新居 学

BACKGROUND/AIM: Patients diagnosed with cancer are expected to choose one or more treatment modalities after receiving corresponding explanations of the options. When making these choices, patients consider the effects of treatment and aspects related to their quality of life. These concerns can cause confusion and conflict owing to the complicated information provided by medical caregivers. The objective of the study was to identify perceptions of cancer treatment in patients with cancer and the decision-making factors affecting their treatment choices. PATIENTS AND METHODS: In this observational (cross-sectional) study, an online questionnaire survey was administered to 194 Japanese cancer patients with treatment experience. Patient information, perceptions of explanations provided by healthcare professionals, treatment views, and reasons for treatment decisions were subjected to a simple tabulation. Content and factor analysis was conducted to determine important treatment selection elements. RESULTS: Regarding treatment perception, 60.3% of respondents (n=117) considered treatment a financial and family burden, 47.4% (n=92) had concerns about physical pain, and 40.2% (n=78) were worried about increased stress. Regarding decision-making quality, 95.9% determined their preferred treatment within one week, 49.0% reported difficulties in making their decisions, and 83.0% chose their treatment themselves. Major decisive factors were prolonging life, opinions of medical staff, and accepting treatment risks (68.0%, 68.6%, and 60.3% of patients, respectively). The main attitudes toward treatment were anxiety, expectations of benefit, and expectations of support and care. CONCLUSION: SDM should enable patients to visualize the changes that their bodies will experience and include discussions on prognosis. Psychological care should be prioritized to alleviate anxiety and improve readiness for decision-making; attention should be paid to the extent and timing of information provision.

目的：看護小規模多機能型居宅介護（看多機）に特徴的なサービス情報の発信と運営状況との関係を明らかにする． 方法：介護サービス情報公開システムに公表された全国の看多機のテキスト情報を厚生労働省から入手し，KH Coderを用いて語の使われ方の特徴を分析した．看多機に特徴的である医療依存度や看取りに関する語を使用していた事業所とそれ以外に分け，利用者数や従業員数，サービスの実施状況を比較した． 結果：医療依存度や看取りに関する語を使用していた事業所は，使用していない事業所よりも要介護5の利用者数，看護職員の常勤人数が有意に多く，処置の実施率も人工肛門の1項目を除く，12項目で有意に高かった． 結論：医療依存度や看取りに関する語を発信していた事業所は，より多くの利用者を確保し多様なサービスを展開していることが示唆された．今後は事業所管理者が看多機サービスの理解を深め，運営に反映できるような支援が求められる．

Background: We investigated factors involved in decision-making support provided by physicians, nurses, pharmacists and medical and psychiatric social workers involved in cancer care. Materials & methods: A questionnaire survey on decision-making support was conducted. The level of clinician support was classified as 'supporting patients' 'decision-making process regarding cancer treatment', 'no support for patients' 'decision-making process regarding cancer treatment' or 'team-based support for patients' 'decision-making process regarding cancer treatment'. Results: Physicians estimated that 83.7% of patients made a cancer treatment decision within 1 week, but 45.4% of patients had difficulty making a decision. Conclusion: Medical personnel should support patients who have difficulty making decisions, establish a screening method to identify those needing support and develop a system providing decision-making support through interprofessional work.

背景・目的:本研究では,先行研究において抽出された看護小規模多機能型居宅介護(以下,看多機)で働く看護師に求められるコンピテンシーの内容と,実際に看多機で働く看護師の優れた看護実践から,コンピテンシーの内容妥当性を検討することを目的とした.方法:看護師や施設管理者11名を対象にフォーカスグループインタビューを2回実施し,コンピテンシー評価指標(大項目・小項目)案の内容妥当性を検討した.結果:その人の希望に沿いながら「生きる」ための支援を重視するという意見から【看取りを支える】は【最期まで「生きる」を支える】に修正された.また【その人を支えるチームをつくる】は,平時から地域資源を活用する必要性の指摘を受けて【その人を地域で支えるチームをつくる】に修正された.家族の介護負担を軽減する支援の重要性から【家族を支える】が新たに追加された.変更のなかった【その人の生活の中で歩み寄りを続ける】【その人や家族の強みを引き出し生活に取り入れる】【個々に合わせ臨機応変にケアを創造する】【命をまもる】【その人の居場所をつくる】と合わせ,大項目は8つとなった.小項目の妥当性についても検討と修正した結果,37項目から48項目となった.考察:フォーカスグループインタビューでは利用者だけでなく家族支援が望まれていた.本調査を経て,看多機で働く看護師のコンピテンシーが精錬され,内容妥当性は確保された.(著者抄録)

