A self-administered, artificial intelligence (AI) platform for cognitive assessment in multiple sclerosis (MS)

SpEx: a German-language dataset of speech and executive function performance

This work presents data from 148 German native speakers (20-55 years of age), who completed several speaking tasks, ranging from formal tests such as word production tests to more ecologically valid spontaneous tasks that were designed to mimic natural speech. This speech data is supplemented by performance measures on several standardised, computer-based executive functioning (EF) tests covering domains of working-memory, cognitive flexibility, inhibition, and attention. The speech and EF data are further complemented by a rich collection of demographic data that documents education level, family status, and physical and psychological well-being. Additionally, the dataset includes information of the participants' hormone levels (cortisol, progesterone, oestradiol, and testosterone) at the time of testing. This dataset is thus a carefully curated, expansive collection of data that spans over different EF domains and includes both formal speaking tests as well as spontaneous speaking tasks, supplemented by valuable phenotypical information. This will thus provide the unique opportunity to perform a variety of analyses in the context of speech, EF, and inter-individual differences, and to our knowledge is the first of its kind in the German language. We refer to this dataset as SpEx since it combines speech and executive functioning data. Researchers interested in conducting exploratory or hypothesis-driven analyses in the field of individual differences in language and executive functioning, are encouraged to request access to this resource. Applicants will then be provided with an encrypted version of the data which can be downloaded.

Health economic analysis of the integrated cognitive assessment tool to aid dementia diagnosis in the United Kingdom

Objectives

The aim of this study was to develop a comprehensive economic evaluation of the integrated cognitive assessment (ICA) tool compared with standard cognitive tests when used for dementia screening in primary care and for initial patient triage in memory clinics.

Methods

ICA was compared with standard of care comprising a mixture of cognitive assessment tools over a lifetime horizon and employing the UK health and social care perspective. The model combined a decision tree to capture the initial outcomes of the cognitive testing with a Markov structure that estimated long-term outcomes of people with dementia. Quality of life outcomes were quantified using quality-adjusted life years (QALYs), and the economic benefits were assessed using net monetary benefit (NMB). Both costs and QALYs were discounted at 3.5% per annum and cost-effectiveness was assessed using a threshold of £20,000 per QALY gained.

Results

ICA dominated standard cognitive assessment tools in both the primary care and memory clinic settings. Introduction of the ICA tool was estimated to result in a lifetime cost saving of approximately £123 and £226 per person in primary care and memory clinics, respectively. QALY gains associated with early diagnosis were modest (0.0016 in primary care and 0.0027 in memory clinic). The net monetary benefit (NMB) of ICA introduction was estimated at £154 in primary care and £281 in the memory clinic settings.

Conclusion

Introduction of ICA as a tool to screen primary care patients for dementia and perform initial triage in memory clinics could be cost saving to the UK public health and social care payer.

The use of the integrated cognitive assessment to improve the efficiency of primary care referrals to memory services in the accelerating dementia pathway technologies study

Background

Current primary care cognitive assessment tools are either crude or time-consuming instruments that can only detect cognitive impairment when it is well established. This leads to unnecessary or late referrals to memory services, by which time the disease may have already progressed into more severe stages. Due to the COVID-19 pandemic, some memory services have adapted to the new environment by shifting to remote assessments of patients to meet service user demand. However, the use of remote cognitive assessments has been inconsistent, and there has been little evaluation of the outcome of such a change in clinical practice. Emerging research has highlighted computerized cognitive tests, such as the Integrated Cognitive Assessment (ICA), as the leading candidates for adoption in clinical practice. This is true both during the pandemic and in the post-COVID-19 era as part of healthcare innovation.

Objectives

The Accelerating Dementias Pathways Technologies (ADePT) Study was initiated in order to address this challenge and develop a real-world evidence basis to support the adoption of ICA as an inexpensive screening tool for the detection of cognitive impairment and improving the efficiency of the dementia care pathway.

