Beyond neuropsychological tests: AI speech analysis in PKU.

Autor: Waisbren SE; Division of Genetics and Metabolism, Boston Children's Hospital, Boston, Massachusetts, USA.; Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA., Norel R; Division of Digital Health, IBM TJ Watson Research Center, Yorktown Heights, New York, USA., Agurto C; Division of Digital Health, IBM TJ Watson Research Center, Yorktown Heights, New York, USA., Singh S; Institute for Technology in Psychiatry, McLean Hospital, Belmont, Massachusetts, USA.; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts, USA., Connor ZA; Division of Genetics and Metabolism, Boston Children's Hospital, Boston, Massachusetts, USA., Ebrahim MG; Division of Genetics and Metabolism, Boston Children's Hospital, Boston, Massachusetts, USA.; Tufts University, Medford, Massachusetts, USA., Cecchi GA; Division of Digital Health, IBM TJ Watson Research Center, Yorktown Heights, New York, USA.
Jazyk: angličtina
Zdroj: Journal of inherited metabolic disease [J Inherit Metab Dis] 2025 Jan; Vol. 48 (1), pp. e12831.
DOI: 10.1002/jimd.12831
Abstrakt: Phenylketonuria (PKU) is a rare inherited metabolic disorder characterized by toxic phenylalanine (Phe) concentrations in blood and brain. State-of-the-art analyses of speech detected a dimension of verbal discourse providing insights that extend beyond those captured by existing paradigms to measure performance associated with biochemical markers in PKU. The Cookie Theft Picture Task provided a standardized stimulus for eliciting spontaneous speech from 42 adults with PKU and 41 adults without PKU. Subtests measuring language and memory from the Wechsler Adult Intelligence Scale-Fourth Edition showed no differences between the groups and no correlations with biomarkers in PKU. In contrast, AI analyses of responses to the Cookie Theft Task revealed significant differences between the PKU and non-PKU groups on 23 linguistic features. Using multidimensional scaling (MDS), these features were aggregated into a single quantifiable Dimension 1 that significantly correlated with biomarkers. When extreme examples of Dimension 1 were presented to chatGPT, the differences noted reflected attention to detail, clarity in word choice, expression cohesion, contextual awareness and emotion recognition. We subsequently defined Dimension 1 as Proficiency in Verbal Discourse. This novel measure elucidated discourse styles possibly associated with suboptimal achievement and learning disabilities, often reported in PKU. In summary, AI captured a characteristic associated with metabolic status undetectable through traditional neuropsychological measures. Future studies will expand upon this novel paradigm, leveraging speech AI to quantify meaningful aspects of everyday functioning and possibly provide information for management decisions. Once validated, this measure holds promise for extension to other rare diseases and incorporation into clinical trials.
(© 2024 SSIEM.)
Databáze: MEDLINE
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