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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

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Supervised deep learning with vision transformer predicts delirium using limited lead EEG.

Malissa A Mulkey¹, Huyunting Huang², Thomas Albanese³

¹College of Nursing, University of South Carolina, Columbia, SC, USA. malissam@mailbox.sc.edu.

Scientific Reports

|May 16, 2023

Summary

Critically ill patients often develop delirium, but it

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Area of Science:

Critical Care Medicine
Neuroscience
Artificial Intelligence in Healthcare

Background:

Delirium affects up to 80% of critically ill patients, leading to increased institutionalization, morbidity, and mortality.
Current clinical detection rates for delirium are below 40%, despite using validated screening tools.
Electroencephalography (EEG) is the gold standard for delirium detection but is too resource-intensive for routine monitoring.

Purpose of the Study:

To evaluate the efficacy of a rapid-response, limited-lead EEG combined with supervised deep learning using a vision transformer model for predicting delirium.
To assess the feasibility of this novel approach for delirium monitoring in mechanically ventilated, critically ill older adults.

Main Methods:

Prospective proof-of-concept study involving mechanically ventilated, critically ill older adults.
Utilized a rapid-response EEG device and supervised deep learning models, specifically a vision transformer architecture.
Analyzed fifteen different models to predict delirium.

Main Results:

Vision transformer models achieved 99.9%+ training accuracy and 97% testing accuracy when using all available data.
The combination of rapid-response EEG and vision transformer demonstrated capability in predicting delirium.
The monitoring approach was found to be feasible in the target patient population.

Conclusions:

Rapid-response EEG coupled with vision transformer deep learning shows significant potential for accurate delirium prediction in critically ill older adults.
This method could enhance delirium detection, enabling timely, individualized interventions.
Potential benefits include reduced hospital length of stay, increased home discharge rates, decreased mortality, and lower healthcare costs.