AIMC Topic: Quality Control

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A Machine Learning Perspective on fNIRS Signal Quality Control Approaches.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Despite a rise in the use of functional Near Infra-Red Spectroscopy (fNIRS) to study neural systems, fNIRS signal processing is not standardized and is highly affected by empirical and manual procedures. At the beginning of any signal processing proc...

Assessment of patient based real-time quality control on comparative assays for common clinical analytes.

Journal of clinical laboratory analysis
BACKGROUND: It is critical for laboratories to conduct multianalyzer comparisons as a part of daily routine work to strengthen the quality management of test systems. Here, we explored the application of patient-based real-time quality controls (PBRT...

Traceable machine learning real-time quality control based on patient data.

Clinical chemistry and laboratory medicine
OBJECTIVES: Patient-based real-time quality control (PBRTQC) has gained attention as an alternative/integrative tool for internal quality control (iQC). However, it is still doubted for its performance and its application in real clinical settings. W...

Design and Implementation of Trace Inspection System Based upon Hyperspectral Imaging Technology.

Computational intelligence and neuroscience
Trace inspection is a key technology for collecting crime scenes in the criminal investigation department. A lot of information can be obtained by restoring and analyzing the remaining traces on the scene. However, with the development of digital tec...

A multi-model fusion algorithm as a real-time quality control tool for small shift detection.

Computers in biology and medicine
BACKGROUND: Patient-based real-time quality control (PBRTQC), a complement to traditional QC, may eliminate matrix effect from QC materials, realize real-time monitoring as well as cut costs. However, the accuracy of PBRTQC has not been satisfactory ...

Real-time coating thickness measurement and defect recognition of film coated tablets with machine vision and deep learning.

International journal of pharmaceutics
This paper presents a system, where images acquired with a digital camera are coupled with image analysis and deep learning to identify and categorize film coating defects and to measure the film coating thickness of tablets. There were 5 different c...

DARQ: Deep learning of quality control for stereotaxic registration of human brain MRI to the T1w MNI-ICBM 152 template.

NeuroImage
Linear registration to stereotaxic space is a common first step in many automated image-processing tools for analysis of human brain MRI scans. This step is crucial for the success of the subsequent image-processing steps. Several well-established al...

Image Quality Control in Lumbar Spine Radiography Using Enhanced U-Net Neural Networks.

Frontiers in public health
PURPOSE: To standardize the radiography imaging procedure, an image quality control framework using the deep learning technique was developed to segment and evaluate lumbar spine x-ray images according to a defined quality control standard.

Deep learning for spirometry quality assurance with spirometric indices and curves.

Respiratory research
BACKGROUND: Spirometry quality assurance is a challenging task across levels of healthcare tiers, especially in primary care. Deep learning may serve as a support tool for enhancing spirometry quality. We aimed to develop a high accuracy and sensitiv...

Near-Infrared Spectral Characteristic Extraction and Qualitative Analysis Method for Complex Multi-Component Mixtures Based on TRPCA-SVM.

Sensors (Basel, Switzerland)
Quality identification of multi-component mixtures is essential for production process control. Artificial sensory evaluation is a conventional quality evaluation method of multi-component mixture, which is easily affected by human subjective factors...