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.

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

Background: 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. Method: We developed the Integrated Cognitive Assessment (ICA); a 5 minute computerised cognitive test employs Artificial Intelligence (AI) to improve its accuracy in detecting cognitive impairment. ICA presents a series of rapidly changing images on a mobile device to measure cognitive impairment via a person's accuracy and response time in categorising those images. We studied the ICA in a total of 230 participants. 95 healthy volunteers, 80 MCI, and 55 mild AD participants completed the ICA, the Montreal Cognitive Assessment (MoCA) and Addenbrooke's Cognitive Examination (ACE) cognitive tests. Result:The ICA demonstrated convergent validity with MoCA (r=0.58) and ACE (r=0.62). The ICA AI model was able to detect cognitive impairment with an area under the curve of 81% for MCI patients (MoCA 77%), and 88% for mild AD patients (MoCA 89%). The AI classifier, based on an explainable logistic regression model, demonstrated improved performance with increased training data. Furthermore it showed generalisability in performance from one population to another. The ICA was able to detect cognitive impairment with high accuracy when trained with one cohort and tested in an independent cohort with different cultural and demographic characteristics, a prerequisite for large population deployment.In a monitoring study, 12 healthy participants self-administered 78 ICA tests remotely over a period of 3 months (936 tests in total). The ICA demonstrated no significant practice effect observed over the duration of the study. Conclusion: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 and has additional advantages over standard of care tests because of its shorter duration, automatic scoring and potential for medical record or research database integration.The pandemic has presented a challenge for face-face assessments. A digital tool such as the ICA allows us to adapt to these changes by administering assessments remotely and monitoring disease progression.

Background: Existing primary care cognitive assessment tools are crude or timeconsuming screening instruments which can only detect cognitive impairment when it is well established, impacting carers and patient quality-of-life.We initiated the Accelerating Dementia Pathway Technologies (ADePT) study to develop a real-world evidence basis to support the adoption of the Integrated Cognitive Assessment (ICA), a 5 minute computerised cognitive test that employs artificial intelligence to improve its accuracy, as an inexpensive screening tool for the detection of cognitive impairment and improving the efficiency of the dementia care pathway. Method:Patients referred to memory clinics from primary care General Practitioners (GPs) 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.The clinical outcomes as well as costing data were used as part of an economic analysis to assess the potential health economic benefits of the use of the ICA in the dementia diagnosis pathway in the United Kingdom.Result: 87 participants referred to memory clinics were recruited who completed all assessments (40 dementia, 19 mild cognitive impairment, 12 inconclusive, 5 healthy, 3 non-dementia conditions). From these patients the ICA was able to identify cognitive impairment with a sensitivity of ∼90%.The results of the health economics model, utilising real world data collected from the ADePT study estimates that if the ICA were introduced in a primary care setting then it could result in a cost saving to the health and social care system of approximately £147 per patient over a lifetime horizon (∼£44m of direct costs to the health care system) or £283 per patient if introduced into a secondary care setting. Conclusion:The results from this study demonstrate the potential of the ICA as a screening tool to support accurate referrals from primary care settings to memory clinics. The introduction of disease modifying treatments for Alzheimer's and dementia will further improve the case for earlier detection of the condition and therefore increase the cost-effectiveness of more accurate screening using tests such as the ICA tool.

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-minute, 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 a total of 230 participants. 95 healthy volunteers, 80 MCI, and 55 participants with mild AD completed the ICA, the 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 practice effect observed 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.