Diagnosis and segmentation effect of the ME-NBI-based deep learning model on gastric neoplasms in patients with suspected superficial lesions - a multicenter study.

Background: Endoscopically visible gastric neoplastic lesions (GNLs), including early gastric cancer and intraepithelial neoplasia, should be accurately diagnosed and promptly treated. However, a high rate of missed diagnosis of GNLs contributes to the potential risk of the progression of gastric cancer. The aim of this study was to develop a deep learning-based computer-aided diagnosis (CAD) system for the diagnosis and segmentation of GNLs under magnifying endoscopy with narrow-band imaging (ME-NBI) in patients with suspected superficial lesions. Methods: ME-NBI images of patients with GNLs in two centers were retrospectively analysed. Two convolutional neural network (CNN) modules were developed and trained on these images. CNN1 was trained to diagnose GNLs, and CNN2 was trained for segmentation. An additional internal test set and an external test set from another center were used to evaluate the diagnosis and segmentation performance. Results: CNN1 showed a diagnostic performance with an accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of 90.8%, 92.5%, 89.0%, 89.4% and 92.2%, respectively, and an area under the curve (AUC) of 0.928 in the internal test set. With CNN1 assistance, all endoscopists had a higher accuracy than for an independent diagnosis. The average intersection over union (IOU) between CNN2 and the ground truth was 0.5837, with a precision, recall and the Dice coefficient of 0.776, 0.983 and 0.867, respectively. Conclusions: This CAD system can be used as an auxiliary tool to diagnose and segment GNLs, assisting endoscopists in more accurately diagnosing GNLs and delineating their extent to improve the positive rate of lesion biopsy and ensure the integrity of endoscopic resection.

Highly Flexible, Efficient, and Sandwich-Structured Infrared Radiation Heating Fabric.

Controlling thermal energy is one of the biggest concerns along with the progress of human civilization for thousands of years. Current thermal comfort devices are mainly based on materials that are bulky, rigid, and heavy, largely limiting their widespread practical applications. It still remains a challenge to develop highly lightweight, flexible, and efficient electrical heaters for personal thermal management and local climate control. In this work, we present a high-performance composite infrared radiation heating fabric (IRHF), which mainly consists of two layers of poly(ethylene terephthalate) (PET) fabrics and one sandwiched layer of carbon nanofibers embedded with different inorganic nanoparticles. A copper electrode sheet was connected with the carbon nanofibers to form a conductive heating circuit. The permanent spontaneous polarization of both carbon nanofibers and infrared radiation nanoparticles can facilitate an enhanced current in the heater by creating an additional electrical field, which results in a fast electrothermal response and favorable heat preservation. The constructed IRHF could achieve an increase in the temperature to 43 °C from room temperature in 1 min under a voltage of 30 V, with an electrothermal conversion efficiency up to 78.99%. With a collection of compelling features such as good thermal stability, excellent flexibility and breathability, and high electrical conductivity and energy conversion efficiency, the fabricated sandwich-structured IRHF can open up new opportunities to develop smart heating textiles and wearable heating clothes in many fields.