Deep learning diagnostics for bladder tumor identification and grade prediction using RGB method.

We evaluate the diagnostic performance of deep learning artificial intelligence (AI) for bladder cancer, which used white-light images (WLIs) and narrow-band images, and tumor grade prediction of AI based on tumor color using the red/green/blue (RGB) method. This retrospective study analyzed 10,991 cystoscopic images of suspicious bladder tumors using a mask region-based convolutional neural network with a ResNeXt-101-32 × 8d-FPN backbone. The diagnostic performance of AI was evaluated by calculating sensitivity, specificity, and diagnostic accuracy, and its ability to detect cancers was investigated using the dice score coefficient (DSC). Using the support vector machine model, we analyzed differences in tumor colors according to tumor grade using the RGB method. The sensitivity, specificity, diagnostic accuracy and DSC of AI were 95.0%, 93.7%, 94.1% and 74.7%. In WLIs, there were differences in red and blue values according to tumor grade (p < 0.001). According to the average RGB value, the performance was ≥ 98% for the diagnosis of benign vs. low-and high-grade tumors using WLIs and > 90% for the diagnosis of chronic non-specific inflammation vs. carcinoma in situ using WLIs. The diagnostic performance of the AI-assisted diagnosis was of high quality, and the AI could distinguish the tumor grade based on tumor color.

Application of hierarchical MFI zeolite for the catalytic pyrolysis of Japanese larch.

The catalytic pyrolysis of Japanese larch was carried out over a hierarchical MFI zeolite (Meso MFI C16). The zeolite was synthesized using an amphiphilic organosilane as a mesopore-directing agent, and its catalytic activity was compared with that of the conventional HZSM-5 and the mesoporous material from HZSM-5 (MMZ(ZSM-5)). The effect of the hierarchical MFI zeolite on the product distribution and chemical composition of the bio-oil was also examined. The hierarchical MFI zeolite exhibited the highest activity in deoxygenation and aromatization during the catalytic pyrolysis of Japanese larch. In particular, it showed high selectivity for valuable aromatics, such as benzene, toluene, and xylenes (BTX), even though it decreased the organic fraction of bio-oil. Its higher mesoporosity resulted, however, in an increase in the coke amount and in undesirable products, such as polycyclic aromatic hydrocarbons (PAHs).