Introduction: Technical burdens and time-intensive review processes limit the
practical utility of video capsule endoscopy (VCE). Artificial intelligence
(AI) is poised to address these limitations, but the intersection of AI and VCE
reveals challenges that must first be overcome. We identified five challenges
to address. Challenge #1: VCE data are stochastic and contains significant
artifact. Challenge #2: VCE interpretation is cost-intensive. Challenge #3: VCE
data are inherently imbalanced. Challenge #4: Existing VCE AIMLT are
computationally cumbersome. Challenge #5: Clinicians are hesitant to accept
AIMLT that cannot explain their process.
Methods: An anatomic landmark detection model was used to test the
application of convolutional neural networks (CNNs) to the task of classifying
VCE data. We also created a tool that assists in expert annotation of VCE data.
We then created more elaborate models using different approaches including a
multi-frame approach, a CNN based on graph representation, and a few-shot
approach based on meta-learning.
Results: When used on full-length VCE footage, CNNs accurately identified
anatomic landmarks (99.1%), with gradient weighted-class activation mapping
showing the parts of each frame that the CNN used to make its decision. The
graph CNN with weakly supervised learning (accuracy 89.9%, sensitivity of
91.1%), the few-shot model (accuracy 90.8%, precision 91.4%, sensitivity
90.9%), and the multi-frame model (accuracy 97.5%, precision 91.5%, sensitivity
94.8%) performed well. Discussion: Each of these five challenges is addressed,
in part, by one of our AI-based models. Our goal of producing high performance
using lightweight models that aim to improve clinician confidence was achieved.

研究介绍了视频胶囊内窥镜、人工智能以及 VCE 数据解释等主题，使用卷积神经网络准确识别解剖标记，以轻量级模型提高临床医生信心。

胶囊内镜和人工智能的交叉领域：利用机器学习应对独特挑战

The intersection of video capsule endoscopy and artificial intelligence:  addressing unique challenges using machine learning

Automatic localization and labeling of vertebra in 3D medical images plays an
important role in many clinical tasks, including pathological diagnosis,
surgical planning and postoperative assessment. However, the unusual conditions
of pathological cases, such as the abnormal spine curvature, bright visual
imaging artifacts caused by metal implants, and the limited field of view,
increase the difficulties of accurate localization. In this paper, we propose
an automatic and fast algorithm to localize and label the vertebra centroids in
3D CT volumes. First, we deploy a deep image-to-image network (DI2IN) to
initialize vertebra locations, employing the convolutional encoder-decoder
architecture together with multi-level feature concatenation and deep
supervision. Next, the centroid probability maps from DI2IN are iteratively
evolved with the message passing schemes based on the mutual relation of
vertebra centroids. Finally, the localization results are refined with sparsity
regularization. The proposed method is evaluated on a public dataset of 302
spine CT volumes with various pathologies. Our method outperforms other
state-of-the-art methods in terms of localization accuracy. The run time is
around 3 seconds on average per case. To further boost the performance, we
retrain the DI2IN on additional 1000+ 3D CT volumes from different patients. To
the best of our knowledge, this is the first time more than 1000 3D CT volumes
with expert annotation are adopted in experiments for the anatomic landmark
detection tasks. Our experimental results show that training with such a large
dataset significantly improves the performance and the overall identification
rate, for the first time by our knowledge, reaches 90 %.

本研究提出一种自动定位和标记三维医学图像中椎体中心的算法，使用 DI2IN 进行初始化，使用基于消息传递方案的聚类方法和稀疏性约束进行迭代演进，最终使用稀疏性正则化进行结果优化。该算法在多个病理学数据集上表现出良好的性能。