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en: Accuracy of an Artificial Intelligence-Based Model for Estimating Leftover Liquid Food in Hospitals: Validation Study.

en: An accurate evaluation of the nutritional status of malnourished hospitalized patients at a higher risk of complications, such as frailty or disability, is crucial. Visual methods of estimating food intake are popular for evaluating the nutritional status in clinical environments. However, from the perspective of accurate measurement, such methods are unreliable. The accuracy of estimating leftover liquid food in hospitals using an artificial intelligence (AI)-based model was compared to that of visual estimation. The accuracy of the AI-based model (AI estimation) was compared to that of the visual estimation method for thin rice gruel as staple food and fermented milk and peach juice as side dishes. A total of 576 images of liquid food (432 images of thin rice gruel, 72 of fermented milk, and 72 of peach juice) were used. The mean absolute error, root mean squared error, and coefficient of determination (R) were used as metrics for determining the accuracy of the evaluation process. Welch t test and the confusion matrix were used to examine the difference of mean absolute error between AI and visual estimation. The mean absolute errors obtained through the AI estimation approach were 0.63 for fermented milk, 0.25 for peach juice, and 0.85 for the total. These were significantly smaller than those obtained using the visual estimation approach, which were 1.40 (P<.001) for fermented milk, 0.90 (P<.001) for peach juice, and 1.03 (P=.009) for the total. By contrast, the mean absolute error for thin rice gruel obtained using the AI estimation method (0.99) did not differ significantly from that obtained using visual estimation (0.99). The confusion matrix for thin rice gruel showed variation in the distribution of errors, indicating that the errors in the AI estimation were biased toward the case of many leftovers. The mean squared error for all liquid foods tended to be smaller for the AI estimation than for the visual estimation. Additionally, the coefficient of determination (R) for fermented milk and peach juice tended to be larger for the AI estimation than for the visual estimation, and the R value for the total was equal in terms of accuracy between the AI and visual estimations. The AI estimation approach achieved a smaller mean absolute error and root mean squared error and a larger coefficient of determination (R) than the visual estimation approach for the side dishes. Additionally, the AI estimation approach achieved a smaller mean absolute error and root mean squared error compared to the visual estimation method, and the coefficient of determination (R) was similar to that of the visual estimation method for the total. AI estimation measures liquid food intake in hospitals more precisely than visual estimation, but its accuracy in estimating staple food leftovers requires improvement.

en: JMIR Formative Research

ja: JMIR Formative Research


en: A web-based survey of educational opportunities of medical professionals based on changes in conference design during the COVID-19 pandemic.

en: The online version contains supplementary material available at 10.1007/s10639-022-11032-5.

en: Education and Information Technologies

ja: Education and Information Technologies


en: The role of medical informatics in the management of medical information

en: With progress in information and communication technology, medical information has been converted to digital formats and stored and managed using computer systems. The construction, management, and operation of medical information systems and regional medical liaison systems are the main components of the clinical tasks of medical informatics departments. Research using medical information accumulated in these systems is also a task for medical informatics department. Recently, medical real-world data (RWD) accumulated in medical information systems has become a focus not only for primary use but also for secondary uses of medical information. However, there are many problems, such as standardization, collection, cleaning, and analysis of them. The internet of things and artificial intelligence are also being applied in the collection and analysis of RWD and in resolving the above problems. Using these new technologies, progress in medical care and clinical research is about to enter a new era. J. Med. Invest. 67 : 27-29, February, 2020.

en: The Journal of Medical Investigation : JMI


en: Accelerated organ region segmentation by the revised radial basis function network using a graphics processing unit.

