A Predictive Model Integrating AI Recognition Technology and Biomarkers for Lung Nodule Assessment.

Background:  Lung cancer is the most prevalent and lethal cancer globally, necessitating accurate differentiation between benign and malignant pulmonary nodules to guide treatment decisions. This study aims to develop a predictive model that integrates artificial intelligence (AI) analysis with biomarkers to enhance early detection and stratification of lung nodule malignancy.

Methods:  The study retrospectively analyzed the patients with pathologically confirmed pulmonary nodules.

Establishing an AI model and application for automated capsule endoscopy recognition based on convolutional neural networks (with video).

Background: Although capsule endoscopy (CE) is a crucial tool for diagnosing small bowel diseases, the need to process a vast number of images imposes a significant workload on physicians, leading to a high risk of missed diagnoses. This study aims to develop an artificial intelligence (AI) model and application based on convolutional neural networks that can automatically recognize various lesions in small bowel capsule endoscopy.

Methods: Three small bowel capsule endoscopy datasets were used for AI model training, validation, and testing, encompassing 12 categories of images.

Integrating AI with medical industry chain data: enhancing clinical nutrition research through semantic knowledge graphs.

In clinical nutrition research, the medical industry chain generates a wealth of multidimensional spatial data across various formats, including text, images, and semi-structured tables. This data's inherent heterogeneity and diversity present significant challenges for processing and mining, which are further compounded by the data's diverse features, which are difficult to extract. To address these challenges, we propose an innovative integration of artificial intelligence (AI) with the medical industry chain data, focusing on constructing semantic knowledge graphs and extracting core features.

SEF-UNet: advancing abdominal multi-organ segmentation with SEFormer and depthwise cascaded upsampling.

The abdomen houses multiple vital organs, which are associated with various diseases posing significant risks to human health. Early detection of abdominal organ conditions allows for timely intervention and treatment, preventing deterioration of patients' health. Segmenting abdominal organs aids physicians in more accurately diagnosing organ lesions.

AI support for colonoscopy quality control using CNN and transformer architectures.

Background: Construct deep learning models for colonoscopy quality control using different architectures and explore their decision-making mechanisms.

Methods: A total of 4,189 colonoscopy images were collected from two medical centers, covering different levels of bowel cleanliness, the presence of polyps, and the cecum. Using these data, eight pre-trained models based on CNN and Transformer architectures underwent transfer learning and fine-tuning.

Application value of the automated machine learning model based on modified CT index combined with serological indices in the early prediction of lung cancer.

Background And Objective: Accurately predicting the extent of lung tumor infiltration is crucial for improving patient survival and cure rates. This study aims to evaluate the application value of an improved CT index combined with serum biomarkers, obtained through an artificial intelligence recognition system analyzing CT features of pulmonary nodules, in early prediction of lung cancer infiltration using machine learning models.

Patients And Methods: A retrospective analysis was conducted on clinical data of 803 patients hospitalized for lung cancer treatment from January 2020 to December 2023 at two hospitals: Hospital 1 (Affiliated Changshu Hospital of Soochow University) and Hospital 2 (Nantong Eighth People's Hospital).

Prognostic factors for liver metastasis in patients with small intestinal stromal tumor: A retrospective analysis of surveillance, epidemiology, and end results.

Background: Liver metastasis (LIM) is the most common distant site of metastasis in small intestinal stromal tumors (SISTs). The aim of this study was to determine the risk and prognostic factors associated with LIM in patients with SISTs.

Methods: Patients diagnosed with gastrointestinal stromal tumors between 2010 and 2019 were selected from the Surveillance, Epidemiology, and End Results (SEER) database.

Improved effects of the b-value for 2000 sec/mm DWI on an accurate qualitative and quantitative assessment of rectal cancer.

