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

Background: Patients with ALS often face difficulties expressing emotions due to impairments in facial expression, speech, body language, and cognitive function. This study aimed to develop non-invasive AI tools to detect and quantify emotional responsiveness in ALS patients, providing objective insights. Improved understanding of emotional responses could enhance patient-provider communication, telemedicine effectiveness, and clinical trial outcome measures.

Methods: In this preliminary exploratory study, fourteen patients with ALS had audio recordings performed during routine clinic visits while wearing a wireless pulse oximeter. Emotion-triggering questions related to symptom progression, breathing, mobility, feeding tube, and financial burden were randomly asked. The same questions were posed in separate psychiatric evaluations. Natural language processing (NLP) was used to analyze transcriptions, topic classifications, sentiment, and emotional states, combining pulse and speech data. AI-generated reports summarized the findings.

Results: Pulse alterations consistent with emotional arousal were identified, with longer consultations and positive communication reducing pulse fluctuations. Financial concerns triggered the strongest emotional response, while discussions about breathing, mobility, and feeding tube increased anxiety. AI-generated reports prioritized patient concerns and streamlined documentation for providers.

Conclusions: This study introduces a novel approach to linking pulse and speech analysis to evaluate emotional responses in ALS patients. AI and affective computing provide valuable insights into emotional responses and disease progression, with potential applications for other neurological disorders. This approach could augment clinical trial outcomes by offering a more comprehensive view of patient well-being.