AI Medical Compendium Topic

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Our tools redefine what it means to be us: perceived robotic agency decreases the importance of agency in humanity.

BMC psychology
Past work has primarily focused on how the perception of robotic agency influences human-robot interaction and the evaluation of robotic progress, while overlooking its impact on reconsidering what it means to be human. Drawing on social identity the...

AADNet: Exploring EEG Spatiotemporal Information for Fast and Accurate Orientation and Timbre Detection of Auditory Attention Based on a Cue-Masked Paradigm.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Auditory attention decoding from electroencephalogram (EEG) could infer to which source the user is attending in noisy environments. Decoding algorithms and experimental paradigm designs are crucial for the development of technology in practical appl...

Machine learning models for improving the diagnosing efficiency of skeletal class I and III in German orthodontic patients.

Scientific reports
The precise and efficient diagnosis of an individual's skeletal class is necessary in orthodontics to ensure correct and stable treatment planning. However, it is difficult to efficiently determine the true skeletal class due to several correlations ...

Estimating gait parameters from sEMG signals using machine learning techniques under different power capacity of muscle.

Scientific reports
The gait analysis has been applied in many fields, such as the assessment of falling, force evaluation in sports, and gait disorder detection for neuromuscular diseases. Its main recording techniques include video cameras and wearable sensors. Howeve...

Machine learning-based differentiation of schizophrenia and bipolar disorder using multiscale fuzzy entropy and relative power from resting-state EEG.

Translational psychiatry
Schizophrenia (SZ) and bipolar disorder (BD) pose diagnostic challenges due to overlapping clinical symptoms and genetic factors, often resulting in misdiagnosis and suboptimal treatment outcomes. This study aimed to identify EEG-based biomarkers tha...

Explainable AI for enhanced accuracy in malaria diagnosis using ensemble machine learning models.

BMC medical informatics and decision making
BACKGROUND: Malaria, an infectious disease caused by protozoan parasites belonging to the Plasmodium genus, remains a significant public health challenge, with African regions bearing the heaviest burden. Machine learning techniques have shown great ...

Predicting the efficacy of microwave ablation of benign thyroid nodules from ultrasound images using deep convolutional neural networks.

BMC medical informatics and decision making
BACKGROUND: Thyroid nodules are frequent in clinical settings, and their diagnosis in adults is growing, with some persons experiencing symptoms. Ultrasound-guided thermal ablation can shrink nodules and alleviate discomfort. Because the degree and r...

Utilizing machine learning and geographic analysis to improve Post-crash traffic injury management and emergency response systems.

International journal of injury control and safety promotion
Traffic injuries are a major public health concern globally. This study uses machine learning (ML) and geographic analysis to analyse road traffic fatalities and improve traffic safety in Nakhon Ratchasima Province, Thailand. Data on road traffic fat...

Evaluation of Anthropometric Measurement Results and the Relationship Between Individual Identity and Geographic Belonging Through Artificial Neural Networks from a Mental Health Perspective.

Nigerian journal of clinical practice
BACKGROUND: Identity verification and geographical belonging are significant issues with mental health implications, particularly in forensic contexts. Anthropometric measurements offer potential insights into these relationships.