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Multiple Sclerosis l: Introduction01:19

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Multiple sclerosis is a chronic autoimmune disease of the central nervous system (CNS) that affects the brain, spinal cord, and optic nerves. It is an inflammatory demyelinating disorder and a leading cause of neurological disability in young adults.EpidemiologyMS commonly begins between 20 and 40 years of age and is twice as common in women. Its exact cause remains unclear, but genetic susceptibility contributes, with higher risk in first-degree relatives and identical twins. A greater...
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Detecting New Lesions Using a Large Language Model: Applications in Real-World Multiple Sclerosis Datasets.

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  • 1UCSF Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA.

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This summary is machine-generated.

A new large language model prompt efficiently extracts key data from multiple sclerosis (MS) MRI reports. This tool aids in monitoring treatment response and identifying disease activity predictors.

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Area of Science:

  • Artificial Intelligence in Medicine
  • Neuroimaging Analysis
  • Multiple Sclerosis Research

Background:

  • Neuroimaging is crucial for detecting new inflammatory activity in multiple sclerosis (MS).
  • Electronic health records (EHRs) contain valuable narrative MRI reports that are challenging to analyze for research.
  • Automating the extraction of discrete data from these reports can significantly benefit MS research.

Purpose of the Study:

  • To develop a large language model (LLM) prompt for classifying narrative MRI reports.
  • To assess the prompt's accuracy and efficiency in identifying new T2-weighted lesions (newT2w) and contrast-enhancing lesions (CEL).
  • To demonstrate the LLM's application in monitoring B-cell depleting therapy (BCDT) response in MS patients.

Main Methods:

  • An institutional ecosystem integrated healthcare data with ChatGPT4 for analyzing MS MRI reports (2000-2022).
  • A refined LLM prompt (msLesionprompt) was created to classify newT2w and CEL.
  • Validation included efficiency assessment (time, cost), comparison with manual annotations, and analysis of BCDT treatment predictors.

Main Results:

  • The msLesionprompt achieved high accuracy for newT2w (97%) and CEL (96.8%) detection.
  • 14,888 reports were processed in 4.13 hours at a cost of $28, with 79% showing no new activity.
  • BCDT demonstrated >97% suppression of new inflammatory activity post-rebaseline scan.
  • Neighborhood poverty (Area Deprivation Index) emerged as a predictor for inflammatory activity.

Conclusions:

  • LLM-driven extraction of discrete information from narrative imaging reports is feasible and efficient.
  • This automated approach can enhance real-world analyses of MS disease progression and treatment effectiveness.
  • The methodology holds potential for augmenting various research applications in MS.