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  1. A thematic review on therapeutic toys and games for the elderly with Alzheimer's disease
    You D, Hasley Bin Ramli S, Ibrahim R, Hibatullah Bin Romli M, Li Z, Chu Q, et al.
    Disabil Rehabil Assist Technol, 2024 Feb 01.
    PMID: 38299880 DOI: 10.1080/17483107.2023.2299713
    PURPOSE: Alzheimer's disease (AD) is a common and devastating neurological ailment that affects millions of the elderly worldwide. Therapeutic toys and games have emerged as potential non-pharmacological interventions for AD. However, despite a growing number of documents on the subject, research on the future direction of therapeutic toys and games for AD remains scarce. To address this gap, this study aims to (1) map the future trends of therapeutic toys and games for AD and (2) identify the categories and design characteristics.

    MATERIALS AND METHODS: Using a thematic review framework, a systematic literature search was conducted in two electronic databases (Scopus and WoS) using established criteria. Thematic analysis was done using ATLAS.ti 23 to identify prominent themes, patterns and trends.

    RESULTS: A total of 180 documents were found. Twenty-five articles met the inclusion criteria. A thematic review of these 25 articles identified 13 initial codes, which were been clustered into four themes: detection and evaluation; intervention; toy/game category; and design characteristics. The word "Cognitive" appears most frequently in documents according to word cloud.

    CONCLUSIONS: Therapeutic toys and games are used to detect and as an intervention for AD. Most of the current studies focused on specific cognitive functions. More research is needed about play therapy for neuropsychiatric symptoms. This thematic review also proposed a conceptual framework for designing toys and games tailored to the needs of the elderly with AD, offering valuable insights to future researchers focusing on this domain.

  2. IDRiD: Diabetic Retinopathy - Segmentation and Grading Challenge
    Porwal P, Pachade S, Kokare M, Deshmukh G, Son J, Bae W, et al.
    Med Image Anal, 2020 01;59:101561.
    PMID: 31671320 DOI: 10.1016/j.media.2019.101561
    Diabetic Retinopathy (DR) is the most common cause of avoidable vision loss, predominantly affecting the working-age population across the globe. Screening for DR, coupled with timely consultation and treatment, is a globally trusted policy to avoid vision loss. However, implementation of DR screening programs is challenging due to the scarcity of medical professionals able to screen a growing global diabetic population at risk for DR. Computer-aided disease diagnosis in retinal image analysis could provide a sustainable approach for such large-scale screening effort. The recent scientific advances in computing capacity and machine learning approaches provide an avenue for biomedical scientists to reach this goal. Aiming to advance the state-of-the-art in automatic DR diagnosis, a grand challenge on "Diabetic Retinopathy - Segmentation and Grading" was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI - 2018). In this paper, we report the set-up and results of this challenge that is primarily based on Indian Diabetic Retinopathy Image Dataset (IDRiD). There were three principal sub-challenges: lesion segmentation, disease severity grading, and localization of retinal landmarks and segmentation. These multiple tasks in this challenge allow to test the generalizability of algorithms, and this is what makes it different from existing ones. It received a positive response from the scientific community with 148 submissions from 495 registrations effectively entered in this challenge. This paper outlines the challenge, its organization, the dataset used, evaluation methods and results of top-performing participating solutions. The top-performing approaches utilized a blend of clinical information, data augmentation, and an ensemble of models. These findings have the potential to enable new developments in retinal image analysis and image-based DR screening in particular.
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