AIMC Topic: Adult

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Prediction model of subacromial pain syndrome in assembly workers using shoulder range of motion and muscle strength based on support vector machine.

Ergonomics
Subacromial pain syndrome (SAPS) is the most common upper-extremity musculoskeletal problem among workers. In this study, a machine learning model was built to predict and classify the presence or absence of SAPS in assembly workers with shoulder joi...

Artificial Intelligence in BI-RADS Categorization of Breast Lesions on Ultrasound: Can We Omit Excessive Follow-ups and Biopsies?

Academic radiology
RATIONALE AND OBJECTIVES: Artificial intelligence (AI) systems have been increasingly applied to breast ultrasonography. They are expected to decrease the workload of radiologists and to improve diagnostic accuracy. The aim of this study is to evalua...

How well do rudimentary plasticity rules predict adult visual object learning?

PLoS computational biology
A core problem in visual object learning is using a finite number of images of a new object to accurately identify that object in future, novel images. One longstanding, conceptual hypothesis asserts that this core problem is solved by adult brains t...

The ENGAGE study: evaluation of a conversational virtual agent that provides tailored pre-test genetic education to cancer patients.

Journal of cancer survivorship : research and practice
PURPOSE: Novel approaches are needed to ensure all patients with cancer have access to quality genetic education before genetic testing to enable informed treatment decisions. The purpose of this study was to test the use of an artificial intelligenc...

Accuracy of artificial intelligence CT quantification in predicting COVID-19 subjects' prognosis.

PloS one
BACKGROUND: Artificial intelligence (AI)-aided analysis of chest CT expedites the quantification of abnormalities and may facilitate the diagnosis and assessment of the prognosis of subjects with COVID-19.

Development of machine learning models to predict cancer-related fatigue in Dutch breast cancer survivors up to 15 years after diagnosis.

Journal of cancer survivorship : research and practice
PURPOSE: To prevent (chronic) cancer-related fatigue (CRF) after breast cancer, it is important to identify survivors at risk on time. In literature, factors related to CRF are identified, but not often linked to individual risks. Therefore, our aim ...

The automatic diagnosis artificial intelligence system for preoperative magnetic resonance imaging of uterine sarcoma.

Journal of gynecologic oncology
OBJECTIVE: Magnetic resonance imaging (MRI) is efficient for the diagnosis of preoperative uterine sarcoma; however, misdiagnoses may occur. In this study, we developed a new artificial intelligence (AI) system to overcome the limitations of requirin...

Identifying Behavior Change Techniques in an Artificial Intelligence-Based Fitness App: A Content Analysis.

Health education & behavior : the official publication of the Society for Public Health Education
In the field of artificial intelligence-based fitness apps, the effective integration of behavior change techniques (BCTs) is critical for promoting physical activity and improving health outcomes. However, the specific BCTs employed by apps and thei...

Contrast-Enhanced CT-Based Deep Learning Radiomics Nomogram for the Survival Prediction in Gallbladder Cancer.

Academic radiology
RATIONALE AND OBJECTIVES: An accurate prognostic model is essential for the development of treatment strategies for gallbladder cancer (GBC). This study proposes an integrated model using clinical features, radiomics, and deep learning based on contr...

CEMRI-Based Quantification of Intratumoral Heterogeneity for Predicting Aggressive Characteristics of Hepatocellular Carcinoma Using Habitat Analysis: Comparison and Combination of Deep Learning.

Academic radiology
RATIONALE AND OBJECTIVES: To explore both an intratumoral heterogeneity (ITH) model based on habitat analysis and a deep learning (DL) model based on contrast-enhanced magnetic resonance imaging (CEMRI) and validate its efficiency for predicting micr...