AIMC Topic: Humans

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Deep-TOF-PET: Deep learning-guided generation of time-of-flight from non-TOF brain PET images in the image and projection domains.

Human brain mapping
We aim to synthesize brain time-of-flight (TOF) PET images/sinograms from their corresponding non-TOF information in the image space (IS) and sinogram space (SS) to increase the signal-to-noise ratio (SNR) and contrast of abnormalities, and decrease ...

A novel combined deep learning methodology to non-invasively estimate hemoglobin levels in blood with high accuracy.

Medical engineering & physics
Hemoglobin is an essential protein found in blood and should not fall below a certain level in humans. Today's methods of hemoglobin measurement are mostly invasive. This study aims to perform a non-invasive estimation of hemoglobin levels using age,...

Identification of Unique Genetic Biomarkers of Various Subtypes of Glomerulonephritis Using Machine Learning and Deep Learning.

Biomolecules
(1) Objective: Identification of potential genetic biomarkers for various glomerulonephritis (GN) subtypes and discovering the molecular mechanisms of GN. (2) Methods: four microarray datasets of GN were downloaded from Gene Expression Omnibus (GEO) ...

A study on the influence of service robots' level of anthropomorphism on the willingness of users to follow their recommendations.

Scientific reports
Service robots are increasingly deployed in various industries including tourism. In spite of extensive research on the user's experience in interaction with these robots, there are yet unanswered questions about the factors that influence user's com...

Machine learning prediction of postoperative major adverse cardiovascular events in geriatric patients: a prospective cohort study.

BMC anesthesiology
BACKGROUND: Postoperative major adverse cardiovascular events (MACEs) account for more than one-third of perioperative deaths. Geriatric patients are more vulnerable to postoperative MACEs than younger patients. Identifying high-risk patients in adva...

A SuperLearner Approach to Predict Run-In Selection in Clinical Trials.

Computational and mathematical methods in medicine
A critical early step in a clinical trial is defining the study sample that appropriately represents the target population from which the sample will be drawn. Envisaging a "run-in" process in study design may accomplish this task; however, the tradi...

Perception Research of Artificial Intelligence in Environmental Public Health Physiotherapy Nursing for the Elderly.

Journal of environmental and public health
This perceptual study focuses on developing artificial intelligence for elderly care design. It analyses and discusses the role of artificial intelligence in elderly care and its application to physiotherapy care. Artificial intelligence, as an emerg...

Deep Learning Model for Predicting Rhythm Outcomes after Radiofrequency Catheter Ablation in Patients with Atrial Fibrillation.

Journal of healthcare engineering
Current guidelines on atrial fibrillation (AF) emphasized that radiofrequency catheter ablation (RFCA) should be decided after fully considering its prognosis. However, a robust prediction model reflecting the complex interactions between the feature...

Medical Data Classification Assisted by Machine Learning Strategy.

Computational and mathematical methods in medicine
With the development of science and technology, data plays an increasingly important role in our daily life. Therefore, much attention has been paid to the field of data mining. Data classification is the premise of data mining, and how well the data...

Design and Application of Artificial Intelligence Technology-Driven Education and Teaching System in Universities.

Computational and mathematical methods in medicine
In recent years, many colleges and universities have been experimenting and exploring the evaluation of education and teaching system and have achieved certain results. In order to understand the quality of education and teaching system in colleges a...