Background Japan is a superaging society unparalleled in the world. Elderly people who need medical care do not receive adequate support in the community. As a new service to address this issue, a small-scale multifunctional in-home care nursing service called Kantaki was created in 2012. Kantaki, in collaboration with a primary physician, operates 24 hours a day and provides various nursing services (home visits, home care, day care, and overnight stays) to older people living in the community. The Japanese Nursing Association is working hard to promote this system; however, its low utilization rate is an issue. Objective This study aimed to determine factors influencing the utilization rate of Kantaki facilities. Methods This was a cross-sectional study. A questionnaire on the operation of Kantaki was sent to all administrators of Kantaki facilities operating in Japan from October 1 to December 31, 2020. A multiple regression analysis was used to determine factors associated with a high utilization rate. Results Responses from 154 of the 593 facilities were analyzed. The average utilization rate for all valid responding facilities was 79.4%. The average number of actual users and the break-even point were almost equal, resulting in little surplus profit from facility operations. A multiple regression analysis showed that factors that had a significant impact on the utilization rate included the break-even point, a surplus of users relative to the break-even point (ie, the margin of revenues), the number of months in office of the administrator, the type of corporation (ie, nonprofit), and Kantaki’s profit from operating home-visit nursing offices. The break-even point, a surplus of users relative to the break-even point, and the number of months in office of the administrator were robust. In addition, support for reducing the burden on family helpers, a service sought by the system, significantly and negatively affected the utilization rate. In the analysis that removed the most influential factors, the cooperation of the home-visit nursing office, Kantaki’s profit from operating the home-visit nursing office, and the number of full-time care workers were significantly related. Conclusions To improve the utilization rate, managers need to stabilize their organization and increase profitability. However, a positive relationship was found between the break-even point and utilization rate, suggesting that simply increasing users did not contribute to cost reduction. Moreover, providing services that meet the needs of individual clients may result in lower utilization rates. These results, which are inconsistent with common sense, reflect the divergence between the assumptions underlying the system’s design and actual conditions. To solve these issues, institutional reforms, such as an increase in nursing care fee points, may be necessary.

【目的】本研究では,2型糖尿病が強く疑われる人とそうでない人の生活習慣の比較を行うことを目的とした。【方法】A市において2008年度から2019年度までに国民健康保険の健康診査を受診した30歳以上の男女のうち,解析項目に欠損がある人,既に生活習慣改善に取り組んでいる人を除く10,401名を解析対象とした。糖尿病が強く疑われる人を「HbA1c(NGSP値)が6.5%以上かつ/または血糖値を下げる薬の服薬ありの人」と定義し,χ2検定を用いて糖尿病の疑いの有無の2群間で生活習慣の比較を行った。その後,糖尿病が強く疑われる/それ以外を従属変数,各生活習慣項目を独立変数,性別と年齢で調整した二項ロジスティック回帰分析を行った。解析は65歳未満と以上で行った。【結果】二項ロジスティック回帰分析の結果,65歳未満の対象者においては,日常生活において歩行または同等の身体活動を1日1時間以上実施していない人,他の人と比較して食べる速度が速い人,就寝前の2時間以内に夕食をとることが週に3回以上ある人は,そうでない人と比較して糖尿病が強く疑われる人のオッズ比がそれぞれ1.69倍(95%信頼区間:1.22-2.32),1.31倍(1.01-1.72),1.52倍(1.12-2.07)であった。一方,お酒を時々あるいは毎日飲み,かつ1日当たり1～2合飲む人では,ほとんど飲まない・飲まない人と比較して糖尿病が強く疑われる人のオッズ比が0.65倍(0.46-0.92)であった。65歳以上の対象者では,日常生活において歩行または同等の身体活動を1日1時間以上実施していない人,ほぼ同じ年齢の同性と比較して歩く速度が速くない人,他の人と比較して食べる速度が速い人は,そうでない人と比較して糖尿病が強く疑われる人のオッズ比がそれぞれ1.41倍(1.18-1.70),1.21倍(1.01-1.45),1.29倍(1.09-1.53)であった。【結論】本研究より,糖尿病予防のためには65歳未満,65歳以上の人に共通して身体活動を行うこと,早食いをしないことが重要であり,加えて65歳未満の人では就寝前2時間以内の夕食摂取に注意すべきことが示唆された。(著者抄録)

