March/April 2016

Toward Dementia Diagnosis via Artificial Intelligence
By Frank Rudzicz, PhD
Today's Geriatric Medicine
Vol. 9 No. 2 P. 8

Alzheimer's disease (AD) is the most common type of dementia and affects 44 million people worldwide; this is projected to triple by 2050.1 Assessing for AD remains an expensive and laborious process that is completely unsustainable given the rapidly aging populations of many nations. Even with the savings associated with early assessment, the amortized cost for each diagnosis is currently at least $6,000,2 which does not take into account indirect costs such as lost time by patients (and their caregivers) in travel, wait times, and hours spent in assessment, which is a process so laborious and stressful that it is often repeated only every year, or even less frequently. This is doubly unfortunate because, given the high variability of symptoms in AD, it often cannot be ascertained accurately from a single assessment,3 especially in its early stages. Repeatable, remote, and cost-effective assessment is therefore essential.

Modern artificial intelligence (AI) research is producing tools that will help to solve this challenge. In particular, machine learning, a subarea of AI, is developing algorithms that can accurately learn models of real-world phenomena, or categorical differences among those phenomena, from representative data. Progress in this area has recently accelerated dramatically, due to the discovery of new algorithms in artificial neural networks, the availability of powerful hardware capable of vast parallel processing, and the wide proliferation of data across a variety of domains, including health care. In particular, recent advances are showing that AI can be used to assess and monitor AD and other types of dementia, using no more than simple measures of a speaker's voice.

Language in AD
Although memory impairment is the main symptom of AD, changes to language often appear shortly thereafter4 and can serve as a sensitive index of the severity of the disease over time. According to standard protocols for testing specific language disorders called aphasias, 36% of individuals with mild AD and 100% of those with severe AD also have specific language impairments.5 In a majority of participants, subtle yet significant changes to the production of continuous speech can occur more than one year before AD is officially diagnosed.6 Weiner et al showed that the severity of dementia correlates significantly with several linguistic measures, including articulation, word-finding ability, and semantic fluency.7 Moreover, in some cases, even when language is typical according to other measures, naming can become difficult for people with AD,8 especially with regard to the names of people and places,9 or with regard to verbs/actions instead of nouns.10

AD affects not only the words and sounds of speech but also higher-level aspects. For example, discourse-level aspects such as understanding metaphor, sarcasm,11 and global coherence12 are negatively impacted in AD. Grammar is also diminished, including reduced syntactic complexity13 and fewer subordinate clauses.14

Crucially, whether an individual becomes more impaired in either his or her grammar or word finding can be indicative of the location of the etiology. For example, damage to Broca's area (Brodmann area 44/45) in the left frontal lobe can result in agrammatical speech, whereas damage to Wernicke's area (Brodmann area 22), in the posterior superior temporal gyrus can reduce the ability to understand or produce meaningful language. Our own work in the linguistic analysis of AD shows that syntax, semantics, and even acoustic properties of the voice can be differentially impacted, and are indicative of this disease.15

Computational Approaches to Assessing Dementia
Many of the aspects of speech that are indicative of typical aging, as compared with dementia, are easily and accurately measured by modern computational linguistics, which is the branch of AI concerned with algorithms and statistical models that represent language. Diagnosis, or at least assessment, can in many cases be facilitated by those computational approaches.

While still in its infancy, assessing clinically relevant aspects of cognition through automated analysis of language is growing in popularity. Bucks et al could identify individuals with AD, from a cohort of eight, with 87.5% accuracy by applying eight linguistic features within a linear discriminant analysis.16 Along these lines, Guinn and Habash built software that identified AD, with up to 79.5% accuracy within 80 conversations and found that measuring the syntactic categories of words (eg, proper nouns, gerund verbs) was less useful than more acoustically identifiable measures such as filled pauses, repetition, and incomplete words.17 Jarrold et al also used acoustic features but combined these with syntactic word categories and psychologically motivated word lists, obtaining 88% accuracy.18

Members of the computational linguistics group at the University of Toronto have recently shown that the early warning signs of dementia were detectable algorithmically through diachronic changes in literary writing19 and were sensitive even to individual writing style.20 At the Toronto Rehabilitation Institute and the Rotman Research Institute, the author of this article and colleagues have expanded on that work, combining a vast collection of linguistic measurements of speech with modern machine learning to develop computational methods that accurately identify primary progressive aphasia and its subtypes,21 Parkinson's disease,22 indications of confusion in spoken dialogue in AD,23 and AD itself.15 The same core algorithms are widely applicable across pathologies and are exceedingly accurate, as long as appropriate linguistic measures are selected by machine learning, given sufficient amounts of data. We are currently in the process of commercializing these technologies in a company called Winterlight Labs.

The Future of AI in Clinical Practice
While an increasing number of laboratories are demonstrating that AI is capable of accurately detecting various cognitive disorders, even from short samples of speech, fundamental ethical considerations remain to be answered before these technologies can be used in medical practice. If diagnoses are increasingly aided by software, then who will bear responsibility for incorrect medical decisions if, in some sense, those decisions are merely the product of statistics and the data from which they are derived? If medically relevant information can be obtained from recordings in one's home or from new wearable technologies, then how will individual privacy be maintained? Will these technologies be available only to practitioners of medicine or will members of the general public also have access? Inequalities of access, potential loss of privacy, and unrestrained faith in technology are serious risks that ought to be considered now because, if the benefits of quantifiable, repeatable, inexpensive, and accurate diagnosis by AI outweigh those risks, then a fundamental change to health care is inevitable.

— Frank Rudzicz, PhD, is a scientist at the Toronto Rehabilitation Institute in the University Health Network, an assistant professor of computer science at the University of Toronto, and cofounder of Winterlight Labs Inc. He is the president of the international joint ACL/ISCA Special Interest Group on Speech and Language Processing for Assistive Technologies, and Young Investigator of the Alzheimer's Society. His work involves machine learning, human-computer interaction, speech-language pathology, rehabilitation engineering, signal processing, and linguistics.

References
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