Maguluri Gopi, Grimble John, Caron Aliana, Zhu Ge, Krishnamurthy Savitri, McWatters Amanda, Beamer Gillian, Lee Seung-Yi, Iftimia Nicusor
Physical Sciences Inc., Andover, MA 01810, USA.
MD Anderson Cancer Center, Houston, TX 77030, USA.
Diagnostics (Basel). 2023 Jul 5;13(13):2276. doi: 10.3390/diagnostics13132276.
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.
本文介绍了一种光学成像/人工智能（OI/AI）联合技术，用于在采集活检样本之前对活检针尖端的组织形态进行实时分析。这是一个重要的临床问题，因为高达40%的采集活检样本由于脂肪或坏死成分含量高而诊断价值低。微米级分辨率的光学相干断层扫描（OCT）图像可以通过微创针探头采集，并使用基于计算机神经网络（CNN）的人工智能软件进行自动分析。结果可以实时传达给临床医生，并用于更充分地选择活检位置。该技术在兔癌模型上进行了评估。使用手持式定制OCT探头采集OCT图像。带注释的OCT图像用作人工智能算法训练的基准真值。人工智能模型的整体性能与执行相同分类任务的人类非常接近。具体而言，组织分割非常出色（准确率约为99%），提供的分割与人类注释提供的基准真值非常相似，而肿瘤和非肿瘤分类的相关准确率超过84%。