Multiple accelerometers (chest and wrist) and physiological sensors (electrocardiogram (ECG) and electrodermal activity (EDA)) based activity classification systems have been proposed to classify those activities which are too abstruse to classify with the accelerometer-based system only. “ActiGraph” count value as well as several features of ECG and EDA were extracted from 14 persons with an open-source dataset named “PPG Field Study Dataset”. The activities were considered as different types (type A, type B, type C) based on the movement intensity of the wrist and the chest. The features were fitted to machine learning (ML) algorithms with accelerometer features only and both (count and physiological feature). In the case of type A activities (no chest and wrist movement), classification with accelerometers only and both, the accuracy was 89.3% with weighted K-NN and 100% with linear regression, respectively. In the case of classification between type B (hand soccer, cycling, driving, lunch) activities with accelerometers only and both, the accuracy was 66.1% and 89.3% with linear and quadratic support vector machine (SVM) respectively. In the case of classification between type C activities (stair and walk) with accelerometers only and both, the accuracy was 64.3% and 82.1% with linear discrimination and SVM (linear) classifier respectively. All the results for type A and type C activities classification were generated with leave one out cross-validation, and classification between type B activities was classified with 50-fold cross-validation to avoid overfitting where Bayesian optimizer tuned the hyperparameters of the ML classifiers.

目的:看護小規模多機能型居宅介護(以下,看多機)で働く看護師の行動特性を明らかにする.方法:看多機の施設長から推薦された看護師29名を対象に行動結果面接法を参考に半構造的面接を行い質的記述的に分析した.結果:看多機で働く看護師の成果は,【利用者・家族の生活の質の向上】【利用者・家族に提供するケアの質の向上】の2カテゴリーと8サブカテゴリー抽出された.これら成果につながる行動特性として【その人の生活の中で歩み寄りを続ける】【その人や家族の強みを引き出し生活に取り入れる】【個々に合わせ臨機応変にケアを創造する】【命をまもる】【看取りを支える】【その人の居場所をつくる】【その人を支えるチームをつくる】の7カテゴリーと37サブカテゴリーが抽出された.結論:今回抽出された看護師の行動特性によって,最期まで住み慣れた自宅や地域で暮らし続けていくという看多機の役割が促進されることが期待される.(著者抄録)

Objectives: In order to cope with a rapidly aging national population, the Japanese government promotes the use of a comprehensive community care system known as Kantaki, which was established in 2012. Aimed at older populations, Kantaki offers a variety of services, including home-visit nursing care, day care, and overnight care. This study clarified the factors that affect the estimated revenue of Kantaki services through a secondary analysis of detailed information released by the Ministry of Health, Labour and Welfare (MHLW). Our goal was to provide information that may facilitate the stable management of Kantaki operations across Japan. Methods: We conducted a secondary analysis of official statistics data and detailed information released by the MHLW in April 2020. As such, we calculated the estimated revenue for Kantaki services. We then conducted a logistic regression analysis with estimated revenue set as the dependent variable in order to assess the magnitude of each influencing factor. Results: A total of 594 multifunctional in-home long-term care services were established. Of these, 506 met the requirements for Kantaki set in this study’s analysis. The logistic regression analysis showed that items with large odds ratios included tube feeding (2.59), enhanced working conditions for care workers (I) (2.58), and colostomy/ileostomy care (1.76). Conclusion: To achieve stable management practices for Kantaki, it is important to handle at-home medical needs through the use of skilled care workers, who must be properly trained and ensured stable employment.

