AIMC Topic: Humans

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Nasopharyngeal cancer adaptive radiotherapy with CBCT-derived synthetic CT: deep learning-based auto-segmentation precision and dose calculation consistency on a C-Arm linac.

Radiation oncology (London, England)
BACKGROUND: To evaluate the precision of automated segmentation facilitated by deep learning (DL) and dose calculation in adaptive radiotherapy (ART) for nasopharyngeal cancer (NPC), leveraging synthetic CT (sCT) images derived from cone-beam CT (CBC...

Machine learning in predicting preoperative intra-aortic balloon pump use in patients undergoing coronary artery bypass grafting.

Journal of cardiothoracic surgery
BACKGROUND: Intra-aortic balloon pump (IABP) implantation in the perioperative period of cardiac surgery is an auxiliary treatment for cardiogenic shock. However, there is a lack of effective prediction models for preoperative IABP implantation.

From correlation to causation: unraveling the role of long non-coding RNAs in COVID-19 pathogenesis.

Virology journal
Heydari et al. present an intriguing study examining the role of three long non-coding RNAs (lncRNAs)-H19, taurine upregulated gene 1 (TUG1), and colorectal neoplasia differentially expressed (CRNDE)-in the context of Coronavirus Disease 2019 (COVID-...

Predicting survival factor following suicide attempt in Iran: an ensemble machine learning technique.

BMC psychiatry
BACKGROUND: Suicide represents a significant challenge to public health that calls for a suitable intervention from the healthcare sector. Despite the typically low suicide rate among most Muslim nations, research indicates that there is an increase ...

Health-economic evaluation of an AI-powered decision support system for anemia management in in-center hemodialysis patients.

BMC nephrology
BACKGROUND: The Anemia Control Model (ACM) is a decision support system powered by an artificial intelligence core designed to assist nephrologists in managing anemia therapy for in-center hemodialysis (HD) patients. This study aims to evaluate the c...

Latent variable sequence identification for cognitive models with neural network estimators.

Behavior research methods
Extracting time-varying latent variables from computational cognitive models plays a key role in uncovering the dynamic cognitive processes that drive behaviors. However, existing methods are limited to inferring latent variable sequences in a relati...

AI-Driven Tai Chi mastery using deep learning framework for movement assessment and personalized training.

Scientific reports
This paper presents a novel system for optimizing Tai Chi movement training using computer vision and deep learning technologies. We developed a comprehensive framework incorporating multi-view pose estimation, temporal feature extraction, and real-t...

Mapping interconnectivity of digital twin healthcare research themes through structural topic modeling.

Scientific reports
Digital twin (DT) technology is revolutionizing healthcare systems by leveraging real-time data integration and advanced analytics to enhance patient care, optimize clinical operations, and facilitate simulation. This study aimed to identify key rese...

A simple and effective approach for body part recognition on CT scans based on projection estimation.

Scientific reports
It is well known that machine learning models require a high amount of annotated data to obtain optimal performance. Labelling Computed Tomography (CT) data can be a particularly challenging task due to its volumetric nature and often missing and/or ...

Telementoring for surgical training in low-resource settings: a systematic review of current systems and the emerging role of 5G, AI, and XR.

Journal of robotic surgery
Telementoring in surgical training enables expert surgeons to provide real-time remote guidance to trainees. This technique is increasingly adopted to address surgical training gaps in low- and middle-income countries (LMICs), i.e., nations with a gr...