# SCH: Overcoming the Intraoperative Data Desert: Biophotonics, Advanced Sensing, and Control for Automated Surgery

> **NIH NIH R01** · DUKE UNIVERSITY · 2020 · $286,383

## Abstract

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...

## Key facts

- **NIH application ID:** 10165101
- **Project number:** 1R01EB030982-01
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** Patrick Codd
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $286,383
- **Award type:** 1
- **Project period:** 2020-09-30 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10165101

## Citation

> US National Institutes of Health, RePORTER application 10165101, SCH: Overcoming the Intraoperative Data Desert: Biophotonics, Advanced Sensing, and Control for Automated Surgery (1R01EB030982-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10165101. Licensed CC0.

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