Chest computed tomography-based artificial intelligence-aided latent class analysis for diagnosis of severe pneumonia.

Background: There is little literature describing the artificial intelligence (AI)-aided diagnosis of severe pneumonia (SP) subphenotypes and the association of the subphenotypes with the ventilatory treatment efficacy. The aim of our study is to illustrate whether clinical and biological heterogeneity, such as ventilation and gas-exchange, exists among patients with SP using chest computed tomography (CT)-based AI-aided latent class analysis (LCA).

Methods: This retrospective study included 413 patients hospitalized at Xinhua hospital diagnosed with SP from June 1, 2015 to May 30, 2020.

Calibration of a Catadioptric System and 3D Reconstruction Based on Surface Structured Light.

In response to the problem of the small field of vision in 3D reconstruction, a 3D reconstruction system based on a catadioptric camera and projector was built by introducing a traditional camera to calibrate the catadioptric camera and projector system. Firstly, the intrinsic parameters of the camera and the traditional camera are calibrated separately. Then, the calibration of the projection system is accomplished by the traditional camera.

High-Resolution Drone Detection Based on Background Difference and SAG-YOLOv5s.

To solve the problem of low accuracy and slow speed of drone detection in high-resolution images with fixed cameras, we propose a detection method combining background difference and lightweight network SAG-YOLOv5s. First, background difference is used to extract potential drone targets in high-resolution images, eliminating most of the background to reduce computational overhead. Secondly, the Ghost module and SimAM attention mechanism are introduced on the basis of YOLOv5s to reduce the total number of model parameters and improve feature extraction, and -DIoU loss is used to replace the original DIoU loss to improve the accuracy of bounding box regression.

Prognostic and predictive value of radiomic signature in stage I lung adenocarcinomas following complete lobectomy.

Background: The overall survival (OS) of stage I operable lung cancer is relatively low, and not all patients can benefit from adjuvant chemotherapy. This study aimed to develop and validate a radiomic signature (RS) for prediction of OS and adjuvant chemotherapy candidates in stage I lung adenocarcinoma.

Methods: A total of 474 patients from 2 centers were divided into 1 training (n = 287), 1 internal validation (n = 122), and 1 external validation (n = 65) cohorts.

Comprehensive profiling of 1015 patients' exomes reveals genomic-clinical associations in colorectal cancer.

The genetic basis of colorectal cancer (CRC) and its clinical associations remain poorly understood due to limited samples or targeted genes in current studies. Here, we perform ultradeep whole-exome sequencing on 1015 patients with CRC as part of the ChangKang Project. We identify 46 high-confident significantly mutated genes, 8 of which mutate in 14.

Dependence of radiomic features on pixel size affects the diagnostic performance of radiomic signature for the invasiveness of pulmonary ground-glass nodule.

Objective: To investigate the effect of reducing pixel size on the consistency of radiomic features and the diagnostic performance of the downstream radiomic signatures for the invasiveness for pulmonary ground-glass nodules (GGNs) on CTs.

Methods: We retrospectively collected the clinical data of 182 patients with GGNs on high resolution CT (HRCT). The CT images of different pixel sizes (0.