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Published on: July 21, 2023
Small intestinal bleeding prediction by spectral reconstruction through band selection
Hsin-Yu Kuo1, Riya Karmakar2, Arvind Mukundan2
1National Cheng Kung University Hospital, National Cheng Kung University, College of Medicine, Department of Internal Medicine, Tainan City, Taiwan.
Insights
Detecting gastrointestinal bleeding, a common issue in capsule endoscopy, is improved by using spectral imaging. This technique enhances accuracy in identifying bleeding in the small intestine.
Area of Science:
- Gastroenterology and Medical Imaging
- Artificial Intelligence in Medicine
Background:
- Gastrointestinal bleeding is a prevalent anomaly and critical indicator of various gastrointestinal disorders.
- Current diagnosis relies on manual review of wireless capsule endoscopy images, which is labor-intensive and time-consuming.
Purpose of the Study:
- To identify and label gastrointestinal bleeding using white-light images (WLIs) from capsule endoscopy.
- To evaluate the efficacy of hyperspectral imaging in enhancing bleeding detection accuracy.
Main Methods:
- Utilized WLIs from 100 patients undergoing capsule endoscopy (PillCam™ SB 3).
- Transformed WLIs into hyperspectral images using spectral reconstruction.
- Trained a Visual Geometry Group-16 (VGG-16) convolutional neural network model on spectral and WLI datasets.
- Assessed diagnostic accuracy, precision, and recall rates.
Main Results:
- Achieved accuracy rates ranging from 65.8% to 88% across different datasets.
- Demonstrated higher recall rates for spectral image data (up to 92.4%) compared to initial WLI.
- The most refined spectral picture data yielded the highest performance metrics.
Conclusions:
- Hyperspectral imaging, particularly within the 405-415 nm wavelength range, significantly improves the accuracy of detecting small intestinal bleeding.
- This spectral imaging approach offers a more efficient and accurate method for diagnosing gastrointestinal bleeding compared to traditional WLI analysis.
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.
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