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ALS TDI<\/p>\nALS TDI<\/p>\n
CAMBRIDGE, MA<\/strong> – One of the most important goals of the ALS Therapy Development Institute\u2019s (ALS TDI) Precision Medicine Program (PMP) since its inception has been to discover objective measures of Amyotrophic Lateral Sclerosis (ALS) progression. In late January, ALS TDI and its collaborators at Google made an important step toward making this goal a reality for people living with ALS with the preprint publication of a paper describing their research to create artificial intelligence tools for scoring ALS-related symptom severity.<\/p>\n The paper, titled A Machine-Learning Based Objective Measure for ALS Disease Severity, details how data from the PMP was utilized by ALS TDI scientists and Google AI researchers to develop a machine learning algorithm that can analyze voice recordings and accelerometer movement data. This algorithm is able to look at these data and assign scores corresponding to an individual\u2019s speech and movement abilities, potentially allowing them to accurately and, importantly, objectively track changes in these abilities over time.<\/p>\n For years, the ALS Functional Rating Scale (ALSFRS-r) has been the main tool for clinicians and researchers to measure ALS disease progression. Although it is the current standard, the ALSFRS-r is limited by its subjectivity \u2013 it relies on either a clinician or subject\u2019s self-reported observations of how well they can move, speak, and perform other functions. This machine learning algorithm, on the other hand, can provide an objective numerical score that should be internally consistent throughout a person\u2019s disease progression.<\/p>\n This is possible because of the combination of Google\u2019s expertise in artificial intelligence and machine learning with the wealth of data provided by participants in ALS TDI\u2019s PMP. To develop the algorithm, the paper\u2019s authors utilized thousands of voice recordings, accelerometer samples, and years self-reported ALSFRS-r scores from 584 people living with ALS. The results are two algorithms that can predict a person\u2019s ALSFRS-r score in the movement and speech ALSFRS-r categories with a strong correlation to their self-reported scores.<\/p>\nCreating an Objective Measure for ALS Symptom Severity<\/h3>\n
The Critical Role of AI and PMP Participant Data<\/h3>\n
Sharing Findings with the Community<\/h3>\n