Publications by authors named "Kazuya Takeda"

Object detection plays a significant role in various industrial and scientific domains, particularly in autonomous driving. It enables vehicles to detect surrounding objects, construct spatial maps, and facilitate safe navigation. To accomplish these tasks, a variety of sensors have been employed, including LiDAR, radar, RGB cameras, and ultrasonic sensors.

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Immunodeficiency with hypogammaglobulinemia is associated with interstitial lung disease, but its mechanism and treatment remain unclear and may differ from ordinary interstitial pneumonia. Herein, we report five such cases. Lung biopsy showed a benign lymphoid infiltration pattern and granulomatous lymphocytic interstitial lung disease.

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Background: Stereotactic radiotherapy (SRT) for kidney cancer, particularly when tumours are situated near critical organs-at-risk (OARs), presents significant challenges in achieving optimal dose delivery. MR-guided online adaptive radiotherapy (MRgoART) offers a promising solution by allowing real-time anatomical modification and plan reoptimisation. However, the ideal strategy for prescription dose selection and reoptimisation remains unclear.

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Background: Patients with Parkinson's disease (PwPDs) experience a progressive decline in their sit-to-stand (STS) ability, including a prolonged STS time, rising failure, and seat-off failure. The clinical and biomechanical factors contributing to this decline are unclear.

Objectives: We investigated clinical and biomechanical factors associated with the different stages of STS decline in PwPDs.

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Chronic rhinosinusitis (CRS) is an inflammatory disease of the upper respiratory tract. Although previously classified based on the presence or absence of nasal polyps, it is now commonly classified by endotype. Eosinophilic CRS (ECRS) is based on type 2 inflammation and the formation of intractable nasal polyps with eosinophil infiltration.

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Objectives: This research used text mining to determine the impact of curricular experiences in each year of study on the formation of professional identity among students aspiring to become physical therapists (PTs) and occupational therapists (OTs).

Methods: This study included 210 students (126 PT and 84 OT) enrolled at a single rehabilitation university in Japan in 2020 and 2021. These participants completed an open-ended questionnaire on personal growth 6, 12, 18 and 24 months after enrollment.

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Objectives: With the recent progression of a super-aging society in Japan, demand for medical and welfare professionals has increased, and occupational therapists are in great demand. Academic performance is important for occupational therapy and rehabilitation students. The current study aimed to determine the influence of self-esteem and psychological factors on academic performance in Japanese occupational therapy students.

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Driving Under the Influence (DUI) has emerged as a significant threat to public safety in recent years. Despite substantial efforts to effectively detect DUI, the inherent risks associated with acquiring DUI-related data pose challenges in meeting the data requirements for training. To address this issue, we propose DUIncoder, which is an unsupervised framework designed to learn exclusively from normal driving data across diverse scenarios to detect DUI behaviors and provide explanatory insights.

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In autonomous driving, retrieving a specific traffic scene in huge datasets is a significant challenge. Traditional scene retrieval methods struggle to cope with the semantic complexity and heterogeneity of traffic scenes and are unable to meet the variable needs of different users. This paper proposes "Query-by-Example", a traffic scene retrieval approach based on Visual-Large Language Model (VLM)-generated Road Scene Graph (RSG) representation.

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Purpose: This study aimed to investigate changes in target coverage using magnetic resonance-guided online adaptive radiotherapy (MRgoART) for kidney tumors and to evaluate the suitable timing of treatment.

Materials And Methods: Among patients treated with 3-fraction MRgoART for kidney cancer, 18 tumors located within 1 cm of the gastrointestinal tract were selected. Stereotactic radiosurgery planning with a prescription dose of 26 Gy was performed using pretreatment simulation and three MRgoART timings with an adapt-to-shape method.

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Purpose: In DRLs 2020, in addition to the fluoroscopic dose rate, air kerma at the patient entrance reference point (K) and air kerma-area product (P) by types of medical treatment and diseases were set as DRL quantity. We surveyed the current equipment setting dose at each facility and the exposure dose in clinical practice. We considered the optimal DRL classification of K and P for the next DRLs update.

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The National Database of Health Insurance Claims and Specific Health Checkups of Japan (NDB) is a database that stores anonymized information on medical receipts and health checkups in Japan. The NDB Open Data is a publicly accessible summary table of the NDB database. To reveal annual trends and regional disparities in radiotherapy utilization in Japan, we analyzed the NDB Open Data tables for a 9-year period from 2014 to 2022.

