AIMC Topic: Adult

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Social anxiety prediction based on ERP features: A deep learning approach.

Journal of affective disorders
BACKGROUND: Social Anxiety Disorder is traditionally diagnosed using subjective scales that may lack accuracy. Recently, EEG technology has gained importance for anxiety detection due to its ability to capture stable and objective neurophysiological ...

PET radiomics-based lymphovascular invasion prediction in lung cancer using multiple segmentation and multi-machine learning algorithms.

Physical and engineering sciences in medicine
The current study aimed to predict lymphovascular invasion (LVI) using multiple machine learning algorithms and multi-segmentation positron emission tomography (PET) radiomics in non-small cell lung cancer (NSCLC) patients, offering new avenues for p...

Speech synthesis from three-axis accelerometer signals using conformer-based deep neural network.

Computers in biology and medicine
Silent speech interfaces (SSIs) have emerged as innovative non-acoustic communication methods, and our previous study demonstrated the significant potential of three-axis accelerometer-based SSIs to identify silently spoken words with high classifica...

Artificial intelligence interpretation of touch print smear cytology of testicular specimen from patients with azoospermia.

Journal of assisted reproduction and genetics
PURPOSE: Identification of mature sperm at microdissection testicular sperm extraction (mTESE) is a crucial step of sperm retrieval to help patients with non-obstructive azoospermia (NOA) proceed to intracytoplasmic sperm injection. Touch print smear...

The Application of Mask Region-Based Convolutional Neural Networks in the Detection of Nasal Septal Deviation Using Cone Beam Computed Tomography Images: Proof-of-Concept Study.

JMIR formative research
BACKGROUND: Artificial intelligence (AI) models are being increasingly studied for the detection of variations and pathologies in different imaging modalities. Nasal septal deviation (NSD) is an important anatomical structure with clinical implicatio...

Machine learning to identify precachexia and cachexia: a multicenter, retrospective cohort study.

Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
BACKGROUND: Detection of precachexia is important for the prevention and treatment of cachexia. However, how to identify precachexia is still a challenge.

Lateral cephalometric parameters among Arab skeletal classes II and III patients and applying machine learning models.

Clinical oral investigations
BACKGROUND: The World Health Organization considers malocclusion one of the most essential oral health problems. This disease influences various aspects of patients' health and well-being. Therefore, making it easier and more accurate to understand a...

Adaptive Modular Neural Control for Online Gait Synchronization and Adaptation of an Assistive Lower-Limb Exoskeleton.

IEEE transactions on neural networks and learning systems
Gait synchronization has attracted significant attention in research on assistive lower-limb exoskeletons because it can circumvent conflicting movements and improve the assistance performance. This study proposes an adaptive modular neural control (...