The Assessment of the Diagnostic Delay in Japanese People with Parkinson's Disease Using a Web-based Survey of Patients and Physicians.

Objective Although diagnostic criteria of Parkinson's disease (PD) have been established, the details of the process by which patients notice symptoms, visit a physician, and receive a diagnosis of PD is unclear. We therefore explored factors influencing latency in diagnosing PD. Methods We performed an internet-based survey of patients with PD and their families as well as physicians treating patients with PD to identify any diagnostic latency and its determinants. Evaluated factors included motor and non-motor symptoms, the diagnosis history and symptoms, patients' feelings toward PD prior to the diagnosis, and physician-determined reasons for the diagnostic delay. Results Among the 186 eligible patient respondents (including 87 responses from family members of patients), 24% received a PD diagnosis >1 year after the onset of PD-related symptoms, 58.6% had mid- or late-stage PD at the diagnosis, and 29% of patients had initially thought their symptoms were common age-related phenomena. Tremor (42%) was the most frequent symptom that led patients to visit a medical institution, whereas gait disturbance (14%) was the least frequent. More patients diagnosed with early-stage PD than those diagnosed with mid- or late-stage PD consulted a neurologist at their first visit. Among the 331 eligible physicians, patients' misinterpretation of their symptoms as being age-related was deemed one of or the most common cause (s) of a diagnostic delay by 67% and 36%, respectively. Conclusion Patients' insufficient or misinterpreted information about PD may cause delays in accessing healthcare services, leading to diagnostic delay.

MarmoDetector: A novel 3D automated system for the quantitative assessment of marmoset behavior.

BACKGROUND: Callithrix jacchus, generally known as the common marmoset, has recently garnered interest as an experimental primate model for better understanding the basis of human social behavior, architecture and function. Modelling human neurological and psychological diseases in marmosets can enhance the knowledge obtained from rodent research for future pre-clinical studies. Hence, comprehensive and quantitative assessments of marmoset behaviors are crucial. However, systems for monitoring and analyzing marmoset behaviors have yet to be established. NEW METHOD: In this paper, we present a novel multimodal system, MarmoDetector, for the automated 3D analysis of marmoset behavior under freely moving conditions. MarmoDetector allows the quantitative assessment of marmoset behaviors using computerised tracking analysis techniques that are based on a Kinect system equipped with video recordings, infrared images and depth analysis. RESULTS: Using MarmoDetector, we assessed behavioral circadian rhythms continuously over several days in home cages. In addition, MarmoDetector detected acute, transient complex behaviors of alcohol injected marmosets. COMPARISON TO EXISTING METHOD: Compared to 2D recording, MarmoDetector detects activities more precisely and is very sensitive as we could detect behavioral defects specifically induced by alcohol administration. CONCLUSION: MarmoDetector facilitates the rapid and accurate analysis of marmoset behavior and will enhance research on the neural basis of brain disorders.

Computerized Hammer Sounding Interpretation for Concrete Assessment with Online Machine Learning.

Developing efficient Artificial Intelligence (AI)-enabled systems to substitute the human role in non-destructive testing is an emerging topic of considerable interest. In this study, we propose a novel hammering response analysis system using online machine learning, which aims at achieving near-human performance in assessment of concrete structures. Current computerized hammer sounding systems commonly employ lab-scale data to validate the models. In practice, however, the response signal patterns can be far more complicated due to varying geometric shapes and materials of structures. To deal with a large variety of unseen data, we propose a sequential treatment for response characterization. More specifically, the proposed system can adaptively update itself to approach human performance in hammering sounding data interpretation. To this end, a two-stage framework has been introduced, including feature extraction and the model updating scheme. Various state-of-the-art online learning algorithms have been reviewed and evaluated for the task. To conduct experimental validation, we collected 10,940 response instances from multiple inspection sites; each sample was annotated by human experts with healthy/defective condition labels. The results demonstrated that the proposed scheme achieved favorable assessment accuracy with high efficiency and low computation load.