Computer-aided Veress needle guidance using endoscopic optical coherence tomography and convolutional neural networks

Wang, Chen; Reynolds, Justin C.; Calle, Paul; Ladymon, Avery D.; Yan, Feng; Yan, Yuyang; Ton, Sam; Fung, Kar‐ming; Patel, Sanjay G.; Yu, Zhongxin; Pan, Chongle; Tang, Qinggong

doi:10.1002/jbio.202100347

Title: Computer-aided Veress needle guidance using endoscopic optical coherence tomography and convolutional neural networks

Journal Article · 31 January 2022 · Journal of Biophotonics

DOI: https://doi.org/10.1002/jbio.202100347 · OSTI ID:1981414

^[1]; Reynolds, Justin C. ^[1]; Calle, Paul ^[1]; Ladymon, Avery D. ^[1]; Yan, Feng ^[1]; Yan, Yuyang ^[1]; Ton, Sam ^[1]; Fung, Kar‐ming ^[2]; Patel, Sanjay G. ^[2]; Yu, Zhongxin ^[2];

^[1]; Tang, Qinggong ^[1]

University of Oklahoma, Norman, OK (United States)
University of Oklahoma Health Sciences Center, Oklahoma City, OK (United States)

During laparoscopic surgery, the Veress needle is commonly used in pneumoperitoneum establishment. Precise placement of the Veress needle is still a challenge for the surgeon. In this study, a computer-aided endoscopic optical coherence tomography (OCT) system was developed to effectively and safely guide Veress needle insertion. This endoscopic system was tested by imaging subcutaneous fat, muscle, abdominal space, and the small intestine from swine samples to simulate the surgical process, including the situation with small intestine injury. Each tissue layer was visualized in OCT images with unique features and subsequently used to develop a system for automatic localization of the Veress needle tip by identifying tissue layers (or spaces) and estimating the needle-to-tissue distance. We used convolutional neural networks (CNNs) in automatic tissue classification and distance estimation. In conclusion, the average testing accuracy in tissue classification was 98.53 ± 0.39%, and the average testing relative error in distance estimation reached 4.42 ± 0.56% (36.09 ± 4.92 μm).

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)

Sponsoring Organization:: National Institutes of Health (NIH); USDOE Office of Science (SC)

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1981414

Journal Information:: Journal of Biophotonics, Journal Name: Journal of Biophotonics Journal Issue: 5 Vol. 15; ISSN 1864-063X

Publisher:: WileyCopyright Statement

Country of Publication:: United States

Language:: English

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59 BASIC BIOLOGICAL SCIENCES
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deep-learning
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Title: Computer-aided Veress needle guidance using endoscopic optical coherence tomography and convolutional neural networks

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