<sec> <title>Background:</title> Anterior cruciate ligament (ACL) injury causes knee instability which affects sports activity involving cutting and twisting motions. The ACL reconstruction surgery replaces the damaged ACL with artificial one which is fixed to the bone tunnels opened by the surgeon. The outcome of the ACL reconstruction is strongly related to the placement of the bone tunnels, therefore, the optimization of tunnel drilling technique is an important factor to obtain satisfactory surgical results. </sec> <sec> <title>Aims:</title> The quadrant method is used for the post-operative evaluation of the ACL reconstruction surgery, which evaluates the bone tunnel opening sites on the lateral 2D X-ray radiograph. </sec> <sec> <title>Methods:</title> For the purpose of applying the quadrant method to the pre-operative knee MRI, we have synthesized the pseudo lateral 2D X-ray radiograph from the patients' knee MRI. This paper proposes a computer-aided surgical planning system for the ACL reconstruction. The proposed system estimates appropriate bone tunnel opening sites on the pseudo lateral 2D X-ray radiograph synthesized from the pre-operative knee MRI. </sec> <sec> <title>Results:</title> In the experiment, the proposed method was applied to 98 subjects including subjects with osteoarthritis. The experimental results showed that the proposed method can estimate the bone tunnel opening sites accurately. The other experiment using 36 healthy patients showed that the proposed method is robust to the knee shape deformation caused by disease. </sec> <sec> <title>Conclusion:</title> It is verified that the proposed method can be applied to subjects with osteoarthritis. </sec>

© 2020 IEEE. The procedure of orthopedic surgery is quite complicated, and many kinds of equipment have been used. Operating room nurses who deliver surgical instruments to surgeon are supposed to be forced to incur a heavy burden. There are some studies to recognize surgical phase with convolutional neural network (CNN) in minimally invasive laparoscopic surgery only. Previously, we proposed a computer-aided orthopedic surgery (CAOS)-AI navigation system based on CNN. However, the work propose a method to improve accuracy of phase recognition by considering temporal dependency of orthopedic surgery video acquired from surgeon-wearable video camera. The method estimates current surgical phase by combining both temporal dependency and convolutional-long-short term memory network (CNN-LSTM). Experimental results shows a phase recognition accuracy of 59.9% by the proposed method applied in unicomapartmenatal knee arthroplasty (UKA).

【背景】高齢化率の進展に伴い，地域包括ケアシステムが推進されている。2012年より，複合型サービス（現在名称は，看護小規模多機能型居宅介護，以下看多機）が創設され，訪問看護・訪問介護，通所，宿泊を組み合わせて包括的な生活支援ができるサービスが展開されているが，全国規模での開設・運営状況の情報は明らかにされていない。【目的】全国の看多機に関する情報や人口動態に関する情報を用いて，看多機の運営状況や開設地域の特徴を明らかにすることである。【方法】「介護事業所・生活関連情報検索」のウェブサイトを用いて，2018年12月31日の時点で登録されている全看多機を対象とし施設属性，運営状況，従業員情報等を抽出した。また，同ウェブサイトから都道府県別の看多機数，小規模多機能型居宅介護数（以下，小多機数），訪問看護事業所数を抽出した。「国勢調査」等から都道府県別の高齢化率，人口密度，人口の情報を収集した。分析は度数分布・記述統計分析，スピアマンの相関係数を用いた相関分析とした。【結果】全国の看多機数は479であった。都道府県別の看多機数は平均10.19±10.51であった。市町村別でみると，1724の市町村のうち233（13.5％）に看多機が所在した。看多機数と有意な相関（相関係数ρ）を認めたのは，小多機数（.765），訪問看護事業所数（.747），人口（.703），人口密度（.513）が正の相関，高齢化率（-.442）が負の相関であった。【考察】地域別の看多機数には大きなばらつきがみられ，高齢者の割合の高い地域ではなく，人口の多い地域や人口の密集した地域に多く開設されていた。過疎地では職員確保やサービス供給コストが影響する可能性があると考えられる。今後，どの地域でも安定して看多機が運営され，拡大するためには，現在生じている運営課題を明らかにしていくことが重要であると考えられる。

