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Effectiveness of tuning an artificial intelligence algorithm for cerebral aneurysm diagnosis: a study of 10,000 consecutive cases. | LitMetric

AI Article Synopsis

  • A study evaluated the effectiveness of an artificial intelligence algorithm for diagnosing unruptured cerebral aneurysms, finding that while it has high sensitivity, there are still too many false positives.
  • Researchers analyzed 10,000 MRI scans to compare aneurysm detection rates before and after the algorithm was tuned, revealing a slight decrease in sensitivity but a significant reduction in false positives.
  • The results showed that by fine-tuning the AI algorithm, the number of false positives dropped from around 2.06 to 0.99 per case, with a minimal change in sensitivity, demonstrating improved diagnostic accuracy.

Article Abstract

Diagnostic image analysis for unruptured cerebral aneurysms using artificial intelligence has a very high sensitivity. However, further improvement is needed because of a relatively high number of false positives. This study aimed to confirm the clinical utility of tuning an artificial intelligence algorithm for cerebral aneurysm diagnosis. We extracted 10,000 magnetic resonance imaging scans of participants who underwent brain screening using the "Brain Dock" system. The sensitivity and false positives/case for aneurysm detection were compared before and after tuning the algorithm. The initial diagnosis included only cases for which feedback to the algorithm was provided. In the primary analysis, the sensitivity of aneurysm diagnosis decreased from 96.5 to 90% and the false positives/case improved from 2.06 to 0.99 after tuning the algorithm (P < 0.001). In the secondary analysis, the sensitivity of aneurysm diagnosis decreased from 98.8 to 94.6% and the false positives/case improved from 1.99 to 1.03 after tuning the algorithm (P < 0.001). The false positives/case reduced without a significant decrease in sensitivity. Using large clinical datasets, we demonstrated that by tuning the algorithm, we could significantly reduce false positives with a minimal decline in sensitivity.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10533861PMC
http://dx.doi.org/10.1038/s41598-023-43418-xDOI Listing

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