AIMC Topic: Quality Control

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Novel approach for quality control testing of medical displays using deep learning technology.

Biomedical physics & engineering express
In digital image diagnosis using medical displays, it is crucial to rigorously manage display devices to ensure appropriate image quality and diagnostic safety. The aim of this study was to develop a model for the efficient quality control (QC) of me...

A deep learning approach to perform defect classification of freeze-dried product.

International journal of pharmaceutics
Cosmetic inspection of freeze-dried products is an important part of the post-manufacturing quality control process. Traditionally done by human visual inspection, this method poses typical challenges and shortcomings that can be addressed with innov...

Lightweight deep learning algorithm for real-time wheat flour quality detection via NIR spectroscopy.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Wheat flour quality, determined by factors such as protein and moisture content, is crucial in food production. Traditional methods for analyzing these parameters, though precise, are time-consuming and impractical for large-scale operations. This st...

Tea grading, blending, and matching based on computer vision and deep learning.

Journal of the science of food and agriculture
BACKGROUND: Accurate tea blending assessment and sample matching are critical in the tea production process. Traditional methods face efficiency and accuracy challenges, which can be addressed by advances in computer vision and deep learning. This st...

Cost-effectiveness analysis of AI-based image quality control for perinatal ultrasound screening.

BMC medical education
PURPOSE: This study aimed to compare the cost-effectiveness of AI-based approaches with manual approaches in ultrasound image quality control (QC).

Methodology for quality risk prediction for milk powder production plants with domain-knowledge-involved serial neural networks.

Food chemistry
In dairy enterprises, predicting product quality attributes that are influenced by operating parameters is a major task. To reduce quality loss in production, a prediction-based quality control method is proposed in this study. In particular, a seria...

Manual data labeling, radiology, and artificial intelligence: It is a dirty job, but someone has to do it.

Magnetic resonance imaging
In this letter to the editor, authors highlight the key role of data labeling in training AI models for medical imaging, discussing the complexities, resource demands, costs, and the relevance of quality control in the labeling process including the ...

Transfer learning in spirometry: CNN models for automated flow-volume curve quality control in paediatric populations.

Computers in biology and medicine
PROBLEM: Current spirometers face challenges in evaluating acceptability criteria, often requiring manual visual inspection by trained specialists. Automating this process could improve diagnostic workflows and reduce variability in test assessments.

Enhanced food authenticity control using machine learning-assisted elemental analysis.

Food research international (Ottawa, Ont.)
With the increasing attention being paid to the authenticity of food, efficient and accurate techniques that can solve relevant problems are crucial for improving public trust in food. This review explains two main aspects of food authenticity, namel...

Evaluation and process monitoring of jujube hot air drying using hyperspectral imaging technology and deep learning for quality parameters.

Food chemistry
Timely and effective detection of quality attributes during drying control is essential for enhancing the quality of fruit processing. Consequently, this study aims to employ hyperspectral imaging technology for the non-destructive monitoring of solu...