AI Medical Compendium Topic

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Equipment Failure

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Leveraging feature extraction and risk-based clustering for advanced fault diagnosis in equipment.

PloS one
In the contemporary manufacturing landscape, the advent of artificial intelligence and big data analytics has been a game-changer in enhancing product quality. Despite these advancements, their application in diagnosing failure probability and risk r...

Enhancing mechanical ventilator reliability through machine learning based predictive maintenance.

Technology and health care : official journal of the European Society for Engineering and Medicine
BackgroundWith the advancement of Artificial Intelligence (AI), clinical engineering has witnessed transformative opportunities, enabling predictive maintenance of medical devices, optimization of healthcare workflows, and personalized patient care. ...

Fault Early Warning Model for High-Speed Railway Train Based on Feature Contribution and Causal Inference.

Sensors (Basel, Switzerland)
The demands for model accuracy and computing efficiency in fault warning scenarios are increasing as high-speed railway train technology continues to advance. The black box model is difficult to interpret, making it impossible for this technology to ...

A Deep Learning Model with Signal Decomposition and Informer Network for Equipment Vibration Trend Prediction.

Sensors (Basel, Switzerland)
Accurate equipment operation trend prediction plays an important role in ensuring the safe operation of equipment and reducing maintenance costs. Therefore, monitoring the equipment vibration and predicting the time series of the vibration trend is o...

Novel glassbox based explainable boosting machine for fault detection in electrical power transmission system.

PloS one
The reliable operation of electrical power transmission systems is crucial for ensuring consumer's stable and uninterrupted electricity supply. Faults in electrical power transmission systems can lead to significant disruptions, economic losses, and ...

Evaluation of Machine Translation Accuracy Focused on the Adverse Event Terminology for Medical Devices.

Studies in health technology and informatics
The purpose of this study was to evaluate the accuracy of deep neural machine translation focused on medical device adverse event terminology. 10 models were obtained, and their English-to-Japanese translation accuracy was evaluated using quantitativ...

Cross-domain zero-shot learning for enhanced fault diagnosis in high-voltage circuit breakers.

Neural networks : the official journal of the International Neural Network Society
Ensuring the stability of high-voltage circuit breakers (HVCBs) is crucial for maintaining an uninterrupted supply of electricity. Existing fault diagnosis methods typically rely on extensive labeled datasets, which are challenging to obtain due to t...

Feasibility of Artificial Intelligence Powered Adverse Event Analysis: Using a Large Language Model to Analyze Microwave Ablation Malfunction Data.

Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
Determine if a large language model (LLM, GPT-4) can label and consolidate and analyze interventional radiology (IR) microwave ablation device safety event data into meaningful summaries similar to humans. Microwave ablation safety data from Januar...

Advancement of post-market surveillance of medical devices leveraging artificial intelligence: Patient monitors case study.

Technology and health care : official journal of the European Society for Engineering and Medicine
BackgroundHealthcare institutions throughout the world rely on medical devices to provide their services reliably and effectively. However, medical devices can, and do sometimes fail. These failures pose significant risk to patients.ObjectiveOne way ...

Deep learning algorithms for detecting fractured instruments in root canals.

BMC oral health
BACKGROUND: Identifying fractured endodontic instruments (FEIs) in periapical radiographs (PAs) is a critical yet challenging aspect of root canal treatment (RCT) due to anatomical complexities and overlapping structures. Deep learning (DL) models of...