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Acquiring large amounts of hyperspectral data of small intestinal tissue with real labels in the clinic is difficult, and the data shows inter‐patient variability. Building an automatic identification model using a small dataset presents a crucial challenge in obtaining a strong generalization of the model. This study aimed to explore the performance of hyperspectral imaging and transfer learning techniques in the automatic identification of normal and ischemic necrotic sites in small intestinal tissue. Hyperspectral data of small intestinal tissues were collected from eight white rabbit samples. The transfer component analysis (TCA) method was performed to transfer learning on hyperspectral data between different samples and the variability of data distribution between samples was reduced. The results showed that the TCA transfer learning method improved the accuracy of the classification model with less training data. This study provided a reliable method for single‐sample modelling to detect necrotic sites in small intestinal tissue. [ABSTRACT FROM AUTHOR]

Obtaining adequate resection margins in small intestinal necrotic tissue remains challenging due to the lack of intraoperative feedback. Here, we used hyperspectral imaging (HSI), an imaging technique for objective identification, combined with deep learning methods for automated small intestine tissue classification. As part of a prospective experimental study, we recorded hyperspectral datasets of small intestine biopsies from seven white rabbits. Based on the differences in the spectral characteristics of normal and ischemic necrotic small intestinal tissues in the wavelength range of 400–1000 nm, we applied deep learning techniques to objectively distinguish between these two types of tissues. The results showed that three-dimensional convolutional neural networks were more effective in extracting both spectral and spatial features of small intestine tissue hyperspectral data for classification. The combination of a deep learning model and HSI provided a new idea for the objective identification of ischemic necrotic tissue in the small intestine. [ABSTRACT FROM AUTHOR]

Objective and automatic clinical discrimination of normal and necrotic sites of small intestinal tissue remains challenging. In this study, hyperspectral imaging (HSI) and unsupervised classification techniques were used to distinguish normal and necrotic sites of small intestinal tissues. Small intestinal tissue hyperspectral images of eight Japanese large‐eared white rabbits were acquired using a visible near‐infrared hyperspectral camera, and K‐means and density peaks (DP) clustering algorithms were used to differentiate between normal and necrotic tissue. The three cases in this study showed that the average clustering purity of the DP clustering algorithm reached 92.07% when the two band combinations of 500–622 and 700–858 nm were selected. The results of this study suggest that HSI and DP clustering can assist physicians in distinguishing between normal and necrotic sites in the small intestine in vivo. [ABSTRACT FROM AUTHOR]

As the most important refraction part in the optical system,cornea possesses characteristics which are important parameters in ophthalmology clinical surgery.During the measurement of the cornea in our study,we acquired the corneal data of Orbscan Ⅱ corneal topographer in real time using the Hook technology under Windows,and then took the data into the corneal analysis software.We then further analyzed and calculated the data to obtain individual Q-value of overall corneal 360semi-meridian.The corneal analysis software took Visual C++ 6.0as development environment,used OpenGL graphics technology to draw three-dimensional individual corneal morphological map and the distribution curve of the Q-value,and achieved real-time corneal data query.It could be concluded that the analysis would further extend the function of the corneal topography system,and provide a solid foundation for the further study of automatic screening of corneal diseases.