AIMC Topic: Young Adult

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Non-Invasive Estimation of Hemoglobin Using a Multi-Model Stacking Regressor.

IEEE journal of biomedical and health informatics
OBJECTIVE: We describe a novel machine-learning based method to estimate total Hemoglobin (Hb) using photoplethysmograms (PPGs) acquired non-invasively.

Comparison of morphometric parameters in prediction of hydrocephalus using random forests.

Computers in biology and medicine
Ventricles of the human brain enlarge with aging, neurodegenerative diseases, intrinsic, and extrinsic pathologies. The morphometric examination of neuroimages is an effective approach to assess structural changes occurring due to diseases such as hy...

Machine learning-based multiparametric MRI radiomics for predicting the aggressiveness of papillary thyroid carcinoma.

European journal of radiology
PURPOSE: To investigate the predictive capability of machine learning-based multiparametric magnetic resonance (MR) imaging radiomics for evaluating the aggressiveness of papillary thyroid carcinoma (PTC) preoperatively.

Investigating cooperation with robotic peers.

PloS one
We explored how people establish cooperation with robotic peers, by giving participants the chance to choose whether to cooperate or not with a more/less selfish robot, as well as a more or less interactive, in a more or less critical environment. We...

Lymph Node Metastasis Prediction from Primary Breast Cancer US Images Using Deep Learning.

Radiology
Background Deep learning (DL) algorithms are gaining extensive attention for their excellent performance in image recognition tasks. DL models can automatically make a quantitative assessment of complex medical image characteristics and achieve incre...

Integrating machining learning and multimodal neuroimaging to detect schizophrenia at the level of the individual.

Human brain mapping
Schizophrenia is a severe psychiatric disorder associated with both structural and functional brain abnormalities. In the past few years, there has been growing interest in the application of machine learning techniques to neuroimaging data for the d...

Diagnosis of cystic lesions using panoramic and cone beam computed tomographic images based on deep learning neural network.

Oral diseases
OBJECTIVES: The aim of the current study was to evaluate the detection and diagnosis of three types of odontogenic cystic lesions (OCLs)-odontogenic keratocysts, dentigerous cysts, and periapical cysts-using dental panoramic radiography and cone beam...

Implementing machine learning in bipolar diagnosis in China.

Translational psychiatry
Bipolar disorder (BPD) is often confused with major depression, and current diagnostic questionnaires are subjective and time intensive. The aim of this study was to develop a new Bipolar Diagnosis Checklist in Chinese (BDCC) by using machine learnin...

Abstract art paintings, global image properties, and verbal descriptions: An empirical and computational investigation.

Acta psychologica
While global image properties (GIPs) relate to preference ratings in many categories of visual stimuli, this relationship is typically not seen for abstract art paintings. Using computational network science and empirical methods, we further investig...