Clustering-based risk stratification of prediabetes populations: Insights from the Taiwan and UK Biobanks.

Aims/introduction: This study aimed to identify low- and high-risk diabetes groups within prediabetes populations using data from the Taiwan Biobank (TWB) and UK Biobank (UKB) through a clustering-based Unsupervised Learning (UL) approach, to inform targeted type 2 diabetes (T2D) interventions.

Materials And Methods: Data from TWB and UKB, comprising clinical and genetic information, were analyzed. Prediabetes was defined by glucose thresholds, and incident T2D was identified through follow-up data.

Exploring concepts and trends in informal caregiver burden: systematic review using citation network and content analysis.

Background: With the increase in the aging population, informal caregivers have become an essential pillar for the long-term care of older individuals. However, providing care can have a negative impact and increase the burden on caregivers, which is a cause for concern.

Objective: This study aimed to comprehensively depict the concept of "informal caregiver burden" through bibliometric and content analyses.

Localization of Colorectal Cancer Lesions in Contrast-Computed Tomography Images via a Deep Learning Approach.

Abdominal computed tomography (CT) is a frequently used imaging modality for evaluating gastrointestinal diseases. The detection of colorectal cancer is often realized using CT before a more invasive colonoscopy. When a CT exam is performed for indications other than colorectal evaluation, the tortuous structure of the long, tubular colon makes it difficult to analyze the colon carefully and thoroughly.

Colon Tissues Classification and Localization in Whole Slide Images Using Deep Learning.

Colorectal cancer is one of the leading causes of cancer-related death worldwide. The early diagnosis of colon cancer not only reduces mortality but also reduces the burden related to the treatment strategies such as chemotherapy and/or radiotherapy. However, when the microscopic examination of the suspected colon tissue sample is carried out, it becomes a tedious and time-consuming job for the pathologists to find the abnormality in the tissue.

Deep Learning Assisted Localization of Polycystic Kidney on Contrast-Enhanced CT Images.

Total Kidney Volume (TKV) is essential for analyzing the progressive loss of renal function in Autosomal Dominant Polycystic Kidney Disease (ADPKD). Conventionally, to measure TKV from medical images, a radiologist needs to localize and segment the kidneys by defining and delineating the kidney's boundary slice by slice. However, kidney localization is a time-consuming and challenging task considering the unstructured medical images from big data such as Contrast-enhanced Computed Tomography (CCT).

Prediction of Colon Cancer Stages and Survival Period with Machine Learning Approach.

The prediction of tumor in the TNM staging (tumor, node, and metastasis) stage of colon cancer using the most influential histopathology parameters and to predict the five years disease-free survival (DFS) period using machine learning (ML) in clinical research have been studied here. From the colorectal cancer (CRC) registry of Chang Gung Memorial Hospital, Linkou, Taiwan, 4021 patients were selected for the analysis. Various ML algorithms were applied for the tumor stage prediction of the colon cancer by considering the Tumor Aggression Score (TAS) as a prognostic factor.