Summary I Abstract I Scope of Work Title: SCH: INT: Overcoming the lntraoperative Data Desert: Biophotonics, Advanced Sensing, and Control for Automated Surgery Advanced imaging techniques coupled with robotics and machine learning have the potential to overcome current control limitations in surgical procedures such as sarcoma resection. An integrated surgical system first require precise localization of the surgical site and pathological tissue to be resected in order to minimize damage to surrounding structures during the procedure and ensure complete removal; we propose to accomplish this with a sensor-fused optical coherence tomography and fluorescent spectroscopy system. Such a system will be capable of imaging a sarcoma tumor intraoperatively and delineating between healthy and pathological tissue in a 3D volumetric image. This image will be fed into a surgical planning algorithm before being removed with a computer-guided laser scalpel via laser photoablation. Laser ablation has unsurpassed cutting precision and multifunctional capabilities for tissue resection, but it is underutilized due to the need for complex control of the laser power based on variable ablation efficiency and rates for different tissues. Since surgeons are already operating at the peak of their abilities, the introduction of automated closed-loop control systems has the potential to transform intraoperative sensing and precision, thereby addressing current limitations and advancing the field of computer-guided surgery. This project will research and integrate leading-edge technologies toward this vision through three aims: (1) develop a method for rapid intraoperative surface reconstruction, (2) achieve non-contact tumor detection using sensor fusion between optical coherence tomography (OCT) and fluorescence spectroscopy during surgery, and (3) perform precise resection of the targeted tissue with a computer-guided laser scalpel. The proposed methods will integrate intraoperative medical imaging, 3D surface image analysis and registration, and machine learning. Aim 1: Measure accuracy of images from OCT-Fluoroscope on sarcoma mural models. As a first and necessary step in determining the efficacy of the TumorCNC system as a whole, the proposed additional sensory system will be characterized. Accuracy of reconstruction between standard histological staining and the intraoperative OCT-Fluoroscope images will be compared to measure the accuracy of the sensory system independent from the laser photoablation system. Aim 2: Characterize the targeting accuracy of the TumorCNC: an integrated surgical system with OCT, Fluorescent Spectroscopy, and laser Photoablation. Once integrated into one system, the TumorCNC with OCT-Fluoroscope will be evaluated as a complete system for a) accuracy of detection of sarcoma, b) accuracy of surgical surface reconstruction, and c) accuracy of deliverance of laser photoablation to the detection sarcoma regions. Aim 3: Optimize laser ablati...