Methods

Ninety-nine patients aged 55-90 who have been referred to a memory clinic by a general practitioner (GP) were recruited. Participants completed the ICA either at home or in the clinic along with medical history and usability questionnaires. The GP referral and ICA outcome were compared with the specialist diagnosis obtained at the memory clinic.Participants were given the option to carry out a retest visit where they were again given the chance to take the ICA test either remotely or face-to-face.

Results

The primary outcome of the study compared GP referral with specialist diagnosis of mild cognitive impairment (MCI) and dementia. Of those the GP referred to memory clinics, 78% were necessary referrals, with ~22% unnecessary referrals, or patients who should have been referred to other services as they had disorders other than MCI/dementia. In the same population the ICA was able to correctly identify cognitive impairment in ~90% of patients, with approximately 9% of patients being false negatives. From the subset of unnecessary GP referrals, the ICA classified ~72% of those as not having cognitive impairment, suggesting that these unnecessary referrals may not have been made if the ICA was in use. ICA demonstrated a sensitivity of 93% for dementia and 83% for MCI, with a specificity of 80% for both conditions in detecting cognitive impairment. Additionally, the test-retest prediction agreement for the ICA was 87.5%.

Conclusion

The results from this study demonstrate the potential of the ICA as a screening tool, which can be used to support accurate referrals from primary care settings, along with the work conducted in memory clinics and in secondary care. The ICA's sensitivity and specificity in detecting cognitive impairment in MCI surpassed the overall standard of care reported in existing literature.

Artificial Intelligence and Multiple Sclerosis: Up-to-Date Review

Multiple sclerosis (MS) remains a challenging neurological disorder for the clinician in terms of diagnosis and management. The growing integration of AI-based algorithms in healthcare offers a golden opportunity for clinicians and patients with MS. AI models are based on statistical analyses of large quantities of data from patients including "demographics, genetics, clinical and radiological presentation." These approaches are promising in the quest for greater diagnostic accuracy, tailored management plans, and better prognostication of disease. The use of AI in multiple sclerosis represents a paradigm shift in disease management. With ongoing advancements in AI technologies and the increasing availability of large-scale datasets, the potential for further innovation is immense. As AI continues to evolve, its integration into clinical practice will play a vital role in improving diagnostics, optimizing treatment strategies, and enhancing patient outcomes for MS. This review is about conducting a literature review to identify relevant studies on AI applications in MS. Only peer-reviewed studies published in the last four years have been selected. Data related to AI techniques, advancements, and implications are extracted. Through data analysis, key themes and tendencies are identified. The review presents a cohesive synthesis of the current state of AI and MS, highlighting potential implications and new advancements.

Effect of deep gray matter atrophy on information processing speed in early relapsing-remitting multiple sclerosis

BACKGROUND

Cognitive dysfunction, including reduced Information processing speed (IPS), is relatively common in multiple sclerosis(MS). IPS deficits have profound effects on several aspects of patients' life. Previous studies showed that deep gray matter atrophy is highly correlated with overall cognitive impairment in MS. However, the effect of deep gray matter atrophy on IPS deficits is not well understood. In this study, we evaluated the effects of deep gray matter volume changes on IPS in people with early relapse-remitting MS (RRMS) compared to healthy control.

METHODS

In this case-control study, we enrolled 63 case with RRMS and 36 healthy controls. All patients were diagnosed within 6 years. IPS was evaluated using the Integrated Cognitive Assessment (ICA) test. We also performed a 1.5T MRI to evaluate deep gray matter structures.

RESULTS

People with RRMS had lower accuracy in the ICA test (p = .01). However, the reaction time did not significantly differ between RRMS and control groups (p = .6). Thalamus volume was significantly lower in the RRMS group with impaired IPS compared to the RRMS with normal IPS and control groups (p < 10-4). Other deep gray matter structures were not significantly different between the RRMS with impaired IPS group and the RRMS with normal IPS group.

