The insertion of conventional colonoscopes can sometimes cause patients to experience pain during the procedure owing to the stretching of the mesentery. In this study, a prototype of a robotic colonoscope with a double-balloon and double-bend tube based on the conventional double-balloon endoscope was developed to simplify insertion and prevent the overstretching of the colon. Both the outer and inner tubes were confirmed to be free from interference from wires and sheaths. Additionally, all functions such as tip bending, inflation and deflation of the balloons, and actuator-driven pulling and pushing of the inner tube were operated properly. During the insertion test, the device could be reached the cecum of a colon model in approximately 442 s when operated by a non-medical operator. In addition, the device did not overstretch the colon model, thereby suggesting that the insertion mechanism can follow the shape of the colon model. As a result, the developed mechanism has the potential to navigate through a highly-bent colon without overstretching.

The insertion of conventional colonoscopes can result in the patient experiencing pain during the procedure owing to the stretching of the mesentery. In this study, a prototype of a robotic colonoscope with a double-balloon and double-bend tube based on the conventional double-balloon endoscope was developed to simplify insertion and prevent the overstretching of the colon. The outer and inner tubes were confirmed to be free from interference owing to wires and sheaths. Additionally, each function such as tip bending, inflation and deflation of the balloons, and pulling and pushing of the inner tube by the actuator could be operated properly. During the insertion test, the device could be reached the cecum of a colon model in approximately 442 s when operated by a non-medical operator. In addition, the device did not overstretch the colon model, thereby suggesting that the insertion mechanism can follow the shape of the colon model. Therefore, the developed mechanism can potentially pass through the highly-bent colon without overstretching.

<p>The Contrastive Learning for blind Hyperspectral Unmixing (CLHU) is a self-supervised deep learning approach for blind hyperspectral unmixing. Unlike existing deep learning methods that rely on reconstruction capabilities, CLHU leverages the input-endmembers relationship for abundance estimation. The endmembers are dynamically updated during training, controlled by two regularization factors. Extensive experiments demonstrate CLHU's effectiveness, achieving state-of-the-art performance in hyperspectral unmixing. This novel approach offers a promising perspective for the field and holds potential for further enhancements in hyperspectral unmixing tasks.</p>

<p>The Contrastive Learning for blind Hyperspectral Unmixing (CLHU) is a self-supervised deep learning approach for blind hyperspectral unmixing. Unlike existing deep learning methods that rely on reconstruction capabilities, CLHU leverages the input-endmembers relationship for abundance estimation. The endmembers are dynamically updated during training, controlled by two regularization factors. Extensive experiments demonstrate CLHU's effectiveness, achieving state-of-the-art performance in hyperspectral unmixing. This novel approach offers a promising perspective for the field and holds potential for further enhancements in hyperspectral unmixing tasks.</p>

Two main features of Near-infrared (NIR) light are the ability to perform component analysis based on spectral differences and to have permeability to biological tissues. These features make the technology to acquire NIR spectral of the deep lesion and analyze the components by each pixel, called hyperspectral imaging (HSI). Mounting this technology to a laparoscope enables visualization of invisible or looking-similar tissues in visible light during laparoscopic surgery. In this research, the developed NIR-HSI laparoscopic device acquired NIR spectrum images on in vivo pig specimens. Through the experiments, the difference in spectrum between the artery and surrounding other tissues was confirmed. Additionally, a machine learning procedure provided high accuracy detection of the artery area; accuracy, precision, and recall are 0.868 %, 0.921 %, and 0.637 % respectively.