AIMC Topic: Neural Networks, Computer

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Differentiation of Healthy Ex Vivo Bovine Tissues Using Raman Spectroscopy and Interpretable Machine Learning.

Lasers in surgery and medicine
OBJECTIVES: Integrating machine learning with Raman spectroscopy (RS) shows strong potential for intraoperative guidance in orthopedic procedures, but limited algorithm transparency remains a barrier to clinician trust. This study aims to develop int...

Multimodal integration of longitudinal noninvasive diagnostics for survival prediction in immunotherapy using deep learning.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: Immunotherapies have revolutionized the landscape of cancer treatments. However, our understanding of response patterns in advanced cancers treated with immunotherapy remains limited. By leveraging routinely collected noninvasive longitud...

Deep learning techniques for automated coronary artery segmentation and coronary artery disease detection: A systematic review of the last decade (2013-2024).

Computer methods and programs in biomedicine
BACKGROUND: Coronary artery disease (CAD) is the most common cardiovascular disease, exacting high morbidity and mortality worldwide. CAD is detected on coronary artery imaging; coronary artery segmentation (CAS) of the images is essential for corona...

Network models incorporating chloride dynamics predict optimal strategies for terminating status epilepticus.

Neurobiology of disease
Status epilepticus (SE), seizures lasting beyond five minutes, is a medical emergency commonly treated with benzodiazepines which enhance GABA receptor (GABAR) conductance. Despite widespread use, benzodiazepines fail in over one-third of patients, p...

Effectiveness of Artificial Intelligence in detecting sinonasal pathology using clinical imaging modalities: a systematic review.

Rhinology
BACKGROUND: Sinonasal pathology can be complex and requires a systematic and meticulous approach. Artificial Intelligence (AI) has the potential to improve diagnostic accuracy and efficiency in sinonasal imaging, but its clinical applicability remain...

The impact of clinical history on the predictive performance of machine learning and deep learning models for renal complications of diabetes.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Diabetes is a chronic disease characterised by a high risk of developing diabetic nephropathy. The early identification of individuals at heightened risk of such complications or their exacerbation can be crucial to set a co...

Causal insights from clinical information in radiology: Enhancing future multimodal AI development.

Computer methods and programs in biomedicine
PURPOSE: This study investigates the causal mechanisms underlying radiology report generation by analyzing how clinical information and prior imaging examinations contribute to annotation shifts. We systematically estimate why and how biases manifest...

A myocardial reorientation method based on feature point detection for quantitative analysis of PET myocardial perfusion imaging.

Computer methods and programs in biomedicine
OBJECTIVE: Reorienting cardiac positron emission tomography (PET) images to the transaxial plane is essential for cardiac PET image analysis. This study aims to design a convolutional neural network (CNN) for automatic reorientation and evaluate its ...

A novel approach for ECG signal classification using sliding Euclidean quantization and bitwise pattern encoding.

Computer methods in biomechanics and biomedical engineering
This study aims to introduce a novel, computationally lightweight feature extraction technique called Sliding Euclidean Pattern Quantization (SEPQ), which encodes local morphological patterns of ECG signals using Euclidean distance-based binary repre...

Neural Network-based Automated Classification of 18 F-FDG PET/CT Lesions and Prognosis Prediction in Nasopharyngeal Carcinoma Without Distant Metastasis.

Clinical nuclear medicine
PURPOSE: To evaluate the diagnostic performance of the PET Assisted Reporting System (PARS) in nasopharyngeal carcinoma (NPC) patients without distant metastasis, and to investigate the prognostic significance of the metabolic parameters.