Last updated: Date and time

 Student: **Nili Abtahi**
 Mentors: Prof. Iulian Iordachita, Mojtaba Esfandiari

Intraocular surgery requires precise and accurate measurement of forces and depths during the insertion of surgical instruments into the eye. FBG-based force-sensing needles have been developed as a promising solution to provide real-time feedback during surgery, but their accuracy depends on the calibration process. Prior works have focused on developing calibration techniques and data analysis methods to improve the accuracy and reliability of FBG-based force-sensing needles. However, the calibration process is still time-consuming and requires manual intervention, which can be a challenge for medical professionals in a clinical setting. To address these challenges, recent works have focused on developing automatic calibration methods for FBG-based force-sensing instruments. Overall, the development of an automatic calibration process for FBG-based force-sensing instruments can significantly improve the accuracy and reliability of intraocular surgery, reduce the risk of complications, and improve patient outcomes. By combining advanced calibration techniques with machine learning algorithms, medical professionals can obtain real-time feedback during surgery and make more informed decisions, leading to better outcomes for patients.

Intraocular surgery requires precise and accurate measurement of forces and depths during the insertion of surgical instruments into the eye. FBG-based force-sensing needles have been developed as a promising solution to provide real-time feedback during surgery, but their accuracy depends on the calibration process. Prior works have focused on developing calibration techniques and data analysis methods to improve the accuracy and reliability of FBG-based force-sensing needles. However, the calibration process is still time-consuming and requires manual intervention, which can be a challenge for medical professionals in a clinical setting. To address these challenges, recent works have focused on developing automatic calibration methods for FBG-based force-sensing instruments.

Previous studies demonstrated the feasibility of using FBG-based force-sensing instruments for ophthalmic surgery:

- Xingchi He et al. (2013) developed an FBG-based force-sensing needle for retinal microsurgery, calibrated using a mechanical testing system, but with a manual calibration process that depends on operator skill and experience.

Given all the prior works, there is still a need for an automatic calibration method that can be easily applied to different instrument designs and reduce the dependence on operator skill and experience.

The overall goal of the project is to develop an automatic calibration process for an FBG-based force-sensing needle that can accurately measure tip force, sclera force, and insertion depth during intraocular surgery. The project aims to improve the accuracy and reliability of these measurements, which can help reduce the risk of complications and improve patient outcomes. Additionally, the project aims to streamline the calibration process and make it easier and more efficient for medical professionals to use in a clinical setting.

- Develop a standardized procedure or framework for the calibration process that can be easily followed by medical professionals of different experience levels.

- Create a calibration method that can adapt to different FBG-based force-sensing needle designs, making it more versatile and widely applicable.

- Investigate machine learning algorithms and optimization techniques to improve calibration accuracy while reducing the need for extensive training data.

- Develop a robust temperature compensation technique to minimize the influence of temperature fluctuations on the FBG sensor readings during calibration.

- Conduct experimental validation of the proposed automatic calibration method using a reference standard or a validated method to assess its accuracy and reliability.

- Design user-friendly software that can streamline the calibration process and provide an intuitive interface for medical professionals to perform calibration in a clinical setting.

- Overall goal: advance the state of the art in FBG-based force-sensing needles for intraocular surgery, and contribute to safer and more accurate surgical procedures, ultimately improving patient outcomes.

The development of an automatic calibration process for FBG-based force-sensing instruments can significantly improve the accuracy and reliability of intraocular surgery, reduce the risk of complications, and improve patient outcomes. By combining advanced calibration techniques with machine learning algorithms, medical professionals can obtain real-time feedback during surgery and make more informed decisions, leading to better outcomes for patients.

The deliverables of this project include an automatic calibration process for an FBG-based force-sensing needle, a graphical user interface (preferably a mobile app) to automate the calibration process, and the validation of the calibrated sensor by comparing its readings to those obtained from a reference standard or another validated method.

• Minimum: (March 25th)

1. Experimental setup and documented calibration procedure

2. Automatic data analysis framework

3. Calibration of the force-sensing instrument

4. Validation of the results

• Expected: (May 1st)

1. Software Requirements Specification (SRS)

2. GUI Design Documents

3. GUI Prototype

4. Source Code

5. Develop a mobile application for tele-calibration

• Maximum: (May 13th)

1. Nonlinear Calibration (higher order polynomials)

2. Temperature Compensation

3. Factorial Experiments

linearity: Until now, we have obtained an algebraic and first-order relationship between the FBG sensors and the force values ft, fs, which is a comprehensive and complete model because non-linear functions are not possible for FBGs.

Temperature : Eliminate the effect of temperature changes on the FBG sensors by using methods such as neural network or other forms of analysis.

Factorial Experiments : Factorial experiments can be used to reduce the amount of time required for calibration in this project by identifying the most important factors that affect the accuracy of the force-sensing instrument, and optimizing these factors using a minimum number of experiments.

The project depends on the availability of FBG-based force-sensing needles and equipment, as well as the expertise of the project team and mentors.

The project proposal is for Spring 2023. The milestones and status of the project will be determined during the course of the project.

• Project Plan
  • proposal_presentation.pptx
  • Project plan proposal
• Project Background Reading
  • background_reading_presentation.pptx
  • background_reading.pdf
  • background_reading_paper_1.pdf
  • background_reading_paper_2.pdf
• Project Checkpoint
  • check_point_presentation.pptx
• Paper Seminar Presentations
  • here provide links to all seminar presentations
• Project Final Presentation
  • PDF of Poster
• Project Final Report
  • Final Report
  • links to any appendices or other material
  • appendix_1._sclera_force_flowchart.pdf
  • appendix_2._k2_k3_flowchart.pdf

1. X. He, M. Balicki, P. Gehlbach, J. Handa, R. Taylor, and I. Iordachita, “A Multi-Function Force Sensing Instrument with Variable Admittance Robot Control for Retinal Microsurgery,” IEEE International Conference on Robotics and Automation (ICRA), Hongkong, China, May 31-June 7, 2014.
2. I. I. Iordachita, M. D. De Smet, G. Naus, M. Mitsuishi and C. N. Riviere, “Robotic Assistance for Intraocular Microsurgery: Challenges and Perspectives,” in Proceedings of the IEEE, vol. 110, no. 7, pp. 893-908, July 2022, doi: 10.1109/JPROC.2022.3169466. 
3. Zhenglong Sun, M. Balicki, Jin Kang, J. Handa, R. Taylor and I. Iordachita, “Development and preliminary data of novel integrated optical micro-force sensing tools for retinal microsurgery,” 2009 IEEE International Conference on Robotics and Automation, Kobe, Japan, 2009, pp. 1897-1902, doi: 10.1109/ROBOT.2009.5152836.

Here give list of other project files (e.g., source code) associated with the project. If these are online give a link to an appropriate external repository or to uploaded media files under this name space (2023-17).