AIMC Topic: Humans

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Detecting severe coronary artery stenosis in T2DM patients with NAFLD using cardiac fat radiomics-based machine learning.

Scientific reports
To analyze radiomics features of cardiac adipose tissue in individuals with type 2 diabetes (T2DM) and non-alcoholic fatty liver disease (NAFLD), integrating relevant clinical indicators, and employing machine learning techniques to construct a preci...

Urban and rural disparities in stroke prediction using machine learning among Chinese older adults.

Scientific reports
Stroke is a significant health concern in China. Differences in stroke risk between rural and urban areas have been highlighted in prior research. However, there is a scarcity of studies on urban-rural differences in predicting stroke. This study aim...

Predicting 90-day risk of urinary tract infections following urostomy in bladder cancer patients using machine learning and explainability.

Scientific reports
This research aims to design and validate a machine learning model to predict the probability of urinary tract infections within 90 days post-urostomy in bladder cancer patients. Clinical and follow-up information from 317 patients who had urostomy p...

Assessing the effectiveness of long short-term memory and artificial neural network in predicting daily ozone concentrations in Liaocheng City.

Scientific reports
Ozone pollution affects food production, human health, and the lives of individuals. Due to rapid industrialization and urbanization, Liaocheng has experienced increasing of ozone concentration over several years. Therefore, ozone has become a major ...

Development and validation of a deep reinforcement learning algorithm for auto-delineation of organs at risk in cervical cancer radiotherapy.

Scientific reports
This study was conducted to develop and validate a novel deep reinforcement learning (DRL) algorithm incorporating the segment anything model (SAM) to enhance the accuracy of automatic contouring organs at risk during radiotherapy for cervical cancer...

Evaluating feature extraction in ovarian cancer cell line co-cultures using deep neural networks.

Communications biology
Single-cell image analysis is crucial for studying drug effects on cellular morphology and phenotypic changes. Most studies focus on single cell types, overlooking the complexity of cellular interactions. Here, we establish an analysis pipeline to ex...

Machine learning for early diagnosis of Kawasaki disease in acute febrile children: retrospective cross-sectional study in China.

Scientific reports
Early diagnosis of Kawasaki disease (KD) allows timely treatment to be initiated, thereby preventing coronary artery aneurysms in children. However, it is challenging due to the subjective nature of the diagnostic criteria. This study aims to develop...

Discrimination of inherent characteristics of susceptible and resistant strains of Anopheles gambiae by explainable artificial intelligence analysis of flight trajectories.

Scientific reports
Understanding mosquito behaviours is vital for the development of insecticide-treated nets (ITNs), which have been successfully deployed in sub-Saharan Africa to reduce disease transmission, particularly malaria. However, rising insecticide resistanc...

Enhancing E-commerce recommendations with sentiment analysis using MLA-EDTCNet and collaborative filtering.

Scientific reports
The rapid growth of e-commerce has made product recommendation systems essential for enhancing customer experience and driving business success. This research proposes an advanced recommendation framework that integrates sentiment analysis (SA) and c...

A feature explainability-based deep learning technique for diabetic foot ulcer identification.

Scientific reports
Diabetic foot ulcers (DFUs) are a common and serious complication of diabetes, presenting as open sores or wounds on the sole. They result from impaired blood circulation and neuropathy associated with diabetes, increasing the risk of severe infectio...