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Updated: Feb 27, 2026

Automated Sholl Analysis of Digitized Neuronal Morphology at Multiple Scales
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Automated morphological analysis of clinical language samples.

Kyle Gorman1, Steven Bedrick1, Géza Kiss1

  • 1Center for Spoken Language Understanding, Oregon Health & Science University, Portland, OR, USA.

Proceedings of the Conference. Association for Computational Linguistics. North American Chapter. Meeting
|July 11, 2017
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Summary

Automating language sample analysis with a new system provides accurate mean length of utterance in morphemes (MLUM) calculations. This technology enhances the assessment of developmental language impairments, improving clinical efficiency and care consistency.

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Area of Science:

  • Computational linguistics
  • Clinical linguistics
  • Speech-language pathology

Background:

  • Quantitative analysis of clinical language samples aids in assessing developmental language impairments.
  • Manual transcription, annotation, and calculation are time-consuming, error-prone, and lead to underutilization in clinical settings.

Purpose of the Study:

  • To describe a novel system for automated morphological analysis.
  • To enable direct computation of statistics like mean length of utterance in morphemes (MLUM) from orthographic transcripts.
  • To support increased automation in language sample analysis for improved clinical utility.

Main Methods:

  • Development of a system for automated morphological analysis.
  • Calculation of MLUM directly from orthographic transcripts.
  • Comparison of automated MLUM estimates with those from manual annotation.

Main Results:

  • Automated MLUM estimates closely comparable to manually derived values.
  • The system integrates with other automated annotation techniques like maze detection.
  • Demonstrated feasibility of automated language sample analysis.

Conclusions:

  • The developed system offers a viable alternative to manual analysis for MLUM calculation.
  • Automation in language sample analysis can increase clinical utilization.
  • Reduced variability in care delivery is an anticipated benefit of this automated approach.