Core Needle Biopsy Guidance Based on Tissue Morphology Assessment with AI-OCT Imaging
This paper presents a combined optical imaging/artificial intelligence (OI/AI) technique for the real-time analysis of tissue morphology at the tip of the biopsy needle, prior to collecting a biopsy specimen. This is an important clinical problem as up to 40% of collected biopsy cores provide low di...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-07-01
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| Series: | Diagnostics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4418/13/13/2276 |
| _version_ | 1797591905444298752 |
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| author | Gopi Maguluri John Grimble Aliana Caron Ge Zhu Savitri Krishnamurthy Amanda McWatters Gillian Beamer Seung-Yi Lee Nicusor Iftimia |
| author_facet | Gopi Maguluri John Grimble Aliana Caron Ge Zhu Savitri Krishnamurthy Amanda McWatters Gillian Beamer Seung-Yi Lee Nicusor Iftimia |
| author_sort | Gopi Maguluri |
| collection | DOAJ |
| description | This paper presents a combined optical imaging/artificial intelligence (OI/AI) technique for the real-time analysis of tissue morphology at the tip of the biopsy needle, prior to collecting a biopsy specimen. This is an important clinical problem as up to 40% of 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 using 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 the ground truth provided by the human annotations, while over 84% correlation accuracy was obtained for tumor and non-tumor classification. |
| first_indexed | 2024-03-11T01:44:03Z |
| format | Article |
| id | doaj.art-79fe6b81d5f94991b04c06480eaee72d |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-11T01:44:03Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-79fe6b81d5f94991b04c06480eaee72d2023-11-18T16:22:28ZengMDPI AGDiagnostics2075-44182023-07-011313227610.3390/diagnostics13132276Core Needle Biopsy Guidance Based on Tissue Morphology Assessment with AI-OCT ImagingGopi Maguluri0John Grimble1Aliana Caron2Ge Zhu3Savitri Krishnamurthy4Amanda McWatters5Gillian Beamer6Seung-Yi Lee7Nicusor Iftimia8Physical Sciences Inc., Andover, MA 01810, USAPhysical Sciences Inc., Andover, MA 01810, USAPhysical Sciences Inc., Andover, MA 01810, USAPhysical Sciences Inc., Andover, MA 01810, USAMD Anderson Cancer Center, Houston, TX 77030, USAMD Anderson Cancer Center, Houston, TX 77030, USAAiforia Inc., Cambridge, MA 02142, USAAiforia Inc., Cambridge, MA 02142, USAPhysical Sciences Inc., Andover, MA 01810, USAThis paper presents a combined optical imaging/artificial intelligence (OI/AI) technique for the real-time analysis of tissue morphology at the tip of the biopsy needle, prior to collecting a biopsy specimen. This is an important clinical problem as up to 40% of 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 using 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 the ground truth provided by the human annotations, while over 84% correlation accuracy was obtained for tumor and non-tumor classification.https://www.mdpi.com/2075-4418/13/13/2276tissue biopsy guidanceoptical coherence tomography imagingartificial intelligence |
| spellingShingle | Gopi Maguluri John Grimble Aliana Caron Ge Zhu Savitri Krishnamurthy Amanda McWatters Gillian Beamer Seung-Yi Lee Nicusor Iftimia Core Needle Biopsy Guidance Based on Tissue Morphology Assessment with AI-OCT Imaging Diagnostics tissue biopsy guidance optical coherence tomography imaging artificial intelligence |
| title | Core Needle Biopsy Guidance Based on Tissue Morphology Assessment with AI-OCT Imaging |
| title_full | Core Needle Biopsy Guidance Based on Tissue Morphology Assessment with AI-OCT Imaging |
| title_fullStr | Core Needle Biopsy Guidance Based on Tissue Morphology Assessment with AI-OCT Imaging |
| title_full_unstemmed | Core Needle Biopsy Guidance Based on Tissue Morphology Assessment with AI-OCT Imaging |
| title_short | Core Needle Biopsy Guidance Based on Tissue Morphology Assessment with AI-OCT Imaging |
| title_sort | core needle biopsy guidance based on tissue morphology assessment with ai oct imaging |
| topic | tissue biopsy guidance optical coherence tomography imaging artificial intelligence |
| url | https://www.mdpi.com/2075-4418/13/13/2276 |
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