Artificial intelligence could be ‘game changer’ in detecting, managing Alzheimer’s disease
Study introduces machine learning as new tactic in assessing cognitive brain health and patient care
Credit: Florida Atlantic University
Worldwide, about 44 million people are living with Alzheimer’s disease (AD) or a related form of dementia. Although 82 percent of seniors in the United States say it’s important to have their thinking or memory checked, only 16 percent say they receive regular cognitive assessments.
Many traditional memory assessment tools are widely available to health professionals, though deficiencies in screening and detection accuracy and reliability remain prevalent. But even with the increasingly favorable instrument MemTrax, a very simple online memory test using images recognition, the clinical efficacy of this new approach as a memory function screening tool has not been sufficiently demonstrated or validated. In practice, there are numerous integrated and complex factors to consider in interpreting memory evaluation test results, which presents a real challenge for clinicians. All these factors stand as a collective barrier to suitably addressing the growing and widespread prevalence of AD and those affected by the disease.
Could artificial intelligence be the solution for testing and managing this complex human health condition? A team of researchers at Florida Atlantic University’s College of Engineering and Computer Science, SIVOTEC Analytics, HAPPYneuron, MemTrax, and Stanford University School of Medicine, think so, and put their theory to the test.
The researchers employed a novel application of supervised machine learning and predictive modeling to demonstrate and validate the cross-sectional utility of MemTrax as a clinical decision support screening tool for assessing cognitive impairment.
Results of the study, published in the Journal of Alzheimer’s Disease, introduce supervised machine learning as a modern approach and new value-added complementary tool in cognitive brain health assessment and related patient care and management.
Findings demonstrate the potential valid clinical utility of MemTrax, administered as part of the online Continuous Recognition Tasks (M-CRT) test, in screening for variations in cognitive brain health. Notably, a comparison of MemTrax to the recognized and widely utilized Montreal Cognitive Assessment Estimation of mild cognitive impairment underscores the power and potential of this new online tool and approach in evaluating short-term memory in diagnostic support for cognitive screening and assessment with a variety of clinical conditions and impairments including dementia.
“Machine learning has an inherent capacity to reveal meaningful patterns and insights from a large, complex inter-dependent array of clinical determinants and the ability to continue to ‘learn’ from ongoing utility of practical predictive models,” said Taghi Khoshgoftaar, Ph.D., co-author and Motorola Professor in FAU’s Department of Computer and Electrical Engineering and Computer Science. “Seamless use and real-time interpretation will enhance case management and patient care through innovative technology and practical and readily usable integrated clinical applications that could be developed into a hand-held device and app.”
For the study, the researchers used an existing dataset (18,395) from HAPPYneuron. They examined answers to general health screening questions (addressing memory, sleep quality, medications, and medical conditions affecting thinking), demographic information, and test results from a sample of adults who took the MemTrax (M-CRT) test for episodic-memory screening. MemTrax performance and participant features were used as independent attributes: true positive/negative, percent responses/correct, response time, age, sex, and recent alcohol consumption. For predictive modeling, they used demographic information and test scores to predict binary classification of the health-related questions (yes/no) and general health status (healthy/unhealthy), based on the screening questions.
“Findings from our study provide an important step in advancing the approach for clinically managing a very complex condition like Alzheimer’s disease,” said Michael F. Bergeron, Ph.D., senior author and senior vice president of development and applications, SIVOTEC Analytics. “By analyzing a wide array of attributes across multiple domains of the human system and functional behaviors of brain health, informed and strategically directed advanced data mining, supervised machine learning, and robust analytics can be integral, and in fact necessary, for health care providers to detect and anticipate further progression in this disease and myriad other aspects of cognitive impairment.”
AD is the sixth leading cause of death in the United States, affecting 5.8 million Americans. According to the Alzheimer’s Association, this number is projected to rise to 14 million by 2050. In 2019, AD and other dementias will cost the nation $290 billion. By 2050, these costs could rise as high as $1.1 trillion.
“With its widespread prevalence and escalating incidence and public health burden, it is imperative to ensure that the tools clinicians use for testing and managing Alzheimer’s disease and other related cognitive conditions are optimal,” said Stella Batalama, Ph.D., dean of FAU’s College of Engineering and Computer Science. “Results from this important study provide new insights and discovery that has set the stage for future impactful and significant research.”
Co-authors of the study are Sara Landset, a Ph.D. student in FAU’s College of Engineering and Computer Science; Franck Tarpin-Bernard, Ph.D., HAPPYneuron, located in Lyon, France; Curtis B. Ashford, MemTrax, located in Redwood City, California; and J. Wesson Ashford, M.D., Ph.D., War-Related Illness and Injury Study Center, VA Palo Alto Health Care System, and Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine.
About FAU’s College of Engineering and Computer Science:
Florida Atlantic University’s College of Engineering and Computer Science is committed to providing accessible and responsive programs of education and research recognized nationally for their high quality. Course offerings are presented on-campus, off-campus, and through distance learning in bioengineering, civil engineering, computer engineering, computer science, electrical engineering, environmental engineering, geomatics engineering, mechanical engineering and ocean engineering. For more information about the college, please visit eng.fau.edu.
About Florida Atlantic University:
Florida Atlantic University, established in 1961, officially opened its doors in 1964 as the fifth public university in Florida. Today, the University, with an annual economic impact of $6.3 billion, serves more than 30,000 undergraduate and graduate students at sites throughout its six-county service region in southeast Florida. FAU’s world-class teaching and research faculty serves students through 10 colleges: the Dorothy F. Schmidt College of Arts and Letters, the College of Business, the College for Design and Social Inquiry, the College of Education, the College of Engineering and Computer Science, the Graduate College, the Harriet L. Wilkes Honors College, the Charles E. Schmidt College of Medicine, the Christine E. Lynn College of Nursing and the Charles E. Schmidt College of Science. FAU is ranked as a High Research Activity institution by the Carnegie Foundation for the Advancement of Teaching. The University is placing special focus on the rapid development of critical areas that form the basis of its strategic plan: Healthy aging, biotech, coastal and marine issues, neuroscience, regenerative medicine, informatics, lifespan and the environment. These areas provide opportunities for faculty and students to build upon FAU’s existing strengths in research and scholarship. For more information, visit fau.edu.