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A multicenter study on deep learning for glioblastoma auto-segmentation with prior knowledge in multimodal imaging.

Cancer science
A precise radiotherapy plan is crucial to ensure accurate segmentation of glioblastomas (GBMs) for radiation therapy. However, the traditional manual segmentation process is labor-intensive and heavily reliant on the experience of radiation oncologis...

Family history of cancer and lung cancer: Utility of big data and artificial intelligence for exploring the role of genetic risk.

Lung cancer (Amsterdam, Netherlands)
OBJECTIVES: Lung Cancer (LC) is a multifactorial disease for which the role of genetic susceptibility has become increasingly relevant. Our aim was to use artificial intelligence (AI) to analyze differences between patients with LC based on family hi...

Pharmacy Student Perceptions of Using AI-Based Interview Software (Big Interview) for Career Preparation.

American journal of pharmaceutical education
OBJECTIVE: The goal of this project was to understand the use of artificial intelligence-based interview software (Big Interview) for job interview preparation and its impact on pharmacy students' self-confidence and interview preparedness.

Clinical application of convolutional neural network lung nodule detection software: An Australian quaternary hospital experience.

Journal of medical imaging and radiation oncology
INTRODUCTION: Early-stage lung cancer diagnosis through detection of nodules on computed tomography (CT) remains integral to patient survivorship, promoting national screening programmes and diagnostic tools using artificial intelligence (AI) convolu...

Diagnosis of Hirschsprung disease by analyzing acetylcholinesterase staining using artificial intelligence.

Journal of pediatric gastroenterology and nutrition
OBJECTIVES: Classical Hirschsprung disease (HD) is defined by the absence of ganglion cells in the rectosigmoid colon. The diagnosis is made from rectal biopsy, which reveals the aganglionosis and the presence of cholinergic hyperinnervation. However...

Unraveling the impact of therapeutic drug monitoring via machine learning for patients with sepsis.

Cell reports. Medicine
Clinical studies investigating the benefits of beta-lactam therapeutic drug monitoring (TDM) among critically ill patients are hindered by small patient groups, variability between studies, patient heterogeneity, and inadequate use of TDM. Accordingl...

The impact of differential pricing subject on consumer behavior.

BMC psychology
The escalating use of artificial intelligence in marketing significantly impacts all aspects of consumer life. This research, grounded in attribution theory and S-O-R theory, employs scenario-based experimental methods to simulate two distinct purcha...

Admission blood tests predicting survival of SARS-CoV-2 infected patients: a practical implementation of graph convolution network in imbalance dataset.

BMC infectious diseases
BACKGROUND: Predicting an individual's risk of death from COVID-19 is essential for planning and optimising resources. However, since the real-world mortality rate is relatively low, particularly in places like Hong Kong, this makes building an accur...

Establishing the importance of co-creation and self-efficacy in creative collaboration with artificial intelligence.

Scientific reports
The emergence of generative AI technologies has led to an increasing number of people collaborating with AI to produce creative works. Across two experimental studies, in which we carefully designed and programmed state-of-the-art human-AI interfaces...

Modeling and predicting meat yield and growth performance using morphological features of narrow-clawed crayfish with machine learning techniques.

Scientific reports
In recent studies, artificial intelligence and machine learning methods give higher accuracy than other prediction methods in large data sets with complex structures. Instead of statistical methods, artificial intelligence, and machine learning are u...