AIMC Topic: Humans

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Enhancing the accuracy of blood-glucose tests by upgrading FTIR with multiple-reflections, quantum cascade laser, two-dimensional correlation spectroscopy and machine learning.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
The accuracy of screening diabetes from non-diabetes is drastically enhanced by strategically upgrading the bench-marking infrared spectroscopy technique for non-invasive tests of blood-glucose, both with state-of-the-art instrumentation-retrofits an...

A deep neural network model for classifying pharmacy practice publications into research domains.

Research in social & administrative pharmacy : RSAP
BACKGROUND: Pharmacy practice faculty research profiles extend beyond the clinical and social domains, which are core elements of pharmacy practice. But as highlighted by journal editors in the Granada Statements, there is no consensus on these terms...

Minimizing prostate diffusion weighted MRI examination time through deep learning reconstruction.

Clinical imaging
PURPOSE: To study the diagnostic image quality of high b-value diffusion weighted images (DWI) derived from standard and variably reduced datasets reconstructed with a commercially available deep learning reconstruction (DLR) algorithm.

AmbiBias Contrast: Enhancing debiasing networks via disentangled space from ambiguity-bias clusters.

Neural networks : the official journal of the International Neural Network Society
The goal of debiasing in classification tasks is to train models to be less sensitive to correlations between a sample's target attribution and periodically occurring contextual attributes to achieve accurate classification. A prevalent method involv...

Enhanced NSCLC subtyping and staging through attention-augmented multi-task deep learning: A novel diagnostic tool.

International journal of medical informatics
OBJECTIVES: The objective of this study is to develop a novel multi-task learning approach with attention encoders for classifying histologic subtypes and clinical stages of non-small cell lung cancer (NSCLC), with superior performance compared to cu...

Artificial intelligence in clinical practice: Quality and evidence.

Revista clinica espanola
A revolution is taking place within the field of artificial intelligence (AI) with the emergence of generative AI. Although we are in an early phase at the clinical level, there is an exponential increase in the number of scientific articles that use...

A generalised computer vision model for improved glaucoma screening using fundus images.

Eye (London, England)
IMPORTANCE: Worldwide, glaucoma is a leading cause of irreversible blindness. Timely detection is paramount yet challenging, particularly in resource-limited settings. A novel, computer vision-based model for glaucoma screening using fundus images co...

Deep Learning-Assisted Label-Free Parallel Cell Sorting with Digital Microfluidics.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Sorting specific cells from heterogeneous samples is important for research and clinical applications. In this work, a novel label-free cell sorting method is presented that integrates deep learning image recognition with microfluidic manipulation to...

Multi-lesion segmentation guided deep attention network for automated detection of diabetic retinopathy.

Computers in biology and medicine
Accurate multi-lesion segmentation together with automated grading on fundus images played a vital role in diagnosing and treating diabetic retinopathy (DR). Nevertheless, the intrinsic patterns of fundus lesions aggravated challenges in DR detection...

Deep learning approaches for automated classification of neonatal lung ultrasound with assessment of human-to-AI interrater agreement.

Computers in biology and medicine
Neonatal respiratory disorders pose significant challenges in clinical settings, often requiring rapid and accurate diagnostic solutions for effective management. Lung ultrasound (LUS) has emerged as a promising tool to evaluate respiratory condition...