New smartphone speech app enables early detection of Alzheimer’s through self-administration

Published Date: May 30, 2023 |

New smartphone speech app enables early detection of Alzheimer's through self-administration

A novel smartphone app has been created by researchers, allowing individuals to self-administer a screening for neurodegenerative disorders such as Alzheimer’s disease and mild cognitive impairment. By analyzing speech patterns, the app can detect subtle disturbances that serve as early indicators of these conditions. This innovative approach offers a convenient and efficient means of obtaining a faster diagnosis.

Even though Alzheimer’s disease (AD) is widely prevalent across the globe, it is estimated that 75% of individuals affected by it remain undiagnosed. One of the initial indications of AD typically involves language impairment. During the early stages, individuals may experience speech difficulties such as stuttering, pauses, and struggles with word recall or finding appropriate words to express their thoughts.

Leveraging technology to capture the subtle alterations in an individual’s voice is a valuable approach in aiding doctors to diagnose Alzheimer’s disease (AD) and mild cognitive impairment (MCI) at an early stage. Early diagnosis holds the potential to enhance the effectiveness of interventions that can decelerate the progression of the disease. Nevertheless, discerning speech patterns in older individuals can pose a challenge.

A collaborative effort between the University of Tsukuba, Japan, and IBM Research led to the creation of a self-administered prototype smartphone app designed to effectively analyze speech patterns and identify early signs of Alzheimer’s disease (AD) and mild cognitive impairment (MCI). The research team gathered speech data from 114 individuals, including 25 diagnosed with AD, 46 with MCI, and 43 cognitively healthy participants. The participants, aged between 72 and 75 years, were seated in a quiet room where they responded to pre-recorded questions, with their answers being recorded on an iPad.

During the study, the participants were engaged in five speech tasks, including counting backwards, subtraction, verbal fluency exercises, and picture description. To transcribe their responses, the researchers utilized the IBM Watson Speech-to-Text automatic speech recognition service. Subsequently, the recordings were thoroughly analyzed for various speech characteristics such as jitter (short-term pitch variations), shimmer (short-term loudness variations), speech rate, intonation, and pauses. Machine learning techniques were employed to classify the three groups—AD, MCI, and control—based on the extracted speech features. In total, 92 speech features were inputted into the classification model by the researchers for each task.

Significant variations in speech patterns were observed by the researchers between the control participants and those diagnosed with Alzheimer’s disease (AD) or mild cognitive impairment (MCI). Notably, the machine learning model achieved an impressive accuracy of 91% for detecting AD and 88% for detecting MCI. This study represents the first demonstration of the feasibility of utilizing an automatic, self-administered tool that employs speech analysis as a marker for detecting AD and MCI. The researchers suggest conducting further investigations to examine whether the speech variations identified by their app correspond to the pathological changes observed in these conditions, such as levels of tau and amyloid beta.

The researchers acknowledge certain limitations in their study. Firstly, the collection of speech data in a laboratory setting may have influenced the participants’ responses to the questions, potentially impacting the results. Secondly, the sample size was relatively small, which limits the extent to which the findings can be generalized. Nevertheless, the research effectively demonstrates the potential of utilizing speech analysis through a self-administered smartphone app as a screening tool for these debilitating diseases.

Market Impacts:

The development of a self-administered smartphone app that can accurately analyze speech patterns for early detection of Alzheimer’s disease (AD) and mild cognitive impairment (MCI) can have a significant impact on the Alzheimer’s disease diagnostics market.

Improved Early Detection: Early detection is crucial in managing and treating Alzheimer’s disease effectively. With the app’s ability to identify subtle speech disturbances indicative of AD and MCI, individuals can seek medical attention at an earlier stage. This can lead to timely interventions, improved patient outcomes, and potentially slower disease progression. Consequently, the demand for early diagnostic tools like the smartphone app is likely to increase.

Convenience and Accessibility: The self-administered nature of the smartphone app allows individuals to screen themselves for AD and MCI conveniently, without the need for specialized medical facilities or extensive testing procedures. This enhanced accessibility can encourage more people to proactively monitor their cognitive health and seek professional diagnosis when necessary. It may also benefit individuals in remote or underserved areas with limited access to healthcare resources.

Market Expansion: The introduction of innovative technologies like the smartphone app can expand the Alzheimer’s diagnosis market by attracting new users. Individuals who might have been hesitant to undergo traditional diagnostic tests due to cost, inconvenience, or stigma associated with visiting healthcare facilities may be more inclined to utilize the app. This expansion of the market can lead to increased investment, research, and development in the field of Alzheimer’s diagnostics.

Potential for Personalized Care: The app’s ability to capture and analyze speech patterns opens the door to personalized care and treatment plans. By identifying specific speech impairments, healthcare professionals can tailor interventions to address individual needs. This personalized approach can enhance the effectiveness of therapies, improve patient experiences, and potentially lead to better long-term outcomes.

Research and Collaboration: The development and implementation of the smartphone app will likely spur further research and collaboration in the field of Alzheimer’s diagnostics. It can serve as a catalyst for interdisciplinary studies involving speech analysis, machine learning, neurology, and other related areas. This collaboration can foster advancements in understanding the disease, refining diagnostic techniques, and developing more accurate and reliable tools for early detection.

In summary, the self-administered smartphone app’s impact on the Alzheimer’s disease diagnostics market is expected to be positive, leading to improved early detection, increased accessibility, market expansion, personalized care, and research advancements. These developments have the potential to transform the landscape of Alzheimer’s diagnostics, benefitting individuals, healthcare providers, and researchers alike.

Source: University of Tsukuba

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