Diagnosis model of early Pneumocystis jirovecii pneumonia based on convolutional neural network: a comparison with traditional PCR diagnostic method.

Background: Pneumocystis jirovecii pneumonia (PJP) is an interstitial pneumonia caused by pneumocystis jirovecii (PJ). The diagnosis of PJP primarily relies on the detection of the pathogen from lower respiratory tract specimens. However, it faces challenges such as difficulty in obtaining specimens and low detection rates.

Prototype early diagnostic model for invasive pulmonary aspergillosis based on deep learning and big data training.

Background: Currently, the diagnosis of invasive pulmonary aspergillosis (IPA) mainly depends on the integration of clinical, radiological and microbiological data. Artificial intelligence (AI) has shown great advantages in dealing with data-rich biological and medical challenges, but the literature on IPA diagnosis is rare.

Objective: This study aimed to provide a non-invasive, objective and easy-to-use AI approach for the early diagnosis of IPA.

Machine Learning for Predicting Individual Severity of Blepharospasm Using Diffusion Tensor Imaging.

Accumulating diffusion tensor imaging (DTI) evidence suggests that white matter abnormalities evaluated by local diffusion homogeneity (LDH) or fractional anisotropy (FA) occur in patients with blepharospasm (BSP), both of which are significantly correlated with disease severity. However, whether the individual severity of BSP can be identified using these DTI metrics remains unknown. We aimed to investigate whether a combination of machine learning techniques and LDH or FA can accurately identify the individual severity of BSP.

Multi-energy computed tomography reconstruction using a nonlocal spectral similarity model.

Multi-energy computed tomography (MECT) is able to acquire simultaneous multi-energy measurements from one scan. In addition, it allows material differentiation and quantification effectively. However, due to the limited energy bin width, the number of photons detected in an energy-specific channel is smaller than that in traditional CT, which results in image quality degradation.

[Adaptive regularized super-resolution reconstruction for magnetic resonance images].

To increase the resolution and signal-to-noise ratio (SNR) of magnetic resonance (MR) images, an adaptively regularized super-resolution reconstruction algorithm was proposed and applied to acquire high resolution MR images from 4 subpixel-shifted low resolution images on the same anatomical slice. The new regularization parameter, which allowed the cost function of the new algorithm to be locally convex within the definition region, was introduced by the piori information to enhance detail restoration of the image with a high frequency. The experiment results proved that the proposed algorithm was superior to other counterparts in achieving the reconstruction of low-resolution MR images.

[Adaptive super resolution algorithm for under-sampled images].

A new algorithm of adaptive super-resolution (SR) reconstruction based on the regularization parameter is proposed to reconstruct a high-resolution (HR) image from the low-resolution (LR) image sequence, which takes into full account the inaccurate estimates of motion error, point spread function (PSF) and the additive Gaussian noise in the LR image sequence. We established a novel nonlinear adaptive regularization function and analyzed experimentally its convexity to obtain the adaptive step size. This novel algorithm can effectively improve the spatial resolution of the image and the rate of convergence, which is verified by the experiment on optical images.