通过波段选择进行光谱重建预测小肠出血

Small intestinal bleeding prediction by spectral reconstruction through band selection.

作者信息

Kuo Hsin-Yu, Karmakar Riya, Mukundan Arvind, Chou Chu-Kuang, Chen Tsung-Hsien, Huang Chien-Wei, Yang Kai-Yao, Wang Hsiang-Chen

机构信息

National Cheng Kung University Hospital, National Cheng Kung University, College of Medicine, Department of Internal Medicine, Tainan City, Taiwan.

National Chung Cheng University, Department of Mechanical Engineering, Chiayi, Taiwan.

出版信息

J Biomed Opt. 2025 Mar;30(3):036004. doi: 10.1117/1.JBO.30.3.036004. Epub 2025 Mar 19.

DOI:10.1117/1.JBO.30.3.036004

PMID:40110550

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11922165/

Abstract

SIGNIFICANCE

The identification of gastrointestinal bleeding holds significant importance in wireless capsule endoscopy examinations, primarily because bleeding is the most prevalent anomaly within the gastrointestinal tract. Moreover, gastrointestinal bleeding serves as a crucial indicator or manifestation of various other gastrointestinal disorders, including ulcers, polyps, tumors, and Crohn's disease. Gastrointestinal bleeding may be classified into two categories: active bleeding, which refers to the presence of continuing bleeding, and inactive bleeding, which can potentially manifest in any region of the gastrointestinal system. Currently, medical professionals diagnose gastrointestinal bleeding mostly by examining complete wireless capsule endoscopy images. This approach is known to be demanding in terms of labor and time.

AIM

This research used white-light images (WLIs) obtained from 100 patients using the PillCam™ SB 3 capsule endoscope to identify and label the areas of bleeding seen in the WLIs.

APPROACH

A total of 152 photographs depicting bleeding and 182 images depicting non-bleeding were selected for analysis. In addition, hyperspectral imaging was used to transform WLI into hyperspectral images using spectral reconstruction through band selection. These images were then categorized into WLIs and hyperspectral images. The training set consisted of seven datasets, each including six spectra. These datasets were used to train the Visual Geometry Group-16 (VGG-16) model, which was developed using a convolutional neural network. Subsequently, the model was tested, and its diagnostic accuracy was assessed.

RESULTS

The accuracy rates for the respective measures are 83.1%, 65.8%, 66.2%, 72.2%, 73.7%, and 88%. The respective precision values are 78.5%, 47.5%, 30.6%, 59.5%, 77.7%, and 80.2%. The recall rates for the relevant data points are 83.3%, 67.9%, 86%, 74.2%, 68.6%, and 92.4%. The initial dataset comprises an image captured under white-light conditions, whereas the final dataset is the most refined spectral picture data.

CONCLUSIONS

The findings suggest that employing spectral imaging within the wavelength range of 405 to 415 nm can enhance the accuracy of detecting small intestinal bleeding.

摘要

意义

在无线胶囊内镜检查中，胃肠道出血的识别具有重要意义，主要是因为出血是胃肠道内最常见的异常情况。此外，胃肠道出血是包括溃疡、息肉、肿瘤和克罗恩病在内的各种其他胃肠道疾病的关键指标或表现形式。胃肠道出血可分为两类：活动性出血，指持续出血的情况；非活动性出血，可能出现在胃肠道系统的任何区域。目前，医学专业人员大多通过检查完整的无线胶囊内镜图像来诊断胃肠道出血。众所周知，这种方法在人力和时间方面要求较高。

目的

本研究使用从100名患者身上通过PillCam™ SB 3胶囊内镜获取的白光图像（WLI），来识别和标记WLI中可见的出血区域。

方法

总共选择了152张描绘出血的照片和182张描绘非出血的图像进行分析。此外，使用高光谱成像通过波段选择进行光谱重建，将WLI转换为高光谱图像。这些图像随后被分类为WLI和高光谱图像。训练集由七个数据集组成，每个数据集包含六个光谱。这些数据集用于训练使用卷积神经网络开发的视觉几何组16（VGG - 16）模型。随后，对该模型进行测试，并评估其诊断准确性。

结果

各项测量的准确率分别为83.1%、65.8%、6б.2%、72.2%、73.7%和88%。各自的精确值分别为78.5%、47.5%、30.6%、59.5%、77.7%和80.2%。相关数据点的召回率分别为83.3%、67.9%、86%、74.2%、68.6%和92.4%。初始数据集包括在白光条件下拍摄的图像，而最终数据集是最精细的光谱图像数据。