© 2020, Bentham Science Publishers. All rights reserved. Background: This study presents a novel method of constructing a spatiotemporal statistical shape model (st-SSM) for adult brain. St-SSM is an extension of statistical shape model (SSM) in the temporal domain which will represent the statistical variability of shape as well as the temporal change of statistical variance with respect to time. Aims: Expectation-Maximization (EM) based weighted principal component analysis (WPCA) using a temporal weight function is applied where the eigenvalues of each data are estimated by Estep using temporal eigenvectors, and M-step updates Eigenvectors in order to maximize the variance. Both E and M-step are iterated until updating vectors reaches the convergence point. A weight parameter for each subject is allocated in accordance with the subject’s age to calculate the weighted variance. A Gaussian function is utilized to define the weight function. The center of the function is a time point while the variance is a predefined parameter. Methods: The proposed method constructs adult brain st-SSM by changing the time point between minimum to maximum age range with a small interval. Here, the eigenvectors changes with aging. The feature vector of representing adult brain shape is extracted through a level set algorithm. To validate the method, this study employed 103 adult subjects (age: 22 to 93 y.o. with Mean ± SD = 59.32±16.89) from OASIS database. st-SSM was constructed for time point 40 to 90 with a step of 2. Results: We calculated the temporal deformation change between two-time points and evaluated the corresponding difference to investigate the influence of analysis parameter. An application of the proposed model is also introduced which involves Alzheimer’s disease (AD) identification utilizing support vector machine.

BACKGROUND: This study presents a novel method of constructing a spatiotemporal statistical shape model (st-SSM) for adult brain. St-SSM is an extension of statistical shape model (SSM) in the temporal domain which will represent the statistical variability of shape as well as the temporal change of statistical variance with respect to time. AIMS: Expectation-Maximization (EM) based weighted principal component analysis (WPCA) using a temporal weight function is applied where the eigenvalues of each data are estimated by Estep using temporal eigenvectors, and M-step updates Eigenvectors in order to maximize the variance. Both E and M-step are iterated until updating vectors reaches the convergence point. A weight parameter for each subject is allocated in accordance with the subject's age to calculate the weighted variance. A Gaussian function is utilized to define the weight function. The center of the function is a time point while the variance is a predefined parameter. METHODS: The proposed method constructs adult brain st-SSM by changing the time point between minimum to maximum age range with a small interval. Here, the eigenvectors changes with aging. The feature vector of representing adult brain shape is extracted through a level set algorithm. To validate the method, this study employed 103 adult subjects (age: 22 to 93 y.o. with Mean ± SD = 59.32±16.89) from OASIS database. st-SSM was constructed for time point 40 to 90 with a step of 2. RESULTS: We calculated the temporal deformation change between two-time points and evaluated the corresponding difference to investigate the influence of analysis parameter. An application of the proposed model is also introduced which involves Alzheimer's disease (AD) identification utilizing support vector machine. CONCLUSION: In this study, st-SSM based adult brain shape feature extraction and classification techniques are introduced to classify between normal and AD subject as an application.

© 2019 IEEE. Human activities like stay, walk, squat, jogging, stair up and down have been estimated by our proposed fuzzy classification system. Data obtained by a wearable biometric sensor device have been used for estimating human activities. The sensor device can obtain electrocardiograms (ECG), 3-axis acceleration, body surface temperature, humidity, ambient temperature, and atmospheric pressure. Calculated body-angle, body vibration, and amount of change of pressure from sensor data have been used for making fuzzy rules. FIR filters have been used for pre-processing of data. Components of the attitude were extracted from the acceleration data. Five-fold and three-fold cross-validation methods have been used when the same person dataset and different person datasets were used for validation, respectively. MHEALTH datasets has also been validated on the fuzzy classification system. Three movements(stay, walk and jogging) have been classified with this system. From the experimental results, in every cases, every movement has been classified with more than 93% accuracy by our proposed method.

