Purpose: This study aims to evaluate a new method for the five times sit to stand test (FTSST), crucial for addressing frailty in an aging population. It utilizes a smart insole for plantar pressure analysis and a marker-less motion capture device for head height analysis. Materials and Methods: Thirty-five participants aged 50 years or older underwent FTSST assessment using three methods: manual measurement with a stopwatch (FTSST-M), plantar pressure analysis with smart insoles (FTSST-P), and head height analysis with a marker-less motion capture device (FTSST-H). Simultaneous measurements using three methods were done. Correlation between results of these methods were analyzed using intraclass correlation coefficient (ICC) and κ coefficient. Comprehensive clinical examinations were conducted with ethical approval. Results: Participants’ mean scores for FTSST-M, FTSST-P, and FTSST-H were 2.43±1.20, 2.43±1.29, and 2.37±1.31, respectively. Correlations of the times and corresponding scores between FTSST-P and FTSST-M, as well as FTSST-H and FTSST-M, exceeded 0.9 (ICC and κ coefficients, p<0.001). Using an FTSST score of 3 or less to indicate vulnerability, the κ value for vulnerability classification between two measurements was 0.886 (p<0.001). Conclusion This study showed strong correlation between FTSST results using smart insoles and marker-less motion capture, compared to conventional methods. These findings highlight the potential of these technologies for precise FTSST measurements, offering convenience and cost-effectiveness. Simultaneous use of these devices enables diverse analyses, enhancing our understanding of frailty.

Background: and Purpose: Amyloid-beta (Aβ) plaques are key in Alzheimer’s disease (AD), with Aβ positron emission tomography imaging enabling non-invasive quantification.To address regional Aβ deposition, we developed regional Centiloid scales (rdcCL) and commercialized them through the computed tomography (CT)-based BeauBrain Amylo platform, eliminating the need for three-dimensional T1 magnetic resonance imaging (MRI). Objective: We aimed to establish robust regional Aβ cutoffs using the commercialized BeauBrain Amylo platform and to explore the prevalence of subgroups defined by global, regional, and striatal Aβ cutoffs across cognitive stages. Methods: We included 2,428 individuals recruited from the Korea-Registries to Overcome Dementia and Accelerate Dementia Research project. We calculated regional Aβ cutoffs using Gaussian Mixture Modeling. Participants were classified into subgroups based on global, regional, and striatal Aβ positivity across cognitive stages (cognitively unimpaired [CU], mild cognitive impairment, and dementia of the Alzheimer’s type). Results: MRI-based and CT-based global Aβ cutoffs were highly comparable and consistent with previously reported Centiloid values. Regional cutoffs revealed both similarities and differences between MRI- and CT-based methods, reflecting modality-specific segmentation processes. Subgroups such as global(−)regional(+) were more frequent in non-dementia stages, while global(+)striatal(−) was primarily observed in CU individuals. Conclusions Our study established robust regional Aβ cutoffs using a CT-based rdcCL method and demonstrated its clinical utility in classifying amyloid subgroups across cognitive stages. These findings highlight the importance of regional Aβ quantification in understanding amyloid pathology and its implications for biomarker-guided diagnosis and treatment in AD.

Purpose: This study aims to evaluate a new method for the five times sit to stand test (FTSST), crucial for addressing frailty in an aging population. It utilizes a smart insole for plantar pressure analysis and a marker-less motion capture device for head height analysis. Materials and Methods: Thirty-five participants aged 50 years or older underwent FTSST assessment using three methods: manual measurement with a stopwatch (FTSST-M), plantar pressure analysis with smart insoles (FTSST-P), and head height analysis with a marker-less motion capture device (FTSST-H). Simultaneous measurements using three methods were done. Correlation between results of these methods were analyzed using intraclass correlation coefficient (ICC) and κ coefficient. Comprehensive clinical examinations were conducted with ethical approval. Results: Participants’ mean scores for FTSST-M, FTSST-P, and FTSST-H were 2.43±1.20, 2.43±1.29, and 2.37±1.31, respectively. Correlations of the times and corresponding scores between FTSST-P and FTSST-M, as well as FTSST-H and FTSST-M, exceeded 0.9 (ICC and κ coefficients, p<0.001). Using an FTSST score of 3 or less to indicate vulnerability, the κ value for vulnerability classification between two measurements was 0.886 (p<0.001). Conclusion This study showed strong correlation between FTSST results using smart insoles and marker-less motion capture, compared to conventional methods. These findings highlight the potential of these technologies for precise FTSST measurements, offering convenience and cost-effectiveness. Simultaneous use of these devices enables diverse analyses, enhancing our understanding of frailty.