Background And Study Objectives: A higher b-value Diffusion-weighted imaging (DWI) would improve the contrast between cancerous and noncancerous tissue. Apparent diffusion coefficient (ADC)-histogram analysis is a method that can provide statistical data and quantitative information on tumor heterogeneity. This study aimed to compare two high b-values (1000 and 2000 sec/mm) DWI in tumor detection and diagnostic performance in identifying early-stage tumor rectal cancer.

Application of Semi-supervised Fuzzy Clustering Based on Knowledge Weighting and Cluster Center Learning to Mammary Molybdenum Target Image Segmentation.

Breast cancer is commonly diagnosed with mammography. Using image segmentation algorithms to separate lesion areas in mammography can facilitate diagnosis by doctors and reduce their workload, which has important clinical significance. Because large, accurately labeled medical image datasets are difficult to obtain, traditional clustering algorithms are widely used in medical image segmentation as an unsupervised model.

Diagnosis of cerebral microbleed via VGG and extreme learning machine trained by Gaussian map bat algorithm.

Cerebral microbleed (CMB) is a serious public health concern. It is associated with dementia, which can be detected with brain magnetic resonance image (MRI). CMBs often appear as tiny round dots on MRIs, and they can be spotted anywhere over brain.

An Improved Soft Subspace Clustering Algorithm Based on Particle Swarm Optimization for MR Image Segmentation.

Soft subspace clustering (SSC), which analyzes high-dimensional data and applies various weights to each cluster class to assess the membership degree of each cluster to the space, has shown promising results in recent years. This method of clustering assigns distinct weights to each cluster class. By introducing spatial information, enhanced SSC algorithms improve the degree to which intraclass compactness and interclass separation are achieved.

Systematic Review on COVID-19 Readmission and Risk Factors: Future of Machine Learning in COVID-19 Readmission Studies.

In this review, current studies on hospital readmission due to infection of COVID-19 were discussed, compared, and further evaluated in order to understand the current trends and progress in mitigation of hospital readmissions due to COVID-19. Boolean expression of ("COVID-19" OR "covid19" OR "covid" OR "coronavirus" OR "Sars-CoV-2") AND ("readmission" OR "re-admission" OR "rehospitalization" OR "rehospitalization") were used in five databases, namely Web of Science, Medline, Science Direct, Google Scholar and Scopus. From the search, a total of 253 articles were screened down to 26 articles.

Multiclass Convolution Neural Network for Classification of COVID-19 CT Images.

In the late December of 2019, a novel coronavirus was discovered in Wuhan, China. In March 2020, WHO announced this epidemic had become a global pandemic and that the novel coronavirus may be mild to most people. However, some people may experience a severe illness that results in hospitalization or maybe death.

Multi-Modality Fusion & Inductive Knowledge Transfer Underlying Non-Sparse Multi-Kernel Learning and Distribution Adaption.

With the development of sensors, more and more multimodal data are accumulated, especially in biomedical and bioinformatics fields. Therefore, multimodal data analysis becomes very important and urgent. In this study, we combine multi-kernel learning and transfer learning, and propose a feature-level multi-modality fusion model with insufficient training samples.

The Promise for Reducing Healthcare Cost with Predictive Model: An Analysis with Quantized Evaluation Metric on Readmission.

Quality of care data has gained transparency captured through various measurements and reporting. Readmission measure is especially related to unfavorable patient outcomes that directly bends the curve of healthcare cost. Under the Hospital Readmission Reduction Program, payments to hospitals were reduced for those with excessive 30-day rehospitalization rates.

Textural differences based on apparent diffusion coefficient maps for discriminating pT3 subclasses of rectal adenocarcinoma.

Background: The accuracy of discriminating pT3a from pT3b-c rectal cancer using high-resolution magnetic resonance imaging (MRI) remains unsatisfactory, although texture analysis (TA) could improve such discrimination.

Aim: To investigate the value of TA on apparent diffusion coefficient (ADC) maps in differentiating pT3a rectal adenocarcinomas from pT3b-c tumors.

