AIMC Topic: Quality Control

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Automated liver magnetic resonance elastography quality control and liver stiffness measurement using deep learning.

Abdominal radiology (New York)
PURPOSE: Magnetic resonance elastography (MRE) measures liver stiffness for fibrosis staging, but its utility can be hindered by quality control (QC) challenges and measurement variability. The objective of the study was to fully automate liver MRE Q...

Recommendations for Artificial Intelligence Application in Continued Process Verification: A Journey Toward the Challenges and Benefits of AI in the Biopharmaceutical Industry.

PDA journal of pharmaceutical science and technology
This review paper explores the transformative impact of Artificial Intelligence (AI) on Continued Process Verification (CPV) in the biopharmaceutical industry. Originating from the CPV of the Future project, the study investigates the challenges and ...

Near-infrared spectroscopy assisted by random forest for predicting the physicochemical indicators of yak milk powder.

Food chemistry
High-efficiency and cost-effective detection of physicochemical indicators is essential for the quality control of yak milk powder. Herein, a rapid and simultaneous detection method based on miniaturized near-infrared (NIR) spectroscopy and chemometr...

An quality evaluation method based on three-dimensional integration and machine learning: Advanced data processing.

Journal of chromatography. A
This study presents an innovative approach for the quality evaluation of traditional Chinese medicine (TCM) by integrating three-dimensional (3D) data processing with machine learning, aimed at enhancing the efficiency and accuracy of HPLC-DAD data a...

A review: Integration of NIRS and chemometric methods for tea quality control-principles, spectral preprocessing methods, machine learning algorithms, research progress, and future directions.

Food research international (Ottawa, Ont.)
With the steady rise in tea production, the need for effective tea quality monitoring has become increasingly pressing. Traditional sensory evaluation and wet chemical detection methods are insufficient for real-time tea quality monitoring. As an eme...

Monitoring Immunohistochemical Staining Variations Using Artificial Intelligence on Standardized Controls.

Laboratory investigation; a journal of technical methods and pathology
Quality control of immunohistochemistry (IHC) slides is crucial to ascertain accurate patient management. Visual assessment is the most commonly used method to assess the quality of IHC slides from patient samples in daily pathology routines. Control...

A deep learning-based peer review method for radiotherapy planning.

Medical physics
BACKGROUND: Quality control (QC) in radiotherapy planning is crucial for ensuring treatment efficacy and patient safety. Traditionally, QC relies on standard indicators and subjective assessments, which may lead to inconsistencies.

Integrating hyperspectrograms with class modeling techniques for the construction of an effective expert system: Quality control of pharmaceutical tablets based on near-infrared hyperspectral imaging.

Journal of pharmaceutical and biomedical analysis
Near-infrared hyperspectral imaging (NIR-HSI) integrated with expert systems can support the monitoring of active pharmaceutical ingredients (APIs) and provide effective quality control of tablet formulations. However, existing quality control method...

iDIA-QC: AI-empowered data-independent acquisition mass spectrometry-based quality control.

Nature communications
Quality control (QC) in mass spectrometry (MS)-based proteomics is mainly based on data-dependent acquisition (DDA) analysis of standard samples. Here, we collect 2754 files acquired by data independent acquisition (DIA) and paired 2638 DDA files fro...

Efficient and accurate commissioning and quality assurance of radiosurgery beam via prior-embedded implicit neural representation learning.

Medical physics
BACKGROUND: Dosimetric commissioning and quality assurance (QA) for linear accelerators (LINACs) present a significant challenge for clinical physicists due to the high measurement workload and stringent precision standards. This challenge is exacerb...