Background: and Purpose: Amyloid-beta (Aβ) plaques are key in Alzheimer’s disease (AD), with Aβ positron emission tomography imaging enabling non-invasive quantification.To address regional Aβ deposition, we developed regional Centiloid scales (rdcCL) and commercialized them through the computed tomography (CT)-based BeauBrain Amylo platform, eliminating the need for three-dimensional T1 magnetic resonance imaging (MRI). Objective: We aimed to establish robust regional Aβ cutoffs using the commercialized BeauBrain Amylo platform and to explore the prevalence of subgroups defined by global, regional, and striatal Aβ cutoffs across cognitive stages. Methods: We included 2,428 individuals recruited from the Korea-Registries to Overcome Dementia and Accelerate Dementia Research project. We calculated regional Aβ cutoffs using Gaussian Mixture Modeling. Participants were classified into subgroups based on global, regional, and striatal Aβ positivity across cognitive stages (cognitively unimpaired [CU], mild cognitive impairment, and dementia of the Alzheimer’s type). Results: MRI-based and CT-based global Aβ cutoffs were highly comparable and consistent with previously reported Centiloid values. Regional cutoffs revealed both similarities and differences between MRI- and CT-based methods, reflecting modality-specific segmentation processes. Subgroups such as global(−)regional(+) were more frequent in non-dementia stages, while global(+)striatal(−) was primarily observed in CU individuals. Conclusions Our study established robust regional Aβ cutoffs using a CT-based rdcCL method and demonstrated its clinical utility in classifying amyloid subgroups across cognitive stages. These findings highlight the importance of regional Aβ quantification in understanding amyloid pathology and its implications for biomarker-guided diagnosis and treatment in AD.

Purpose: This study aims to evaluate a new method for the five times sit to stand test (FTSST), crucial for addressing frailty in an aging population. It utilizes a smart insole for plantar pressure analysis and a marker-less motion capture device for head height analysis. Materials and Methods: Thirty-five participants aged 50 years or older underwent FTSST assessment using three methods: manual measurement with a stopwatch (FTSST-M), plantar pressure analysis with smart insoles (FTSST-P), and head height analysis with a marker-less motion capture device (FTSST-H). Simultaneous measurements using three methods were done. Correlation between results of these methods were analyzed using intraclass correlation coefficient (ICC) and κ coefficient. Comprehensive clinical examinations were conducted with ethical approval. Results: Participants’ mean scores for FTSST-M, FTSST-P, and FTSST-H were 2.43±1.20, 2.43±1.29, and 2.37±1.31, respectively. Correlations of the times and corresponding scores between FTSST-P and FTSST-M, as well as FTSST-H and FTSST-M, exceeded 0.9 (ICC and κ coefficients, p<0.001). Using an FTSST score of 3 or less to indicate vulnerability, the κ value for vulnerability classification between two measurements was 0.886 (p<0.001). Conclusion This study showed strong correlation between FTSST results using smart insoles and marker-less motion capture, compared to conventional methods. These findings highlight the potential of these technologies for precise FTSST measurements, offering convenience and cost-effectiveness. Simultaneous use of these devices enables diverse analyses, enhancing our understanding of frailty.

Background: and Purpose: Amyloid-beta (Aβ) plaques are key in Alzheimer’s disease (AD), with Aβ positron emission tomography imaging enabling non-invasive quantification.To address regional Aβ deposition, we developed regional Centiloid scales (rdcCL) and commercialized them through the computed tomography (CT)-based BeauBrain Amylo platform, eliminating the need for three-dimensional T1 magnetic resonance imaging (MRI). Objective: We aimed to establish robust regional Aβ cutoffs using the commercialized BeauBrain Amylo platform and to explore the prevalence of subgroups defined by global, regional, and striatal Aβ cutoffs across cognitive stages. Methods: We included 2,428 individuals recruited from the Korea-Registries to Overcome Dementia and Accelerate Dementia Research project. We calculated regional Aβ cutoffs using Gaussian Mixture Modeling. Participants were classified into subgroups based on global, regional, and striatal Aβ positivity across cognitive stages (cognitively unimpaired [CU], mild cognitive impairment, and dementia of the Alzheimer’s type). Results: MRI-based and CT-based global Aβ cutoffs were highly comparable and consistent with previously reported Centiloid values. Regional cutoffs revealed both similarities and differences between MRI- and CT-based methods, reflecting modality-specific segmentation processes. Subgroups such as global(−)regional(+) were more frequent in non-dementia stages, while global(+)striatal(−) was primarily observed in CU individuals. Conclusions Our study established robust regional Aβ cutoffs using a CT-based rdcCL method and demonstrated its clinical utility in classifying amyloid subgroups across cognitive stages. These findings highlight the importance of regional Aβ quantification in understanding amyloid pathology and its implications for biomarker-guided diagnosis and treatment in AD.

Purpose: This study aims to evaluate a new method for the five times sit to stand test (FTSST), crucial for addressing frailty in an aging population. It utilizes a smart insole for plantar pressure analysis and a marker-less motion capture device for head height analysis. Materials and Methods: Thirty-five participants aged 50 years or older underwent FTSST assessment using three methods: manual measurement with a stopwatch (FTSST-M), plantar pressure analysis with smart insoles (FTSST-P), and head height analysis with a marker-less motion capture device (FTSST-H). Simultaneous measurements using three methods were done. Correlation between results of these methods were analyzed using intraclass correlation coefficient (ICC) and κ coefficient. Comprehensive clinical examinations were conducted with ethical approval. Results: Participants’ mean scores for FTSST-M, FTSST-P, and FTSST-H were 2.43±1.20, 2.43±1.29, and 2.37±1.31, respectively. Correlations of the times and corresponding scores between FTSST-P and FTSST-M, as well as FTSST-H and FTSST-M, exceeded 0.9 (ICC and κ coefficients, p<0.001). Using an FTSST score of 3 or less to indicate vulnerability, the κ value for vulnerability classification between two measurements was 0.886 (p<0.001). Conclusion This study showed strong correlation between FTSST results using smart insoles and marker-less motion capture, compared to conventional methods. These findings highlight the potential of these technologies for precise FTSST measurements, offering convenience and cost-effectiveness. Simultaneous use of these devices enables diverse analyses, enhancing our understanding of frailty.