Methods: This was a case-control study of 59 patients with pT3 rectal adenocarcinoma, who underwent diffusion-weighted imaging (DWI) between October 2016 and December 2018.

Current Trends in Readmission Prediction: An Overview of Approaches.

Hospital readmission shortly after discharge threatens the quality of patient care and leads to increased medical care costs. In the United States, hospitals with high readmission rates are subject to federal financial penalties. This concern calls for incentives for healthcare facilities to reduce their readmission rates by predicting patients who are at high risk of readmission.

Comparison of Diagnostic Performance between Perfusion-Related Intravoxel Incoherent Motion DWI and Dynamic Contrast-Enhanced MRI in Rectal Cancer.

This study was aimed to determine the diagnostic performance of perfusion-related parameters derived from intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) by comparing them with quantitative parameters from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) based on differentiation grades of rectal cancer. We retrospectively analyzed 98 patients with rectal cancer. Perfusion-related IVIM parameters ( , , and · ) and quantitative DCE parameters ( , , , and ) were obtained by plotting the volume-of-interest on in-house software.

Drone Assisted Robust Emergency Service Management for Elderly Chronic Disease.

It is important to monitor the early screening of chronic diseases, predict the risk, and provide the comprehensive management of chronic diseases for the elderly. However, it is difficult to provide the robust and real-time emergency service for elderly chronic disease because of the complex social network and diversity of elderly chronic disease service. To address these issues, we design a new drone assisted robust emergency service system.

BDBB: A Novel Beta-Distribution-Based Biclustering Algorithm for Revealing Local Co-Methylation Patterns in Epi-Transcriptome Profiling Data.

N6-methyladenosine (mA) has been shown to play crucial roles in RNA metabolism, physiology, and pathological processes. However, the specific regulatory mechanisms of most methylation sites remain uncharted due to the complexity of life processes. Biological experimental methods are costly to solve this problem, and computational methods are relatively lacking.

Temporal Patterns of Vertigo and Migraine in Vestibular Migraine.

Vestibular migraine (VM) is a multidisciplinary disease under exploration. Multiple temporal patterns of vertigo and migraine make it difficult to diagnose VM, and their effect on the clinical features of VM is still unclear. Here we investigated the clinical features of VM under three temporal patterns.

Cross-Domain Classification Model With Knowledge Utilization Maximization for Recognition of Epileptic EEG Signals.

Conventional classification models for epileptic EEG signal recognition need sufficient labeled samples as training dataset. In addition, when training and testing EEG signal samples are collected from different distributions, for example, due to differences in patient groups or acquisition devices, such methods generally cannot perform well. In this paper, a cross-domain classification model with knowledge utilization maximization called CDC-KUM is presented, which takes advantage of the data global structure provided by the labeled samples in the related domain and unlabeled samples in the current domain.

Prediction of Clinical Pathologic Prognostic Factors for Rectal Adenocarcinoma: Volumetric Texture Analysis Based on Apparent Diffusion Coefficient Maps.

Texture analysis has been used to characterize and measure tissue heterogeneity in medical images. The purpose of this study was to investigate the potential of texture features derived from apparent diffusion coefficient (ADC) maps, to serve as imaging markers for predicting important histopathologic prognostic factors in rectal cancer. One hundred patients of rectal cancer received 3 T preoperative magnetic resonance imaging including diffusion-weighted imaging (DWI).

A Novel Distributed Multitask Fuzzy Clustering Algorithm for Automatic MR Brain Image Segmentation.

Artificial intelligence algorithms have been used in a wide range of applications in clinical aided diagnosis, such as automatic MR image segmentation and seizure EEG signal analyses. In recent years, many machine learning-based automatic MR brain image segmentation methods have been proposed as auxiliary methods of medical image analysis in clinical treatment. Nevertheless, many problems regarding precise medical images, which cannot be effectively utilized to improve partition performance, remain to be solved.