AIMC Topic: Humans

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BSA-Seg: A Bi-level sparse attention network combining narrow band loss for multi-target medical image segmentation.

Neural networks : the official journal of the International Neural Network Society
Segmentation of multiple targets of varying sizes within medical images is of significant importance for the diagnosis of disease and pathological research. Transformer-based methods are emerging in the medical image segmentation, leveraging the powe...

TCDE-Net: An unsupervised dual-encoder network for 3D brain medical image registration.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Medical image registration is a critical task in aligning medical images from different time points, modalities, or individuals, essential for accurate diagnosis and treatment planning. Despite significant progress in deep learning-based registration...

Integrating data mining with transcranial focused ultrasound to refine neuralgia treatment strategies.

Journal of neuroscience methods
BACKGROUND: Neuralgia and other neuropathic pain are difficult to treat owing to their complicated etiology and a wide variety of responses to treatment. The novel neuromodulation technology transcranial focused ultrasound (tFUS) has intriguing impli...

Evaluation and comparison of machine learning algorithms for predicting discharge against medical advice in injured inpatients.

Surgery
BACKGROUND: Whether the application of machine learning algorithms offers an advantage over logistic regression in forecasting discharge against medical advice occurrences needs to be evaluated.

The role of artificial intelligence in intraoral scanning for complete-arch digital impressions: An in vitro study.

Journal of dentistry
OBJECTIVES: Artificial intelligence (AI) is increasingly being integrated into intraoral scanners (IOS) to improve the quality of digital impressions. However, information on the accuracy of AI-assisted virtual models is limited. This study aimed to ...

Development and validation of pan-cancer lesion segmentation AI-model for whole-body 18F-FDG PET/CT in diverse clinical cohorts.

Computers in biology and medicine
BACKGROUND: This study develops a deep learning-based automated lesion segmentation model for whole-body 3DF-fluorodeoxyglucose (FDG)-Position emission tomography (PET) with computed tomography (CT) images agnostic to disease location and site.

Which approach better predicts diabetes: Traditional econometric methods or machine learning? Evidence from a cross-sectional study in South Korea.

Computers in biology and medicine
To prevent chronic disease from getting worse, it is important to detect and predict it at an early stage. Therefore, the accuracy of the prediction is particularly important. To investigate the accuracy of different methods, this study compares the ...

AI Efficiency in Dentistry: Comparing Artificial Intelligence Systems with Human Practitioners in Assessing Several Periodontal Parameters.

Medicina (Kaunas, Lithuania)
Artificial intelligence (AI) is increasingly used in healthcare, including dental and periodontal diagnostics, due to its ability to analyze complex datasets with speed and precision. This study aimed to evaluate the reliability of AI-assisted denta...

Machine learning methods for determining skin age: A systematic review.

Journal of tissue viability
AIM: This systematic review explores how machine learning is used in determining skin aging, aiming to evaluate accuracy, limitations, and gaps in the current literature.