Background: and Purpose: Amyloid-beta (Aβ) plaques are key in Alzheimer’s disease (AD), with Aβ positron emission tomography imaging enabling non-invasive quantification.To address regional Aβ deposition, we developed regional Centiloid scales (rdcCL) and commercialized them through the computed tomography (CT)-based BeauBrain Amylo platform, eliminating the need for three-dimensional T1 magnetic resonance imaging (MRI). Objective: We aimed to establish robust regional Aβ cutoffs using the commercialized BeauBrain Amylo platform and to explore the prevalence of subgroups defined by global, regional, and striatal Aβ cutoffs across cognitive stages. Methods: We included 2,428 individuals recruited from the Korea-Registries to Overcome Dementia and Accelerate Dementia Research project. We calculated regional Aβ cutoffs using Gaussian Mixture Modeling. Participants were classified into subgroups based on global, regional, and striatal Aβ positivity across cognitive stages (cognitively unimpaired [CU], mild cognitive impairment, and dementia of the Alzheimer’s type). Results: MRI-based and CT-based global Aβ cutoffs were highly comparable and consistent with previously reported Centiloid values. Regional cutoffs revealed both similarities and differences between MRI- and CT-based methods, reflecting modality-specific segmentation processes. Subgroups such as global(−)regional(+) were more frequent in non-dementia stages, while global(+)striatal(−) was primarily observed in CU individuals. Conclusions Our study established robust regional Aβ cutoffs using a CT-based rdcCL method and demonstrated its clinical utility in classifying amyloid subgroups across cognitive stages. These findings highlight the importance of regional Aβ quantification in understanding amyloid pathology and its implications for biomarker-guided diagnosis and treatment in AD.

Purpose: This study aims to evaluate a new method for the five times sit to stand test (FTSST), crucial for addressing frailty in an aging population. It utilizes a smart insole for plantar pressure analysis and a marker-less motion capture device for head height analysis. Materials and Methods: Thirty-five participants aged 50 years or older underwent FTSST assessment using three methods: manual measurement with a stopwatch (FTSST-M), plantar pressure analysis with smart insoles (FTSST-P), and head height analysis with a marker-less motion capture device (FTSST-H). Simultaneous measurements using three methods were done. Correlation between results of these methods were analyzed using intraclass correlation coefficient (ICC) and κ coefficient. Comprehensive clinical examinations were conducted with ethical approval. Results: Participants’ mean scores for FTSST-M, FTSST-P, and FTSST-H were 2.43±1.20, 2.43±1.29, and 2.37±1.31, respectively. Correlations of the times and corresponding scores between FTSST-P and FTSST-M, as well as FTSST-H and FTSST-M, exceeded 0.9 (ICC and κ coefficients, p<0.001). Using an FTSST score of 3 or less to indicate vulnerability, the κ value for vulnerability classification between two measurements was 0.886 (p<0.001). Conclusion This study showed strong correlation between FTSST results using smart insoles and marker-less motion capture, compared to conventional methods. These findings highlight the potential of these technologies for precise FTSST measurements, offering convenience and cost-effectiveness. Simultaneous use of these devices enables diverse analyses, enhancing our understanding of frailty.

Objectives: The aim of this study was to classify polysomnography (PSG)-based sleep disorders using actigraphy data using a convolutional neural network (CNN). Methods: Actigraphy data, PSG data, and diagnoses were obtained from 214 patients from a single-center sleep clinic. Patients diagnosed with circadian sleep disorders, narcolepsy, or periodic limb movement disorders were excluded. From the actigraphy data, three types of data were selected from the first 5 days, namely, sleep-wake status, activity count, and light exposure per epoch. The data were processed into a two-dimensional array with four instances, namely, 24-hour full-day data and data for 6, 8, and 10 hours timepoints after sleep onset, and then analyzed. Using a CNN, we attempted to classify the processed data into PSG-based diagnoses. Results: Overfitting of the training data was observed. The CNN showed near-perfect accuracy on the test data, but failed to classify the validation data (area under the curve: 24-hour full-day data: 0.6031, 6 hours after sleep onset: 0.5148, 8 hours: 0.6122, and 10 hours: 0.5769). Conclusions The lack and inaccuracy of data were responsible for the results. A higher sampling rate and additional ancillary data, such as PSG or heart rate variability data, are necessary for accurate classification. Additionally, alternative approaches to machine learning, such as transformers, should be considered in future studies.