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Improving care for amyotrophic lateral sclerosis with artificial intelligence and affective computing.

Marc Garbey1, Quentin Lesport2, Gülşen Öztosun3

  • 1Department of Surgery, George Washington University School of Medicine & Health Sciences, Washington, DC, USA; Care Constitution Corp, Houston, TX, USA; Laboratoire des Sciences de l'Ingénieur pour l'Environnement (LaSIE) UMR-CNRS 7356 University of La Rochelle, France.

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Artificial intelligence (AI) tools analyzed speech and pulse data to detect emotional responses in Amyotrophic Lateral Sclerosis (ALS) patients. Financial concerns elicited the strongest emotional reactions, offering new insights into patient well-being.

Keywords:
Affective computingAmyotrophic lateral sclerosisClinical trialGenerative languageNatural language processingSignal analysisTelemedicine

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Area of Science:

  • Neurology
  • Affective Computing
  • Artificial Intelligence

Background:

  • Patients with Amyotrophic Lateral Sclerosis (ALS) exhibit challenges in emotional expression due to neurological impairments affecting facial, vocal, and non-verbal communication.
  • Cognitive function deficits in ALS can further complicate the accurate assessment of emotional states.
  • Objective measures are needed to understand and quantify emotional responsiveness in ALS for improved care and research.

Purpose of the Study:

  • To develop and validate non-invasive artificial intelligence (AI) tools for detecting and quantifying emotional responsiveness in individuals with ALS.
  • To provide objective insights into the emotional experiences of ALS patients.
  • To enhance patient-provider communication, telemedicine efficacy, and clinical trial outcome measures.

Main Methods:

  • A preliminary study involved fourteen ALS patients who underwent audio recordings and wore wireless pulse oximeters during clinic visits.
  • Emotion-triggering questions concerning symptom progression, breathing, mobility, feeding tubes, and financial burden were posed.
  • Natural Language Processing (NLP) analyzed speech transcriptions for sentiment and emotional states, integrating pulse data to identify emotional arousal patterns.

Main Results:

  • Pulse oximeter data revealed alterations consistent with emotional arousal, with longer, positive interactions reducing pulse fluctuations.
  • Financial burden discussions triggered the most significant emotional responses, while topics like breathing and mobility increased anxiety.
  • AI-generated reports effectively summarized patient concerns and streamlined clinical documentation.

Conclusions:

  • This study presents a novel method combining pulse and speech analysis using AI to assess emotional responses in ALS patients.
  • AI and affective computing offer valuable insights into emotional states and disease progression, applicable to other neurological conditions.
  • This approach has the potential to improve clinical trial outcomes by providing a more holistic view of patient well-being.