Iterative prototyping based on lessons learned from the falloposcope in vivo pilot study experience
Andrew D Rocha1, William K Drake1, Photini F Rice2
1The University of Arizona, Wyant College of Optical Science, Tucson, Arizona, United States.
Early detection of ovarian cancer is crucial. A novel falloposcope using multispectral fluorescence imaging and optical coherence tomography successfully imaged fallopian tubes, paving the way for earlier diagnosis.
Area of Science:
- Gynecological oncology
- Medical imaging
- Biomedical engineering
Background:
- High-grade serous ovarian cancer, the deadliest gynecological cancer, often originates in the fallopian tubes (FTs).
- Early detection significantly improves survival rates, but current methods are limited.
- Autofluorescence imaging, multispectral fluorescence imaging (MFI), and optical coherence tomography (OCT) show promise for detecting precancerous and cancerous lesions in FTs and ovaries.
Purpose of the Study:
- To develop and evaluate a novel falloscope for in vivo imaging of the fallopian tubes (FTs) using MFI and OCT.
- To assess the safety and feasibility of FT imaging prior to salpingectomy in a pilot study.
- To describe iterative improvements made to the falloscope based on clinical feedback.
Main Methods:
- Development of a clinical prototype falloscope with a diameter.
- Minimally invasive introduction via a hysteroscope and catheter.
- Acquisition of navigation video, MFI, and OCT images of human FTs in 12 volunteers.
Main Results:
- The falloscope successfully acquired in vivo images of the fallopian tubes.
- Iterative improvements enhanced device robustness, usability, and image quality.
- Feedback from stakeholders guided modifications to the imaging system and accessories.
Conclusions:
- The falloposcope is a feasible tool for in vivo imaging of the fallopian tubes.
- Iterative prototyping has significantly improved the device's robustness and functionality.
- Further trials are warranted to establish the falloscope's role in early ovarian cancer detection.
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