© 2019 TSI® Press. This work aimed to predict postoperative knee functions of a new patient prior to total knee arthroplasty (TKA) surgery using machine learning, because such prediction is essential for surgical planning and for patients to better understand the TKA outcome. However, the main difficulty is to determine the relationships among individual varieties of preoperative and postoperative knee kinematics. The problem was solved by constructing predictive models from the knee kinematics data of 35 osteoarthritis patients, operated by posterior stabilized implant, based on generalized linear regression (GLR) analysis. Two prediction methods (without and with principal component analysis followed by GLR) along with their sub-classes were proposed, and they were finally evaluated by a leaveone- out cross-validation procedure. The best method can predict the postoperative outcome of a new patient with a Pearson’s correlation coefficient (cc) of 0.84±0.15 (mean±SD) and a root-mean-squared-error (RMSE) of 3.27±1.42 mm for anterior-posterior vs. flexion/extension (A-P pattern), and a cc of 0.89±0.15 and RMSE of 4.25±1.92° for internal-external vs. flexion/extension (i-e pattern). Although these were validated for one type of prosthesis, they could be applicable to other implants, because the definition of knee kinematics, measured by a navigation system, is appropriate for other implants

© 2018 IEEE. Knee implantation is a popular knee surgery to replace damaged knee joint in Total knee arthroplasty (TKA). It is essential to predict postoperative knee kinematic before the surgery for patient-specific TKA surgical planning because outcome of the TKA operation strongly depends on types of prosthesis and surgical methods. Previously, we proposed postoperative kinematics (A-P and i-e patterns) prediction method based on generalized linear regression (GLR). However, this study mainly focuses on comparative performance analysis of the two popular machine learning methods (SVR and NN) in predictive model construction for postoperative kinematics prediction using PCA-based feature extraction, then compared with GLR method. It was found that predictive model&#039;s prediction performance slightly varies from each other&#039;s because the characteristics features of the kinematic patterns differs from each type. Therefore, this study recommends the best ML method (NN for A-P pattern and GLM for i-e pattern) with high prediction performance for predicting TKA outcome.

© 2018 IEEE. In this paper, we propose a new approach to classify ovarian follicles into two classes. A smoothing filter which is designed to consider speckle patterns under the resolution of the ultrasound devices is applied for filtering ovarian follicle images. Then, convolutional neural networks are used for extracting features from the filtered ovarian follicle images. Finally, both features extracted by CNNs from the filtered ovarian follicle images and numerical features defined by our previous works are used for classification. From experimental results, we show the effectiveness of our proposed method.

© 2018 IEEE. In order to estimate the existence of ovum in ovarian follicle from ultrasound images, we reconstruct three-dimensional follicle model by using acceleration sensor. Because the ovum is too small to see in ultrasound image, so we are going to estimate its existence from shape and texture features of the follicle by using some AI methods. First, we obtain ultrasound images and rotation angle of probe simultaneously by attaching acceleration sensor on the probe. Then, we reconstruct the follicle as three-dimensional point cloud information. From the follicle point clouds, we can achieve arbitrary cross-sectional images. Then, we extracted shape feature quantity and compare them with actual cross-sectional image. As a result, we showed that we can extract shape feature quantities in arbitrary cross section. We also clarified that if the rotation speed of the ultrasonic probe is sufficiently slow, it can reproduce texture features as well as shape features.

本邦には約70万人の慢性関節リウマチ患者が存在し、また毎年数万人が発病する。リウマチは早期治療による予後の著しい改善がみられるが、リウマチの進行度に応じた適切な治療を行う必要がある。リウマチ進行度診断では、年に数回関節レントゲン画像を撮影し、関節破壊進行度mTSスコアを算出しているが、手動であるため膨大な作業時間を要し、また、スコアは主観的評価であるため自動化、定量化の需要が高まっている。本稿では、mTSスコアの自動推定を目的とした手X線画像からの手指関節自動検出法を提案する。また、サポートベクター回帰による手関節X線画像からのmTSスコア推定とその評価を行う。特徴量として関節周辺画素のHOG(histograms of oriented gradient)を用いた。90名のリウマチ患者手X線画像に提案法を適用した結果、81.4%の精度で手指関節を自動認識できた。また、mTSスコア推定結果から、サポートベクター回帰によるmTSスコアの推定が可能であることが示唆された。(著者抄録)

© 2017 IEEE. Nurses who engaged in elderly care would like to assess their ability of chewing and swallowing because deterioration of the ability of chewing and swallowing will cause pulmonary aspiration. Currently, nurses can not assess the chewing and swallowing ability quantitatively. In this paper, to quantitatively assess the ability of chewing and swallowing, electromyography (EMG) signals around the lower jaw and the neck are obtained by some electrodes when the subject persons vocalize some Japanese pronunciations. Then, the obtained EMG signals are classified by some machine learning methods. fc-nearest neighbor methods show better classification results for the obtained EMG signals.