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Rationale: In people with Parkinson's disease (PD), quantitative assessment of activities inside and outside the home is crucial for planning effective rehabilitation tailored to a person's living conditions and characteristics.

Aims And Objectives: We examined the feasibility of combining a physical activity metre and a daily activity diary for people with PD.

Methods: Physical activity was evaluated using a triaxial accelerometer and recorded in a daily activity diary by the participant.

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The accurate prediction of vehicle behavior is crucial for autonomous driving systems, impacting their safety and efficiency in complex urban environments. To address the challenge of multi-agent trajectory prediction, we propose a novel model integrating multiple input modalities, including historical trajectories, map data, vehicle features, and interaction information. Our approach employs a Conditional Variational Autoencoder (CVAE) framework with a decoder that predicts control actions using the Gaussian Mixture Model (GMM) and then converts these actions into dynamically feasible trajectories through a bicycle model.

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Integrating multiple types of sensors into autonomous systems, such as cars and robots, has become a widely adopted approach in modern technology. Among these sensors, RGB cameras, thermal cameras, and LiDAR are particularly valued for their ability to provide comprehensive environmental data. However, despite their advantages, current research primarily focuses on the one or combination of two sensors at a time.

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Article Synopsis
  • This study explored how the spinal cord and canal moved during real-time adaptive radiotherapy for kidney cancer, focusing on MR-guided treatments from February 2022 to February 2024.
  • The research involved 22 patients receiving precise doses of radiation, with MRI scans taken at three different points during treatment to assess movement.
  • Findings showed minimal movement of the spinal cord, but emphasized that specific margins are needed in treatment planning to ensure patient safety amidst these movements.
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Unlabelled: Watanabe T, Hirowatari H, Tokura Y, Takeda K, Hanaoka H. A 30-day kendama program for community-dwelling elderly: Effects on participants' physical and cognitive functions and the practicality of the program. Jpn J Compr Rehabil Sci 2024; 15: 34-41.

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Background And Objectives: This study aimed to examine the development of clinical competence of novice physical therapists (PTs) during their first year of employment, following the implementation of an original in-house educational program. The educational program was designed to offer diverse training opportunities at an early stage, during the first year of employment.

Methods: Thirty-eight novice PTs (21 males and 17 females, mean age 23.

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Article Synopsis
  • The paper introduces MulCPred, a new framework designed to provide explainable predictions for pedestrian action, which is essential for applications like autonomous driving.
  • It addresses limitations of existing methods by using a linear aggregator for multi-modal concept integration, a channel-wise recalibration module for focusing on detailed input areas, and a regularization loss to capture diverse patterns.
  • Evaluation on various datasets shows that MulCPred enhances the explainability of predictions without significantly harming accuracy, and by filtering out unrecognizable concepts, it improves performance across different datasets.
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Genetically diverse populations can increase plant resistance to natural enemies. Yet, beneficial genotype pairs remain elusive due to the occurrence of positive or negative effects of mixed planting on plant resistance, respectively called associational resistance or susceptibility. Here, we identify key genotype pairs responsible for associational resistance to herbivory using the genome-wide polymorphism data of the plant species Arabidopsis thaliana.

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Purpose: In DRLs 2020, the entrance surface air kerma (Ka,e) was set to 17 mGy/min as the reference dose rate in fluoroscopy. But, Ka,e in fluoroscopy for different regions and Ka,e in exposure was not set. A multicenter survey was conducted to evaluate Ka,e by each area.

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Granulomatous lesions in the nasal sinuses are associated with a variety of diseases, including immune disorders such as sarcoidosis, vasculitis, immunoglobulin G4 (IgG4)-related diseases, malignant lymphomas, and microbial infections. Here, we report a rare case of fungal granuloma that occurred exclusively within the nasal septum. The patient presented to the Department of Surgery with the chief complaint of nasal obstruction associated with nasal septal deviation.

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We conducted a study to examine the treatment outcomes and prognostic factors for patients who underwent craniospinal irradiation (CSI) for leptomeningeal metastasis of solid tumors. This retrospective study included patients who received CSI for leptomeningeal metastasis at a single institute between 2010 and 2021. Data from clinical records and the radiation information system were obtained and analyzed.

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Cognitive scientists believe that adaptable intelligent agents like humans perform spatial reasoning tasks by learned causal mental simulation. The problem of learning these simulations is called predictive world modeling. We present the first framework for a learning open-vocabulary predictive world model (OV-PWM) from sensor observations.

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