研究者詳細 - 大山　慎太郎

論文 - 大山　慎太郎

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Insights and trends review: Use of extended reality (xR) in hand surgery 査読有り国際誌

Oyama S, Iwase H, Yoneda H, Yokota H, Hirata H, Yamamoto M.

Journal of Hand Surgery European Volume 2025年1月

担当区分：筆頭著者,　最終著者,　責任著者記述言語：英語掲載種別：研究論文（学術雑誌）出版者・発行元：Sage Journals

Digital transformation through extended reality (xR)—comprising virtual, augmented, mixed, and substitutional reality—has become an integral part of the future of clinical and surgical practice. xR technologies facilitate advanced surgical planning, training, therapies and education, reshaping both personal and institutional healthcare. This paper examines the potential changes that xR has introduced into the field of hand surgery, exploring how xR enhances patient-centric care, increases medical service efficiency and revolutionizes surgical training, planning and therapeutic interventions. Key areas such as surgical assistance, telemedicine, therapies and rehabilitation, medical training and education, and improving patient understanding are highlighted. By synthesizing the current literature, this narrative review articulates the theoretical and practical implications of xR, offering insights into its transformative potential and supporting continuous educational advancement in medical practice.

DOI： https://doi.org/10.1177/1753193424131320

DOI： 10.1177/1753193424131320

Insights and trends review: Use of extended reality (xR) in hand surgery.

Oyama S, Iwase H, Yoneda H, Yokota H, Hirata H, Yamamoto M

The Journal of hand surgery, European volume 頁： 17531934241313208 2025年1月

担当区分：筆頭著者,　責任著者記述言語：英語掲載種別：研究論文（学術雑誌）出版者・発行元：Journal of Hand Surgery: European Volume

DOI： 10.1177/17531934241313208

Enhancing Medical Device Management with IoT Powerstrip. 査読有り国際誌

Oyama S, Otsuka T, Ideno Y, Shima K, Miyagi H

Studies in health technology and informatics 316 巻頁： 485 - 486 2024年8月

担当区分：筆頭著者記述言語：英語掲載種別：論文集(書籍)内論文出版者・発行元：Studies in Health Technology and Informatics

Managing medical devices efficiently was a challenge, especially with manual methods proving error-prone amid staff shortages. To overcome these issues, we developed a novel IoT powerstrip device for tracking usage and location. This helps monitor devices in real-time, empowering more efficient management and utilization. We introduced it in four hospitals, connecting 192 medical devices to the strips and quantitatively confirmed its effectiveness.

添付ファイル： SHTI-316-SHTI240454.pdf

DOI： 10.3233/SHTI240454

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A machine learning approach for predicting treatment response of hyponatremia.

Kinoshita T, Oyama S, Hagiwara D, Azuma Y, Arima H

Endocrine journal 71 巻 ( 4 ) 頁： 345 - 355 2024年4月

担当区分：責任著者記述言語：英語掲載種別：研究論文（学術雑誌）出版者・発行元：Endocrine Journal

Hyponatremia leads to severe central nervous system disorders and requires immediate treatment in some cases. However, a rapid increase in serum sodium (s-Na) concentration could cause osmotic demyelination syndrome. To achieve a safety hyponatremia treatment, we develop a prediction model of s-Na concentration using a machine learning. Among the 341 and 47 patients admitted to two tertiary hospitals for hyponatremia treatment (s-Na <130 mEq/L), those who were admitted to the general unit with urine sodium <20 mEq/L or treated with desmopressin were excluded. Ultimately, 74 and 15 patients (342 and 146 6-hourly datasets) were included in the learning and validation data, respectively. We trained the prediction model using three regression algorithms for shallow machine learning to predict s-Na every 6 h during treatment with the data of patients with hyponatremia (median s-Na: 112.5 mEq/L; range: 110.0–116.8 mEq/L) from one hospital. The model was validated externally using the data of patients with hyponatremia (median s-Na: 117.0 mEq/L; range: 112.9– 120.0 mEq/L) from another hospital. Using 5–7 predictors (water intake, sodium intake, potassium intake, urine volume, s-Na concentration, serum potassium concentration, serum chloride concentration), the support vector regression model showed the best performance overall (root mean square error = 0.05396; R2 = 0.92), followed by the linear regression and regression tree models. The predicted s-Na levels, using explainable machine learning algorithms and clinically accessible parameters, correlated well with the actual levels. Thus, our model could be applied to the treatment of hyponatremia in clinical practice.

