ElliQ, an AI-Driven Social Robot to Alleviate Loneliness: Progress and Lessons Learned.

Background: Loneliness is a significant issue in older adults and can increase the risk of morbidity and mortality. Objective: To present the development of ElliQ, a proactive, AI-driven social robot with multiple social and health coaching functions specifically designed to address loneliness and support older people. Development/Implementation: ElliQ, a consumer robot with a friendly appearance, uses voice, sounds, light, and buttons through a touch screen to facilitate conversation, music, video calls, well-being assessments, stress reduction, cognitive games, and health reminders. The robot was deployed by 15 government agencies in the USA. Initial experience suggests it is not only highly engaging for older people but may be able to improve their quality of life and reduce loneliness. In addition, the development of a weekly report that patients can share with their clinicians to allow better integration into routine care is described. Conclusion: This paper describes the development and real-world implementation of this product innovation and discusses challenges encountered and future directions.

Rationale for Early Diagnosis of Mild Cognitive Impairment (MCI) Supported by Emerging Digital Technologies.

Disease-modifying pharmacotherapies for Alzheimer's Disease (AD) are currently in late-stage clinical development; once approved, new healthcare infrastructures and services, including primary healthcare, will be necessary to accommodate a huge demand for early and large-scale detection of AD. The increasing global accessibility of digital consumer electronics has opened up new prospects for early diagnosis and management of mild cognitive impairment (MCI) with particular regard to AD. This new wave of innovation has spurred research in both academia and industry, aimed at developing and validating a new "digital generation" of tools for the assessment of the cognitive performance. In light of this paradigm shift, an international working group (the Global Advisory Group on Future MCI Care Pathways) convened to elaborate on how digital tools may be optimally integrated in screening-diagnostic pathways of AD The working group developed consensus perspectives on new algorithms for large-scale screening, detection, and diagnosis of individuals with MCI within primary medical care delivery. In addition, the expert panel addressed operational aspects concerning the implementation of unsupervised at-home testing of cognitive performance. The ultimate intent of the working group's consensus perspectives is to provide guidance to developers of cognitive tests and tools to facilitate the transition toward globally accessible cognitive screening aimed at the early detection, diagnosis, and management of MCI due to AD.

Early Detection of Mild Cognitive Impairment (MCI) in Primary Care.

Mild cognitive impairment (MCI) is significantly misdiagnosed in the primary care setting due to multi-dimensional frictions and barriers associated with evaluating individuals' cognitive performance. To move toward large-scale cognitive screening, a global panel of clinicians and cognitive neuroscientists convened to elaborate on current challenges that hamper widespread cognitive performance assessment. This report summarizes a conceptual framework and provides guidance to clinical researchers and test developers and suppliers to inform ongoing refinement of cognitive evaluation. This perspective builds upon a previous article in this series, which outlined the rationale for and potentially against efforts to promote widespread detection of MCI. This working group acknowledges that cognitive screening by default is not recommended and proposes large-scale evaluation of individuals with a concern or interest in their cognitive performance. Such a strategy can increase the likelihood to timely and effective identification and management of MCI. The rising global incidence of AD demands innovation that will help alleviate the burden to healthcare systems when coupled with the potentially near-term approval of disease-modifying therapies. Additionally, we argue that adequate infrastructure, equipment, and resources urgently should be integrated in the primary care setting to optimize the patient journey and accommodate widespread cognitive evaluation.

SU-E-E-01: Commissiong of Linear Accelerator and Beam Modeling in Treatment Planning Systems.

PURPOSE: Sooner or later every medical physicist is involved with commissioning and beam modeling of a new linear accelerator (linac) and a new treatment planning system (TPS). In spite of all instructions and training offered by the vendors, at the time a new linac is being purchased and added to the present ones the outside help is not so complete. The physicist who has to perform the commissioning job may not even be the one who was trained for that. What we are missing is a good comprehensive set of information and instructions on how to do's. From shielding calculation verifications, surveys, to collecting the beam data, modeling, entering the data into the TPS, and verifications of the goodness of the data we need a lot of support and we don't have it. I will provide a step by step description of the required work with the results we are looking for. METHODS: Presentation of the shielding calculations, survey required, tools needed to perform them. Detailed beam data collections, scanning system needed, machine set of specs needed, applicator details needed. Importing beam data from the scanning system and beam calculations. Algorithms used in dose calculation, IMRT optimization, heterogeneity corrections presented to be understood before modeling the beam data. RESULTS: At the completion of this course the medical physicist will be able to commission a linear accelerator and a treatment planning system with confidence and very little help from the outside. CONCLUSIONS: This compendium of detailed instructions on commissioning a linear accelerator will provide good uidance to every physicist who will be involved with the installation and bringing into safe use for treatment of a new linear accelerator.

The effect of random measurement errors on kinetic transport parameter estimation.

Saturation kinetics experiments, in which uptake U of a substance across a transport barrier is measured as a function of initial concentration difference C, are used to describe transport of nutrients. Many such processes are characterized by low- and high-affinity systems in which kinetic parameters Vmax and Km differ by orders of magnitude. Transformations of equations to straight-line relationships between U and C are popular methods of parameter estimation. The aims of this study are (1) to show effects of random errors in U measurement on Vmax and Km estimation in a two-affinity process under several transformations: Lineweaver-Burk (1/U vs. 1/C), Hanes (C/U vs. C), Eadie-Hofstee (U/C vs. U), and Wolff (U vs. U/C), and (2) to indicate strategies for minimizing effects of errors. Two transport properties will illustrate: an ideal process of low- (Vmax = 100, Km = 10) and high-affinity (Vmax = 1, Km = .1) systems to which random error is added, and experimental uptake of 5-methyltetrahydrofolic acid by isolated hepatocytes.