© 2017 IEEE. Rheumatoid arthritis (RA) damages joints, and the destructed and/or deformed joint causes the pain and reduces the joint function. The prognosis can be improved by early treatment, but it requires accurate evaluation of the degree of RA progression to apply appropriate treatment. The modified total sharp (mTS) score in hand or foot X-ray image has been used to quantitatively evaluate the RA progression evaluation. However, the mTS score measurement takes huge labor and it is very time consuming method because a physician should evaluate progression grade for all hand joints, and the evaluation is subjective. This paper proposes an automated finger joint detection and mTS score estimation method using support vector machine. The experiment in 45 RA patients shows that the proposed method succeeded in detecting the finger joint and estimating the mTS score. As the number of learning data increases, the proposed method can estimate the mTS score with higher accuracy.

© 2017 IEEE. In this paper, a convolutional neural network (CNN) based classification method is proposed. In computer vision and speech recognition areas, CNNs have obtained strong performance. Recently, CNNs have been applied to sentence classification. We have studied nursing-care text classification [5]-[17] for improving nursing-care quality. In our former works, several types of feature definitions were proposed and examined by some classification models. In this paper, a CNN is used for classification of nursing-care texts and then we analyze the trained CNN for extracting important part for decision of classification. First, each nursing-care text is represented as a concatenated word vectors. Then, every nursing-care text is classified using CNN-based classification methods. Next, we examined the structure of the trained CNN for extracting important parts of the nursing-care texts. From our experimental results, the proposed CNN-based method obtained better performance than our former works. And also the results suggest that the extracted part of each nursing-care text has importance for deciding its quality of nursing.

Lifestyle and genetics are known to be the major factors causing cerebral aneurysms, but some studies suggest that the shape of cerebral arteries might be correlated with the risk of aneurysm occurrence. This study focuses on the shape of cerebral arteries where cerebral aneurysms tend to occur. First, it extracts the shape feature of the cerebral artery ring, which is a predilection site of cerebral aneurysm, from 3-D magnetic resonance angiography images, and calculates four types of shape feature vectors - 3-D shape, bifurcation angle, degree ofmeandering, and direction of the branch points. Then, it estimates the risk of cerebral aneurysms occurring, based on the extracted features using support vector machine. To validate the proposed method, we conducted a leave-one-out cross validation test using 80 subjects (40 subjects with and 40 subjects without cerebral aneurysms). The method using a 3-D artery shape achieved 75% sensitivity and 75% specificity the one using the bifurcation angle showed 47% sensitivity and 41% specificity. The method using the degree of meandering showed 55% sensitivity and 53% specificity, and the one that used the direction of the six branch points showed 30% sensitivity and 27% specificity. These results show that the 3-D artery shape could be a possible indicator for predicting the risk of developing cerebral aneurysms.

In this paper, a convolution neural network (CNN) based text classification method is proposed. CNNs show strong performance for computer vision and speech recognition applications. Recently, in some researches, CNNs have been applied to sentence classification applications. Currently, we have studied nursing-care text classification to improve nursing-care quality in Japan. In our former works, several types of feature definitions have been proposed and examined by some classification models like SVMs. In this paper, a single layer CNN is used for classifying nursing-care texts. Each nursing-care text is represented as a concatenated word vectors. Each word is represented as a fixed length word vector which is obtained by the word2vec [1]-[4]. Then, nursing-care texts are classified using a two-dimensional CNN-based classification method. The proposed CNN has a new kind of filters which extracts dependency relation between words. From our experimental results, the proposed CNN-based method obtained better performance than our former works.