CONCLUSION

Some people with MS are impaired in IPS even in the early stages of the disease. Thalamic atrophy affected IPS in these patients, however atrophy in other deep gray matter structures, including caudate, putamen, globus pallidus, hippocampus, amygdala, accumbens, and cerebellum, were not significantly correlated with IPS impairment in early RRMS.

Impact of frailty, mild cognitive impairment and cognitive frailty on adverse health outcomes among community-dwelling older adults: A systematic review and meta-analysis

Aims

This study analyzes the impact of frailty, mild cognitive impairment, and cognitive frailty on adverse outcomes in community-dwelling older adults.

Methods

This systematic review and meta-analysis were conducted using the PRISMA guidelines and MOOSE statement. We developed a specific search strategy for each electronic database and searched PubMed, Web of Science, MEDLINE, and Embase from initial records to July 2021. The studies on adverse outcomes of frailty, pre-frailty, mild cognitive impairment, and mild cognitive impairment with pre-frailty and cognitive frailty were included. Two researchers independently extracted data based on a spreadsheet and assessed the risk of bias. The primary outcomes were mortality, dementia, disability, and hospitalization. The second outcome included quality of life and falls. All analysis was conducted by using Review Manager (RevMan) 5.3 software.

Results

A total of 22 cohort studies (71,544 older adults with mean age ranging from 65.1 to 93.6 years) were included with a low risk of bias and high methodological quality with a NOS score ≥7. Compared to robust elders, individuals identified as frailty were associated with a higher risk of mortality (RR = 2.11, 95% CI: 1.57–2.83) and disability (RR = 5.91, 95% CI: 2.37–14.74). Mild cognitive impairment with pre-frailty was associated with mortality (RR = 1.74, 95% CI: 1.48–2.05) and dementia (RR = 4.15, 95% CI: 1.87–9.20). Pre-frailty was associated with a higher risk of mortality (RR = 1.29, 95% CI: 1.11–1.50). Cognitive frailty was associated with higher risk of incident mortality (RR = 2.41, 95% CI: 1.97–2.94), dementia (RR = 3.67, 95% CI: 2.81–4.78), disability (RR = 11.32, 95% CI: 4.14–30.97), and hospitalization (RR = 2.30, 95% CI: 1.60–3.32), as well as poor quality of life.

Conclusion

Cognitive frailty could be a comprehensive psychosomatic predictor for adverse outcomes among older people. Interactions between frailty, mild cognitive impairment, and cognitive frailty on adverse outcomes must be further explored.

Systematic review registration

[https://inplasy.com/inplasy-2022-5-0064/], identifier [INPLASY202250064].

Temporal dynamics of animacy categorization in the brain of patients with mild cognitive impairment

Electroencephalography (EEG) has been commonly used to measure brain alterations in Alzheimer’s Disease (AD). However, reported changes are limited to those obtained from using univariate measures, including activation level and frequency bands. To look beyond the activation level, we used multivariate pattern analysis (MVPA) to extract patterns of information from EEG responses to images in an animacy categorization task. Comparing healthy controls (HC) with patients with mild cognitive impairment (MCI), we found that the neural speed of animacy information processing is decreased in MCI patients. Moreover, we found critical time-points during which the representational pattern of animacy for MCI patients was significantly discriminable from that of HC, while the activation level remained unchanged. Together, these results suggest that the speed and pattern of animacy information processing provide clinically useful information as a potential biomarker for detecting early changes in MCI and AD patients.

Assessing Cognitive Function in Multiple Sclerosis With Digital Tools: Observational Study

Background

Cognitive impairment (CI) is one of the most prevalent symptoms of multiple sclerosis (MS). However, it is difficult to include cognitive assessment as part of MS standard care since the comprehensive neuropsychological examinations are usually time-consuming and extensive.

Objective

To improve access to CI assessment, we evaluated the feasibility and potential assessment sensitivity of a tablet-based cognitive battery in patients with MS.