en: This study aimed to accelerate the segmentation of organs in medical imaging with the revised radial basis function (RBF) network, using a graphics processing unit (GPU). We segmented the lung and liver regions from 250 chest x-ray computed tomography (CT) images and 160 abdominal CT images, respectively, using the revised RBF network. We compared the time taken to segment images and their accuracy between serial processing by a single-core central processing unit (CPU), parallel processing using four CPU cores, and GPU processing. Segmentation times for lung and liver organ regions shortened to 57.80 and 35.35 seconds for CPU parallel processing and 20.16 and 11.02 seconds for GPU processing, compared to 211.03 and 124.21 seconds for CPU serial processing, respectively. The concordance rate of the segmented region to the normal region in slices excluding the upper and lower ends (173 lung and 111 liver slices) was 98% for lung and 96% for liver. The use of CPU parallel processing and GPU shortened the organ segmentation time in the revised RBF network without compromising segmentation accuracy. In particular, segmentation time was shortened to less than 10%with GPU. This processing method will contribute to workload reduction in imaging analysis. J. Med. Invest. 66 : 86-92, February, 2019.

ja: The Journal of Medical Investigation : JMI


en: The Behavior Modification to the Lifestyle by Healthy Condition Visualization which Utilized NFC Communication Pedometer

ja: NFC通信歩数計を活用した健康データの可視化による生活習慣の行動変容

en: To record individual body weight and physical activity has been shown to be beneficial for maintaining personal health. We, therefore, established a system which connects a personal computer and health appliances to record body weight, daily step, and blood pressure. We applied this system to 226 employees in 3 companies during the period of October, 2011-March, 2013 and evaluated whether this system could improve individual behavioral modification. One hundred sixty seven persons (73.9%) had continuously recorded the measurement of the pedometer for at least one year. We compared the average of the first three months with the average of the next three months. As a result, the number of steps tended to increase to 7,515±4,629 steps from 7,299±3,731steps (p=0.075). As for blood pressure, the systolic phase significantly decreased from 129.2±13.2 mmHg to 127.1±13.5 mmHg (p=0.028), and diastolic phase decreased from 78.6±9.8 mmHg to 76.0±9.9 mmHg (p=0.00016). BMI also significantly decreased from 23.3±2.9 kg/m2 to 23.2±2.8 kg/m2(p=0.029). In conclusion, to record daily step, body weight and blood pressure by using newly established system improved individual behavior towards a healthy condition.

ja: 健康データの登録の自動化と可視化ができるシステムを利用することで，計測者が健康改善に向けての意識変化や行動変容が起こるかどうか，健康管理を継続することにより健康データに変化が起こるかどうかについて検証することを目的としている．日常の健康管理を行う際に体重や運動の記録が自動で登録・グラフ化できるように，PCとNFC通信歩数計・体組成計・血圧計などの健康機器を連携したシステムを構築した．このシステムを利用し，健康データを日々計測することによってデータの可視化を図り，計測者が健康改善に向けての行動変容が起きるかについて検証を行った．利用期間が1年を経過している企業3社226名の利用者について，2011年10月から2013年3月までに得られたデータで分析を行った．歩数計の計測を2013年3月まで継続し続けたのは167名(73.9%)となった．歩数は「1カ月∼3カ月」の平均と「4カ月∼6カ月」の平均を比べると，7,299±3,731歩から7,515±4,629歩(p＝0.075)へと増加傾向があった．血圧は収縮期が129.2±13.2 mmHgから127.1±13.5 mmHg(p＝0.028)へ有意に減少し，拡張期も78.6±9.8 mmHgから76.0±9.9 mmHg(p＝0.00016)へと有意に減少しており，BMIについても，23.3±2.9 kg/m2から23.2±2.8 kg/m2(p＝0.029)へと有意に減少していた．これらの結果より，システムを利用することで開始当初に健康データが改善し，その後も血圧に関しては継続効果が認められ，健康改善に向けての行動変容が生じたと考えられた．

en: Japan Journal of Medical Informatics

ja: 医療情報学


en: 徳島大学病院における個人情報保護対策 プライバシーマーク認証取得のための組織的アプローチ

en: The Journal of Japan Society for Health Care Management

ja: 日本医療マネジメント学会雑誌