Deep Learning for Classification of Pediatric Otitis Media.

OBJECTIVES/HYPOTHESIS: To create a new strategy for monitoring pediatric otitis media (OM), we developed a brief, reliable, and objective method for automated classification using convolutional neural networks (CNNs) with images from otoscope. STUDY DESIGN: Prospective study. METHODS: An otoscopic image classifier for pediatric OM was built upon the idea of deep learning and transfer learning using the two most widely used CNN architectures named Xception and MobileNet-V2. Otoscopic images, including acute otitis media (AOM), otitis media with effusion (OME), and normal ears were obtained from our institution. Among qualified otoendoscopic images, 10,703 images were used for training, and 1,500 images were used for testing. In addition, 102 images captured by smartphone with WI-FI connected otoscope were used as a prospective test set to evaluate the model for home screening and monitoring. RESULTS: For all diagnoses combined in the test set, the Xception model and the MobileNet-V2 model had similar overall accuracies of 97.45% (95% CI 96.81%-97.94%) and 95.72% (95% CI 95.12%-96.16%). The overall accuracies of two models with smartphone images were 90.66% (95% CI 90.21%-90.98%) and 88.56% (95% CI 87.86%-90.05%). The class activation map results showed that the extracted features of smartphone images were the same as those of otoendoscopic images. CONCLUSIONS: We have developed deep learning algorithms for the successfully automated classification of pediatric AOM and OME with otoscopic images. With a smartphone-enabled wireless otoscope, artificial intelligence may assist parents in early detection and continuous monitoring at home to decrease the visit frequencies. LEVEL OF EVIDENCE: NA Laryngoscope, 131:E2344-E2351, 2021.

3 D Hierarchical Porous Carbon for Supercapacitors Prepared from Lignin through a Facile Template-Free Method.

Lignin-derived hierarchical porous carbon (LHPC) was prepared through a facile template-free method. Solidification of the lignin-KOH solution resulted in KOH crystalizing within lignin. The crystalized KOH particles in solid lignin acted both as template and activating agent in the heat-treatment process. The obtained LHPC, exhibiting a 3D network, consisted of macroporous cores, mesoporous channels, and micropores. The LHPC comprised 12.27 at % oxygen-containing groups, which resulted in pseudocapacitance. The LHPC displayed a capacitance of 165.0 F g(-1) in 1 M H2 SO4 at 0.05 A g(-1) , and the capacitance was still 123.5 F g(-1) even at 10 A g(-1) . The LHPC also displayed excellent cycling stability with capacitance retention of 97.3 % after 5000 galvanostatic charge-discharge cycles. On account of the facile preparation of LHPC, this paper offers a facile alternative method for the preparation of hierarchical porous carbon for electrochemical energy storage devices.