Methods

In total, 53 participants with MS (24 [45%] with CI and 29 [55%] without CI) and 24 non-MS participants were assessed with a tablet-based cognitive battery (Adaptive Cognitive Evaluation [ACE]) and standard cognitive measures, including the Symbol Digit Modalities Test (SDMT) and the Paced Auditory Serial Addition Test (PASAT). Associations between performance in ACE and the SDMT/PASAT were explored, with group comparisons to evaluate whether ACE modules can capture group-level differences.

Results

Correlations between performance in ACE and the SDMT (R=–0.57, P<.001), as well as PASAT (R=–0.39, P=.01), were observed. Compared to non-MS and non-CI MS groups, the CI MS group showed a slower reaction time (CI MS vs non-MS: P<.001; CI MS vs non-CI MS: P=.004) and a higher attention cost (CI MS vs non-MS: P=.02; CI MS vs non-CI MS: P<.001).

Conclusions

These results provide preliminary evidence that ACE, a tablet-based cognitive assessment battery, provides modules that could potentially serve as a digital cognitive assessment for people with MS.

Trial Registration

ClinicalTrials.gov NCT03569618; https://clinicaltrials.gov/ct2/show/NCT03569618

The Use of a Computerized Cognitive Assessment to Improve the Efficiency of Primary Care Referrals to Memory Services: Protocol for the Accelerating Dementia Pathway Technologies (ADePT) Study

Background

Existing primary care cognitive assessment tools are crude or time-consuming screening instruments which can only detect cognitive impairment when it is well established. Due to the COVID-19 pandemic, memory services have adapted to the new environment by moving to remote patient assessments to continue meeting service user demand. However, the remote use of cognitive assessments has been variable while there has been scant evaluation of the outcome of such a change in clinical practice. Emerging research in remote memory clinics has highlighted computerized cognitive tests, such as the Integrated Cognitive Assessment (ICA), as prominent candidates for adoption in clinical practice both during the pandemic and for post-COVID-19 implementation as part of health care innovation.

Objective

The aim of the Accelerating Dementia Pathway Technologies (ADePT) study is to develop a real-world evidence basis to support the adoption of ICA as an inexpensive screening tool for the detection of cognitive impairment to improve the efficiency of the dementia care pathway.

Methods

Patients who have been referred to a memory clinic by a general practitioner (GP) are recruited. Participants complete the ICA either at home or in the clinic along with medical history and usability questionnaires. The GP referral and ICA outcome are compared with the specialist diagnosis obtained at the memory clinic. The clinical outcomes as well as National Health Service reference costing data will be used to assess the potential health and economic benefits of the use of the ICA in the dementia diagnosis pathway.

Results

The ADePT study was funded in January 2020 by Innovate UK (Project Number 105837). As of September 2021, 86 participants have been recruited in the study, with 23 participants also completing a retest visit. Initially, the study was designed for in-person visits at the memory clinic; however, in light of the COVID-19 pandemic, the study was amended to allow remote as well as face-to-face visits. The study was also expanded from a single site to 4 sites in the United Kingdom. We expect results to be published by the second quarter of 2022.

Conclusions

The ADePT study aims to improve the efficiency of the dementia care pathway at its very beginning and supports systems integration at the intersection between primary and secondary care. The introduction of a standardized, self-administered, digital assessment tool for the timely detection of neurodegeneration as part of a decision support system that can signpost accordingly can reduce unnecessary referrals, service backlog, and assessment variability.

