小橋 昌司

Establishing an optimal first-line approach for suspected intracranial atherosclerotic disease (ICAD)-related acute large vessel occlusion (LVO) remains challenging. We developed an ICAD model using an agarose phantom to investigate the specific features of the stent retriever (SR) design for ICAD-LVO for safety use. An ICAD model that reproduced the mechanical features of plaque was developed to quantify the pull-out resistance and intraluminal injury caused by SRs. The impacts of vessel factors (ICAD existence and plaque stiffness) and SR design (non-segmented, segmented, and manually-controllable-diameter) were evaluated using this model. In the 6% agarose (as soft plaque) ICAD model, SR caused significantly higher pull-out resistance and severe intraluminal injury compared with the non-ICAD model or the 10% agarose (as hard plaque) ICAD model with almost all SRs. In the 6% ICAD model, non-segmented, rather than segmented, SRs seemed to reduce pull-out resistance and intraluminal injury. Longer SRs strengthened the benefit of non-segmented design. SRs with manually controllable diameter, when fully relaxed before retrieval, seemed to provide the safest option among all SR designs, while those with usual handling such as slight relaxation before retrieval seemed to be the most harmful due to specific deformation. Using SRs for ICAD-LVO may increase pull-out resistance and intraluminal injury, particularly in ICAD involving soft plaques. SR use safety for ICAD seems to depend largely on SR design and handling procedures of manually controllable diameter SRs.

PURPOSE: Distinguishing idiopathic normal pressure hydrocephalus (iNPH) from progressive supranuclear palsy (PSP) presents a clinical challenge due to overlapping clinical symptoms such as gait disturbances and cognitive decline. This study presents a novel multi-scale deep learning framework that integrates global and local magnetic resonance imaging (MRI) features using a mixture of experts (MoE) mechanism, enhancing diagnostic accuracy and minimizing interobserver variability. METHODS: The proposed framework combines a 3D convolutional neural network (CNN) for capturing global volumetric features with a 2.5D recurrent CNN focusing on disease-specific regions of interest (ROIs), including the lateral ventricles, high convexity sulci, midbrain, and Sylvian fissures. The MoE mechanism dynamically weights global and local features, optimizing the classification process. Model performance was assessed using stratified fivefold cross-validation on T1-weighted MRI from 118 patients (53 iNPH, 65 PSP) to ensure balanced class distributions across training folds. RESULTS: The MoE model using ResNet-34 achieved an accuracy of 0.983 (95% CI 0.875-1.000), a recall of 0.985 (95% CI 0.750-1.000), a precision of 0.986 (95% CI 0.769-1.000), and an area under the curve (AUC) of 1.000 (95% CI 1.000-1.000), outperforming traditional morphological markers and single-branch deep learning models. The MoE mechanism allowed adaptive weighting of global and local features, contributing to both improved robustness and interpretability. Grad-CAM visualizations highlighted disease-specific regions, demonstrating that the model focused on relevant features in both successful and failure modes of the 3D CNN expert for iNPH and PSP. CONCLUSION: The dynamic integration of global and local MRI features through the MoE framework offers a powerful, robust, and interpretable tool for differentiating iNPH from PSP. This approach reduces reliance on subjective visual assessments and has the potential for broader clinical application through dataset expansion and multicenter validation.

BACKGROUND: The optimal scale of urbanization for stroke health care provision and the potential impact of compact city design on stroke outcomes remain unclear. We investigated the impact of zip code area-level compact city design using the walkability index (WI) and its mediators on stroke outcomes. METHODS: This nationwide retrospective study used data from patients with stroke from the J-ASPECT study (2017-2022). WI was calculated as the average of 3 Z-scored city design elements (population density, road connectivity, and variation in walkable facilities) from 113 1156 zip code areas in Japan. The association between WI and in-hospital mortality, functional independence at discharge, and medical costs was assessed using multivariable mixed-effects logistic regression model. RESULTS: Overall, 555 296 patients (median age, 75 [interquartile range, 66-83] years; female, 42.5%) from 818 hospitals were included. Higher WI was significantly associated with decreased in-hospital mortality (odds ratio [OR], 0.94 [95% CI, 0.92-0.96]) and increased functional independence (OR, 1.03 [95% CI, 1.02-1.04]). The highest WI group was associated with decreased mortality, primarily mediated by management in intensive or stroke care units (proportion mediated, 0.46 [95% CI, 0.35-0.63]), and the highest WI group was associated with increased functional independence, mediated by short road distance to the hospital (proportion mediated, 0.30 [95% CI, 0.21-0.44]). CONCLUSIONS: Zip code area-level compact city design was associated with decreased in-hospital mortality and increased functional independence. Compact city design at community level, even without large-scale urbanization, may contribute to improving stroke care provision and outcomes in increasingly urbanized societies.

肩腱板断裂は，日常生活の動作やスポーツ外傷，加齢などによって引き起こされる一般的な肩関節の障害である．肩腱板断裂の診断には磁気共鳴画像装置（MRI）が広く用いられているが，より迅速かつ普及した撮像手法として単純X線撮影がある．本研究ではより簡便な肩腱板断裂診断のために，肩のX線画像に畳み込みニューラルネットワークを用いた検出法を提案した．その結果139名の被験者，断裂の有無による2クラス分類において，最大79.3%の検出精度を示した．