department of Biomedical Engineering, University of Basel, 4123 Allschwil, Switzerland.
⁴ Division of Engineering, New York University Abu Dhabi, Abu Dhabi, 129188, United Arab Emirates.
⁵ Tandon School of Engineering, New York University, Brooklyn, NY, 11201, USA.

Abstract

This article presents a real-time noninvasive method for detecting bone and bone marrow in laser osteotomy. This is the first optical coherence tomography (OCT) implementation as an online feedback system for laser osteotomy. A deep-learning model has been trained to identify tissue types during laser ablation with a test accuracy of 96.28 %. For the hole ablation experiments, the average maximum depth of perforation and volume loss was 0.216 mm and 0.077 mm³, respectively. The contactless nature of OCT with the reported performance shows that it is becoming more feasible to utilize it as a real-time feedback system for laser osteotomy.