DOI： 10.1507/endocrj.EJ23-0561

Intra-Articular Ultrasonography Probe for Minimally Invasive Upper Extremity Arthroscopic Surgery: A Phantom Study. 査読有り

Oyama S, Niimi N, Mori M, Hirata H

Journal of clinical medicine 12 巻 ( 17 ) 頁： 5727 2023年9月

担当区分：筆頭著者,　責任著者記述言語：英語掲載種別：研究論文（学術雑誌）出版者・発行元：Journal of Clinical Medicine

Background: Upper extremity arthroscopic surgery is a highly technique-dependent procedure that requires the surgeon to assess difficult cartilage conditions and manage the risk of iatrogenic damage to nerves and vessels adjacent to the joint capsule in a confined joint space, and a device that can safely assist in this procedure has been in demand. Methods: In this study, we developed a small intra-articular ultrasound (AUS) probe for upper extremity joint surgery, evaluated its safety using underwater sound field measurement, and tested its visualization with a phantom in which nerves and blood vessels were embedded. Results: Sound field measurement experiments confirmed the biological safety of the AUS probe’s output, while confirming that sufficient output power level performance was obtained as an ultrasound measurement probe. In addition, images of blood vessels and nerves were reconstructed discriminatively using A-mode imaging of the agar phantom. Conclusions: This study provides proof-of-concept of the AUS probe in upper extremity surgery. Further studies are needed to obtain approval for use in future medical devices.

添付ファイル： jcm-12-05727.pdf

DOI： 10.3390/jcm12175727

In-Hospital Cancer Mortality Prediction by Multimodal Learning of Non-English Clinical Texts. 査読有り国際誌

Oyama S, Furukawa T, Misawa S, Kano R, Yarimizu H, Taniguchi T, Onoda K, Sato K, Shiratori Y

Studies in health technology and informatics 302 巻頁： 821 - 822 2023年5月

担当区分：筆頭著者,　最終著者記述言語：英語掲載種別：研究論文（学術雑誌）出版者・発行元：Studies in Health Technology and Informatics

Predicting important outcomes in patients with complex medical conditions using multimodal electronic medical records remains challenge. We trained a machine learning model to predict the inpatient prognosis of cancer patients using EMR data with Japanese clinical text records, which has been considered difficult due to its high context. We confirmed high accuracy of the mortality prediction model using clinical text in addition to other clinical data, suggesting applicability of this method to cancer.

DOI： 10.3233/SHTI230276

A comprehensible machine learning tool to differentially diagnose idiopathic pulmonary fibrosis from other chronic interstitial lung diseases. 査読有り国際誌

Furukawa T, Oyama S, Yokota H, Kondoh Y, Kataoka K, Johkoh T, Fukuoka J, Hashimoto N, Sakamoto K, Shiratori Y, Hasegawa Y

Respirology (Carlton, Vic.) 27 巻 ( 9 ) 頁： 739 - 746 2022年9月

担当区分：責任著者記述言語：英語掲載種別：研究論文（学術雑誌）出版者・発行元：Respirology

Background and objective: Idiopathic pulmonary fibrosis (IPF) has poor prognosis, and the multidisciplinary diagnostic agreement is low. Moreover, surgical lung biopsies pose comorbidity risks. Therefore, using data from non-invasive tests usually employed to assess interstitial lung diseases (ILDs), we aimed to develop an automated algorithm combining deep learning and machine learning that would be capable of detecting and differentiating IPF from other ILDs. Methods: We retrospectively analysed consecutive patients presenting with ILD between April 2007 and July 2017. Deep learning was used for semantic image segmentation of HRCT based on the corresponding labelled images. A diagnostic algorithm was then trained using the semantic results and non-invasive findings. Diagnostic accuracy was assessed using five-fold cross-validation. Results: In total, 646,800 HRCT images and the corresponding labelled images were acquired from 1068 patients with ILD, of whom 42.7% had IPF. The average segmentation accuracy was 96.1%. The machine learning algorithm had an average diagnostic accuracy of 83.6%, with high sensitivity, specificity and kappa coefficient values (80.7%, 85.8% and 0.665, respectively). Using Cox hazard analysis, IPF diagnosed using this algorithm was a significant prognostic factor (hazard ratio, 2.593; 95% CI, 2.069–3.250; p < 0.001). Diagnostic accuracy was good even in patients with usual interstitial pneumonia patterns on HRCT and those with surgical lung biopsies. Conclusion: Using data from non-invasive examinations, the combined deep learning and machine learning algorithm accurately, easily and quickly diagnosed IPF in a population with various ILDs.