Trial Registration

ISRCTN 16596456; https://www.isrctn.com/ISRCTN16596456

International Registered Report Identifier (IRRID)

DERR1-10.2196/34475

Validity and Cultural Generalisability of a 5-Minute AI-Based, Computerised Cognitive Assessment in Mild Cognitive Impairment and Alzheimer's Dementia

Introduction: Early detection and monitoring of mild cognitive impairment (MCI) and Alzheimer's Disease (AD) patients are key to tackling dementia and providing benefits to patients, caregivers, healthcare providers and society. We developed the Integrated Cognitive Assessment (ICA); a 5-min, language independent computerised cognitive test that employs an Artificial Intelligence (AI) model to improve its accuracy in detecting cognitive impairment. In this study, we aimed to evaluate the generalisability of the ICA in detecting cognitive impairment in MCI and mild AD patients. Methods: We studied the ICA in 230 participants. 95 healthy volunteers, 80 MCI, and 55 mild AD participants completed the ICA, Montreal Cognitive Assessment (MoCA) and Addenbrooke's Cognitive Examination (ACE) cognitive tests. Results: The ICA demonstrated convergent validity with MoCA (Pearson r=0.58, p<0.0001) and ACE (r=0.62, p<0.0001). The ICA AI model was able to detect cognitive impairment with an AUC of 81% for MCI patients, and 88% for mild AD patients. The AI model demonstrated improved performance with increased training data and showed generalisability in performance from one population to another. The ICA correlation of 0.17 (p = 0.01) with education years is considerably smaller than that of MoCA (r = 0.34, p < 0.0001) and ACE (r = 0.41, p < 0.0001) which displayed significant correlations. In a separate study the ICA demonstrated no significant practise effect over the duration of the study. Discussion: The ICA can support clinicians by aiding accurate diagnosis of MCI and AD and is appropriate for large-scale screening of cognitive impairment. The ICA is unbiased by differences in language, culture, and education.

Machine Learning Use for Prognostic Purposes in Multiple Sclerosis

The course of multiple sclerosis begins with a relapsing-remitting phase, which evolves into a secondarily progressive form over an extremely variable period, depending on many factors, each with a subtle influence. To date, no prognostic factors or risk score have been validated to predict disease course in single individuals. This is increasingly frustrating, since several treatments can prevent relapses and slow progression, even for a long time, although the possible adverse effects are relevant, in particular for the more effective drugs. An early prediction of disease course would allow differentiation of the treatment based on the expected aggressiveness of the disease, reserving high-impact therapies for patients at greater risk. To increase prognostic capacity, approaches based on machine learning (ML) algorithms are being attempted, given the failure of other approaches. Here we review recent studies that have used clinical data, alone or with other types of data, to derive prognostic models. Several algorithms that have been used and compared are described. Although no study has proposed a clinically usable model, knowledge is building up and in the future strong tools are likely to emerge.

Implementing Remote Memory Clinics to Enhance Clinical Care During and After COVID-19

Social isolation is likely to be recommended for older adults due to COVID-19, with ongoing reduced clinical contact suggested for this population. This has increased the need for remote memory clinics, we therefore review the literature, current practices and guidelines on organizing such remote memory clinics, focusing on assessment of cognition, function and other relevant measurements, proposing a novel pathway based on three levels of complexity: simple telephone or video-based interviews and testing using available tests (Level 1), digitized and validated methods based on standard pen-and-paper tests and scales (Level 2), and finally fully digitized cognitive batteries and remote measurement technologies (RMTs, Level 3). Pros and cons of these strategies are discussed. Remotely collected data negates the need for frail patients or carers to commute to clinic and offers valuable insights into progression over time, as well as treatment responses to therapeutic interventions, providing a more realistic and contextualized environment for data-collection. Notwithstanding several challenges related to internet access, computer skills, limited evidence base and regulatory and data protection issues, digital biomarkers collected remotely have significant potential for diagnosis and symptom management in older adults and we propose a framework and pathway for how technologies can be implemented to support remote memory clinics. These platforms are also well-placed for administration of digital cognitive training and other interventions. The individual, societal and public/private costs of COVID-19 are high and will continue to rise for some time but the challenges the pandemic has placed on memory services also provides an opportunity to embrace novel approaches. Remote memory clinics' financial, logistical, clinical and practical benefits have been highlighted by COVID-19, supporting their use to not only be maintained when social distancing legislation is lifted but to be devoted extra resources and attention to fully potentiate this valuable arm of clinical assessment and care.