Construction of a clinical prediction model for complicated appendicitis based on machine learning techniques.

Acute appendicitis is a typical surgical emergency worldwide and one of the common causes of surgical acute abdomen in the elderly. Accurately diagnosing and differentiating acute appendicitis can assist clinicians in formulating a scientific and reasonable treatment plan and providing high-quality medical services for the elderly. In this study, we validated and analyzed the different performances of various machine learning models based on the analysis of clinical data, so as to construct a simple, fast, and accurate estimation method for the diagnosis of early acute appendicitis.

Analysis of the impact of radiotherapy and surgical treatment regimens based on the SEER database on the survival outcomes of rectal cancer patients over 70 years.

Objective: This study evaluates the impact of different combinations of treatment regimens, such as additional radiation, chemotherapy, and surgical treatments, on the survival of elderly rectal cancer patients ≥ 70 years of age to support physicians' clinical decision-making.

Methods: Data from a sample of elderly rectal cancer patients aged ≥ 70 years diagnosed from 2005-2015 from the US surveillance, epidemiology, and end results (SEER) database were retrospectively analyzed. The best cut-off point was selected using the x-tile software for the three continuity indices: age, tumor size, and number of regional lymph nodes.

CT medical image segmentation algorithm based on deep learning technology.

For the problems of blurred edges, uneven background distribution, and many noise interferences in medical image segmentation, we proposed a medical image segmentation algorithm based on deep neural network technology, which adopts a similar U-Net backbone structure and includes two parts: encoding and decoding. Firstly, the images are passed through the encoder path with residual and convolutional structures for image feature information extraction. We added the attention mechanism module to the network jump connection to address the problems of redundant network channel dimensions and low spatial perception of complex lesions.

Automatic polyp image segmentation and cancer prediction based on deep learning.

The similar shape and texture of colonic polyps and normal mucosal tissues lead to low accuracy of medical image segmentation algorithms. To solve these problems, we proposed a polyp image segmentation algorithm based on deep learning technology, which combines a HarDNet module, attention module, and multi-scale coding module with the U-Net network as the basic framework, including two stages of coding and decoding. In the encoder stage, HarDNet68 is used as the main backbone network to extract features using four null space convolutional pooling pyramids while improving the inference speed and computational efficiency; the attention mechanism module is added to the encoding and decoding network; then the model can learn the global and local feature information of the polyp image, thus having the ability to process information in both spatial and channel dimensions, to solve the problem of information loss in the encoding stage of the network and improving the performance of the segmentation network.

Longitudinal Changes in Functional Connectivity of the Caudate Is Associated With Recovery From Bell's Palsy.

Several studies have demonstrated through resting-state functional magnetic resonance imaging (fMRI) that functional connectivity changes are important in the recovery from Bell's palsy (BP); however, these studies have only focused on the cortico-cortical connectivity. It is unclear how corticostriatal connectivity relates to the recovery process of patients with BP. In the present study, we evaluated the relationship between longitudinal changes of caudate-based functional connectivity and longitudinal changes of facial performance in patients with intractable BP.

Aberrant Coupling Between Resting-State Cerebral Blood Flow and Functional Connectivity in Wilson's Disease.

Both abnormalities of resting-state cerebral blood flow (CBF) and functional connectivity in Wilson's disease (WD) have been identified by several studies. Whether the coupling of CBF and functional connectivity is imbalanced in WD remains largely unknown. To assess this possibility, 27 patients with WD and 27 sex- and age-matched healthy controls were recruited to acquire functional MRI and arterial spin labeling imaging data.