Maguluri, G.; Grimble, J.; Caron, A.; Zhu, G.; Krishnamurthy, S.; McWatters, A.; Beamer, G.; Lee, S.-Y.; Iftimia, N. Core Needle Biopsy Guidance Based on Tissue Morphology Assessment with AI-OCT Imaging. Diagnostics2023, 13, 2276.
Maguluri, G.; Grimble, J.; Caron, A.; Zhu, G.; Krishnamurthy, S.; McWatters, A.; Beamer, G.; Lee, S.-Y.; Iftimia, N. Core Needle Biopsy Guidance Based on Tissue Morphology Assessment with AI-OCT Imaging. Diagnostics 2023, 13, 2276.
Maguluri, G.; Grimble, J.; Caron, A.; Zhu, G.; Krishnamurthy, S.; McWatters, A.; Beamer, G.; Lee, S.-Y.; Iftimia, N. Core Needle Biopsy Guidance Based on Tissue Morphology Assessment with AI-OCT Imaging. Diagnostics2023, 13, 2276.
Maguluri, G.; Grimble, J.; Caron, A.; Zhu, G.; Krishnamurthy, S.; McWatters, A.; Beamer, G.; Lee, S.-Y.; Iftimia, N. Core Needle Biopsy Guidance Based on Tissue Morphology Assessment with AI-OCT Imaging. Diagnostics 2023, 13, 2276.
Abstract
This paper presents a combined optical imaging/artificial intelligence (OI/AI) technique for real-time analysis of tissue morphology at the tip of the biopsy needle, prior collecting a biopsy specimen. This is an important clinical problem as up to 40% of the collected biopsy cores provide low diagnostic value due to high adipose or necrotic content. Micron-scale resolution optical coherence tomography (OCT) images can be collected with a minimally invasive needle probe and automatically analyzed by a computer neural network (CNN)-based AI software. The results can be conveyed to the clinician in real-time and used to select the biopsy location more adequately. This technology was evaluated on a rabbit model of cancer. OCT images were collected with a hand-held custom-made OCT probe. Annotated OCT images were used as ground truth for AI algorithm training. The overall performance of the AI model was very close to that of the humans performing the same classification tasks. Specifically, tissue segmentation was excellent (~99% accuracy) and provided segmentation that closely mimicked ground truth provided by the human annotations, while over 83% correlation accuracy was obtained for tumor and non-tumor classification.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
