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AI tool outperforms humans in identifying stroke

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A team of researchers have developed a new artificial intelligence framework in order to address the number of strokes that go unrecognised by human emergency call handlers.

This new AI framework has already shown to outperform emergency call handlers in recognising stroke for both sexes and across all age groups studied, indicating its potential as a supplementary tool for early and precise stroke identification in the future.

The retrospective study, presented at the European Stroke Organisation Conference (ESOC) 2023, drew from the Danish Stroke Registry and a dataset of over1.5 million calls made to the Copenhagen Emergency Medical Services between 2015 and 2020, which included over 7000 stroke-related calls.

The researchers were able to utilise this data to train an AI framework to firstly transcribe the call audio and then predict the risk of stroke based on the transcribed text.

The results, evaluated from calls in 2021, revealed that the AI framework performed more effectively than emergency call handlers in identifying stroke cases.

The AI framework achieved a recall (sensitivity) of 63.0 per cent and a precision (positive predictive value) of 24.9 per cent, which resulted in an F1 score of 35.7. In contrast, emergency call handlers had a recall of 52.7 per cent and precision of 17.1 per cent, resulting in an F1 score of 25.8.

One of the lead authors of the study, Dr Jonathan Wenstrup, says: “As one of the first points of contact for patients seeking medical assistance, emergency call handlers play a critical role in facilitating early and accurate stroke recognition. Many stroke cases can go undetected at this stage, leading to delays in treatment that can have potentially life-threatening consequences for patients.

“With the implementation of this new, cost-effective supporting tool, we can enhance stroke identification by call handlers and ensure more patients receive appropriate and timely care, ultimately improving patient outcomes

“As with any new tool, further research and development are necessary to improve the framework’s accuracy and expand its capabilities. In the future, it may be possible to train the framework directly from the call audio, bypassing the transcription step, as well as incorporating non-word audio – such as a slurred voice – into the training data. However, given the promising results of this study, it is already clear that technologies like this have the capability to completely transform stroke diagnosis and care.”

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