Development of hybrid 3D printing approach for fabrication of high-strength hydroxyapatite bioscaffold using FDM and DLP techniques.

This study develops a hybrid 3D printing approach that combines fused deposition modeling (FDM) and digital light processing (DLP) techniques for fabricating bioscaffolds, enabling rapid mass production. The FDM technique fabricates outer molds, while DLP prints struts for creating penetrating channels. By combining these components, hydroxyapatite (HA) bioscaffolds with different channel sizes (600, 800, and 1000m) and designed porosities (10%, 12.

Hyperspectral Imaging Combined with Artificial Intelligence in the Early Detection of Esophageal Cancer.

This study uses hyperspectral imaging (HSI) and a deep learning diagnosis model that can identify the stage of esophageal cancer and mark the locations. This model simulates the spectrum data from the image using an algorithm developed in this study which is combined with deep learning for the classification and diagnosis of esophageal cancer using a single-shot multibox detector (SSD)-based identification system. Some 155 white-light endoscopic images and 153 narrow-band endoscopic images of esophageal cancer were used to evaluate the prediction model.

Endoscopic Images by a Single-Shot Multibox Detector for the Identification of Early Cancerous Lesions in the Esophagus: A Pilot Study.

Diagnosis of early esophageal neoplasia, including dysplasia and superficial cancer, is a great challenge for endoscopists. Recently, the application of artificial intelligence (AI) using deep learning in the endoscopic field has made significant advancements in diagnosing gastrointestinal cancers. In the present study, we constructed a single-shot multibox detector using a convolutional neural network for diagnosing different histological grades of esophageal neoplasms and evaluated the diagnostic accuracy of this computer-aided system.

Identification of Skin Lesions by Using Single-Step Multiframe Detector.

An artificial intelligence algorithm to detect mycosis fungoides (MF), psoriasis (PSO), and atopic dermatitis (AD) is demonstrated. Results showed that 10 s was consumed by the single shot multibox detector (SSD) model to analyze 292 test images, among which 273 images were correctly detected. Verification of ground truth samples of this research come from pathological tissue slices and OCT analysis.

Resting-state functional magnetic resonance imaging: the impact of regression analysis.

Purpose: To investigate the impact of regression methods on resting-state functional magnetic resonance imaging (rsfMRI). During rsfMRI preprocessing, regression analysis is considered effective for reducing the interference of physiological noise on the signal time course. However, it is unclear whether the regression method benefits rsfMRI analysis.

Automatic calibration of trigger delay time for cardiac MRI.

This study aimed to automatically identify the cardiac rest period using a rapid free-breathing (FB) calibration scanning procedure, and to determine the optimal trigger delay for cardiac imaging. A standard deviation (SD) method was used to rapidly identify cardiac quiescent phases employing multiphase cine cardiac images. The accuracy of this method was investigated using 46 datasets acquired from 22 healthy volunteers.