Alvaro Pascual-Leone, MD, PhD

"If you say, it’s mild cognitive impairment vs amnestic mild cognitive impairment, that makes a difference between an 80% risk of progression to dementia vs 10% or 30% risk. That’s a huge difference. If we can identify whether or not [it is amnestic]—in addition to signs of frailty—as well as characterize the whole person better, it allows us to be more fine-grained not only from a prognostic point of view, but for guiding interventions.”

For years, the Alzheimer disease community has attempted to find the most highly accurate combination of tools to predict clinical progression and trajectory of cognitive decline. Linus Health’s DCTclock is an FDA registered Class II medical device that assesses an individual’s cognitive health by analyzing more than 700 features of the clock drawing process using artificial intelligence and machine learning. Linus subsequently developed the Digital Clock and Recall Assessment, which incorporates voice and delayed verbal recall alongside the DCTclock.

2022 Alzheimer’s Association International Conference (AAIC)

, July 31 to August 4, in San Diego, California, a large-scale, 495-patient, longitudinal trial evaluated the added benefit of the DCR and the relationship between voice analytics and diagnosis. Results showed that DCR was more highly correlated with Mini-Mental State Examination scores than DCTclock (

= 0.43 vs 0.38), but also, that combining acoustic features with cognitive assessments achieved greater classification accuracy for healthy (area under the curve [AUC], 0.95), mild cognitively impaired (AUC, 0.93), and Alzheimer disease (AUC, 0.97) groups than models including either acoustic features or cognitive assessments alone.

, believes this type of approach has serious clinical implications, considering most of the measures used are standard and widely available in the field. Pascual-Leone, who is chief medical officer and cofounder of Linus Health, provided additional background on application of acoustic and speech production to help classify cognitive status, as well as why these results serve as recommendations for future decision-making.

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REFERENCE
1. Banks R, Jannati A, Gomes-Osman J, et al. Enhanced machine learning classification of mild cognitive impairment with multimodal digital biomarkers. Presented at: 2022 Alzheimer’s Association International Conference; July 31-Aug 4; San Diego, CA. 60485

Videos

The chief medical officer and cofounder of Linus Health provided insight on the outlook of acoustic measures and machine learning to further classify cognitive impairment. [WATCH TIME: 4 minutes]

Incorporating Voice Analytics Into Cognitive Assessments: Alvaro Pascual-Leone, MD, PhD

"We thought it would be paramount that the assessment is short, works well in the workflow of primary care physicians, and frankly, that the assessment is something that they are familiar with and has good backing in the literature.”

Previously conducted studies have used acoustic and speech production as a means to classify cognitive status, but results have been inconsistent. At the 

, held July 31 to August 4, in San Diego, California, one study examined if the addition of speech production metrics can increase the classification accuracy of cognitive impairment. The group of investigators, led by 

, created an enhanced machine learning algorithm using several multimodal digital biomarkers that categorized patients as either cognitively healthy (HC)-cognitively impaired (CI), HC-probable Alzheimer disease (AD), HC-mild cognitive impairment (MCI), and MCI-AD.

At the conclusion of the analysis, statistical models that included both digital clock drawing metrics and 3-word speech production analysis to classify cognitive status outperformed models that included only digital clock drawing metrics. The study included several cognitive assessments and metrics, including the Rey Auditory Verbal Learning Test, Delayed Recall, Mini-Mental State Exam, physician diagnosis, Functional Activities Questionnaire, Digital Clock Test, and Digital Clock and Recall.

Pascual-Leone, chief medical officer and co-founder of Linus Health, sat down with 

 to answer questions on the reasons behind the metrics chosen. He also discussed the need to find the right combination of assessments and how this translates to clinical care.

The chief medical officer and cofounder of Linus Health provided background on the reasoning for the measures used in a new machine learning algorithm that classifies cognitive status. [WATCH TIME: 3 minutes]

Formulating the Right Combination of Acoustic Metrics to Quantify Cognitive Impairment: Alvaro Pascual-Leone, MD, PhD

"If you want to capture overall cognitive performance with the greatest sensitivity, you don’t want to use only one metric. You want to capture multiple domains. That’s why the idea is to use multimodal digital biomarkers, not just voice and the hundreds of metrics that can be extracted from it."

Patients with mild cognitive impairment (MCI) are at risk of conversion to dementia, and thus, classifying their cognitive status may be beneficial in guiding treatment and prevention strategies. A study presented at the 

2022 Alzheimer’s Association International Conference

, July 31 to August 4, in San Diego, California, examined if speech production metrics—frequency, loudness, and voice quality—can increase the accuracy of classifying cognitively healthy individuals and MCI subtypes.

The study included several cognitive assessments such as the Rey Auditory Verbal Learning Test, Mini-Mental State Examination, Functional Activities Questionnaire, Digital Clock Test, Digital Clock and Recall, and physician diagnosis. With the inclusion of speech and voice analysis, the classification accuracy was improved, with 4 cognitive statuses identified: HC – cognitively impaired (CI), HC – probably Alzheimer disease (AD), HC-MCI, and MCI-AD.

To learn more about the background of the study and its direct clinical implications, 

 sat down with senior investigator Alvaro Pascual-Leone, MD, PhD. Pascual-Leone currently serves as the medical director of the Deanna and Sidney Wolk Center for Memory Health and a senior scientist at the Hinda and Arthus Marcus Institute for Aging Research at Hebrew SeniorLife, as well as a professor of neurology at Harvard Medical School. He also answered questions about the use of speech production metrics in the AD field, and whether there needs to be more attention given to these approaches.

The medical director of the Deanna and Sidney Wolk Center for Memory Health provided background on his new study using multimodal digital biomarkers to classify patients with mild cognitive impairment. [WATCH TIME: 5 minutes]

Alvaro Pascual-Leone, MD, PhD

Articles

Incorporating Voice Analytics Into Cognitive Assessments: Alvaro Pascual-Leone, MD, PhD

Formulating the Right Combination of Acoustic Metrics to Quantify Cognitive Impairment: Alvaro Pascual-Leone, MD, PhD

Using Machine Learning, Speech Production to Quantify Mild Cognitive Impairment: Alvaro Pascual-Leone, MD, PhD

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