Implanted knee kinematics recognition is required in total knee arthroplasty (TKA), which replaces damaged knee joint with artificial one. The 3-D kinematics of implanted knee in-vivo is used to quantify the knee function for diagnosis of TKA patients and to evaluate the design of TKA prosthesis and surgical techniques. There are some methods for the implanted knee kinematics estimation, however, those methods are classified into still image analysis. The discontinuous knee kinematics estimated by the still image analysis is not considered as the actual knee kinematics. This paper proposes an kinematics recognition method for implanted knee using particle filter. The proposed method estimates the 3-D pose/position parameters, which are varying in time, based on a priori knowledge of time evolution of the parameters represented by random walk models and utilizing similarity between acquired DR image frame and synthesized DR image based on hypothesized value of the parameters. The experimental results showed that the proposed method successfully estimated the 3-D implanted knee kinematics with an accuracy of 1.61 mm and 0.32°.

Total knee arthroplasty (TKA) improves patient's Quality of Life (QoL) whose knee has pain caused by aging and diseases. During the TKA surgery, the physician subjectively selects the size and type of the TKA prosthesis. The implanted knee kinematics in-vivo is essential for the evaluation of its function after the surgery. The 2-D/3-D still image registration based conventional methods do not consider the temporal continuity of the knee kinematics. This study proposes a kinematics analysis method for implanted knee using particle filter. Particle filter algorithm requires high computational cost for the accurate outcome. This paper proposes the new prediction model which evaluates the relative pose/position of the femoral and the tibial implants. The experimental results showed that the smooth estimation results were obtained with low computational time.

There are 700,000 Rheumatoid Arthritis (RA) patients in Japan, and the number of patients is increased by 30,000 annually. The early detection and appropriate treatment according to the progression of RA are effective to improve the patient's prognosis. The modified Total Sharp (mTS) score is widely used for the progression evaluation of Rheumatoid Arthritis. The mTS score assessments on hand or foot X-ray image is required several times a year, and it takes very long time. The automatic mTS score calculation system is required. This paper proposes the finger joint detection method and the mTS score estimation method using support vector machine. Experimental results on 45 RA patient's X-ray images showed that the proposed method detects finger joints with accuracy of 81.4 %, and estimated the erosion and JSN score with accuracy of 50.9, 64.3 %, respectively.

先行研究でGyftopoulosらは腱板断裂患者の2次元MR画像から腱板3次元形状の再構築を行う方法を報告しているが、論文中に例示された腱板3次元形状は滑らかではないため、腱板の形状を忠実に再現できたとは言い難い。一方、Turkらは複数枚の断面図から物体3次元形状を再構築する方法を報告している。同手法の概要は、3次元形状を陰関数で表現し、断面間の陰関数値を放射基底関数で補間することで滑らかな3次元形状を生成するというものであり、本法を肝臓などの医用画像に応用した報告はあるが、腱板に応用した報告はみられない。そこで今回、腱板断裂例に応用し、本法によって再構築された3次元形状と実際の術中所見を比較した。結果、再構築された3次元形状は滑らか且つ自然であり、術中所見と一致した。

In this paper, we propose a convolutional neural network (CNN) based classification method for nursing-care classification. CNNs have obtained strong performance in computer vision speech recognition areas. Recently, CNNs have been also applied sentence classification. We have studied nursing-care text classification [6]-[18]. In our former works, we proposed several types of feature definitions and examined some classification models. In this paper, each text is represented as a concatenated word vector. Then, every text is classified using CNN-based classification methods. We examined some classification models at the classification layer in CNNs. From our experimental results, the proposed CNN-based method obtained better performance than our former works.

In this paper, we present a method of analyzing relationships between items in specific health examination data, as one of the basic researches to address increases of lifestyle-related diseases. We use self-organizing maps, and pick up the data from the examination dataset according to the condition specified by some item values. We then focus on twelve items such as hemoglobin A1c (HbA1c), aspartate transaminase (AST), alanine transaminase (ALT), gamma-glutamyl transpeptidase (gamma-GTP), and triglyceride (TG). We generate training data presented to a map by calculating the difference between item values associated with successive two years and normalizing the values of this calculation. We label neurons in the map on condition that one of the item values of training data is employed as a parameter. We finally examine the relationships between items by comparing results of labeling (clusters formed in the map) to each other. From experimental results, we separately reveal the relationships among HbA1c, AST, ALT, gamma-GTP and TG in the unfavorable case of HbA1c value increasing and those in the favorable case of HbA1c value decreasing.