AIMC Topic: Reproducibility of Results
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Accelerating FLAIR imaging via deep learning reconstruction: potential for evaluating white matter hyperintensities.
Japanese journal of radiology
Sep 24, 2024
PURPOSE: To evaluate deep learning-reconstructed (DLR)-fluid-attenuated inversion recovery (FLAIR) images generated from undersampled data, compare them with fully sampled and rapidly acquired FLAIR images, and assess their potential for white matter...
Reliability of brain volume measures of accelerated 3D T1-weighted images with deep learning-based reconstruction.
Neuroradiology
Sep 24, 2024
PURPOSE: The time-intensive nature of acquiring 3D T1-weighted MRI and analyzing brain volumetry limits quantitative evaluation of brain atrophy. We explore the feasibility and reliability of deep learning-based accelerated MRI scans for brain volume...
High accuracy in lower limb alignment analysis using convolutional neural networks, with improvements needed for joint-level metrics.
Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA
Sep 22, 2024
PURPOSE: Evaluation of long-leg standing radiographs (LSR) is a standardised procedure for analysis of primary or secondary deformities of the lower limbs. Deep-learning convolutional neural networks (CNN) offer the potential to enhance radiological ...
AI-based lumbar central canal stenosis classification on sagittal MR images is comparable to experienced radiologists using axial images.
European radiology
Sep 20, 2024
OBJECTIVES: The assessment of lumbar central canal stenosis (LCCS) is crucial for diagnosing and planning treatment for patients with low back pain and neurogenic pain. However, manual assessment methods are time-consuming, variable, and require axia...
Optimization of fungal secondary metabolites production via response surface methodology coupled with multi-parameter optimized artificial neural network model.
Bioresource technology
Sep 20, 2024
Filamentous fungi's secondary metabolites (SMs) possess significant application owing to their distinct structure and diverse bioactivities, yet their restricted yield levels often hinder further research and application. The study developed a respon...
Computer Vision Identification of Trachomatous Inflammation-Follicular Using Deep Learning.
Cornea
Sep 20, 2024
PURPOSE: Trachoma surveys are used to estimate the prevalence of trachomatous inflammation-follicular (TF) to guide mass antibiotic distribution. These surveys currently rely on human graders, introducing a significant resource burden and potential f...
Effects of Individual Research Practices on fNIRS Signal Quality and Latent Characteristics.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Sep 20, 2024
Functional near-infrared spectroscopy (fNIRS) is an increasingly popular tool for cross-cultural neuroimaging studies. However, the reproducibility and comparability of fNIRS studies is still an open issue in the scientific community. The paucity of ...
Integrating Large Language Model, EEG, and Eye-Tracking for Word-Level Neural State Classification in Reading Comprehension.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Sep 20, 2024
With the recent proliferation of large language models (LLMs), such as Generative Pre-trained Transformers (GPT), there has been a significant shift in exploring human and machine comprehension of semantic language meaning. This shift calls for inter...
Diagnostic performance of an automated robot for MALDI target preparation in microbial identification.
Journal of clinical microbiology
Sep 19, 2024
The MBT Pathfinder is an automated colony-picking robot designed for efficient sample preparation in matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry. This article presents results from three key experiments ev...
Ten quick tips for ensuring machine learning model validity.
PLoS computational biology
Sep 19, 2024
Artificial Intelligence (AI) and Machine Learning (ML) models are increasingly deployed on biomedical and health data to shed insights on biological mechanism, predict disease outcomes, and support clinical decision-making. However, ensuring model va...
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