添付ファイル： Respirology - 2022 - Furukawa - A comprehensible machine learning tool to differentially diagnose idiopathic pulmonary.pdf

DOI： 10.1111/resp.14310

その他リンク： https://onlinelibrary.wiley.com/doi/full-xml/10.1111/resp.14310

Telerehabilitation Based on Markerless Motion Capture and IMT-2020 (5G) Networks. 国際誌

Oyama S, Saeki M, Kaneta S, Shimoda S, Yoneda H, Hirata H

Studies in health technology and informatics 290 巻頁： 1108 - 1109 2022年6月

担当区分：筆頭著者,　最終著者記述言語：英語掲載種別：研究論文（学術雑誌）出版者・発行元：Studies in Health Technology and Informatics

Eldercare programs such as health consultations and physiotherapy that improve the well-being and extend the life expectancy of people in rural or sparsely populated areas is a socially important though costly problem. We ran a pilot project to test the effectiveness potential of telerehabilitation using markerless motion capture technology integrated in a fast and low-latency IMT-2020 (5G) mobile network. Accelerating technological innovations and the surge in advances of telehealth will greatly impact conventional home visit or outpatient rehabilitation services, working in concert with or even supplanting them, given the potential lower cost and better utilization of time.

添付ファイル： SHTI-290-SHTI220291.pdf

DOI： 10.3233/SHTI220291

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スマートホスピタル構想における汎用型多目的ロボットの活用招待有り査読有り

大山慎太郎, 山下佳子, 小倉環, 藤原友莉子, 高椋大寛, 白鳥義宗

情報処理 63 巻 ( 5 ) 頁： 1 - 32 2022年4月

Nursing behavior recognition and presenteeism risk analysis using IoT sensing technology.

Keiko Yamashita, Shintaro Oyama, Teruhiko Suzuki, Yuji Sakamoto, Yoshinori Ideno, Satoshi Yamashita, Satomi Akagawa, Akiko Fujii, Kensaku Mori, Yoshimune Shiratori

AMIA 2022年

疼痛診療のオラリティ情報を評価するシステム査読有り

大山慎太郎, 岩月克之, 平田仁

Journal of Musculoskeletal Pain Research 13 巻 ( 4 ) 頁： S57 - S57 2021年10月

Smart hospital infrastructure: geomagnetic in-hospital medical worker tracking. 国際誌

Yamashita K, Oyama S, Otani T, Yamashita S, Furukawa T, Kobayashi D, Sato K, Sugano A, Funada C, Mori K, Ishiguro N, Shiratori Y

Journal of the American Medical Informatics Association : JAMIA 28 巻 ( 3 ) 頁： 477 - 486 2021年3月

担当区分：責任著者記述言語：英語掲載種別：研究論文（学術雑誌）出版者・発行元：Journal of the American Medical Informatics Association

Purpose: Location visualization is essential for locating people/objects, improving efficiency, and preventing accidents. In hospitals, Wi-Fi, Bluetooth low energy (BLE) Beacon, indoor messaging system, and similar methods have generally been used for tracking, with Wi-Fi and BLE being the most common. Recently, nurses are increasingly using mobile devices, such as smartphones and tablets, while shifting. The accuracy when using Wi-Fi or BLE may be affected by interference or multipath propagation. In this research, we evaluated the positioning accuracy of geomagnetic indoor positioning in hospitals. Materials and Methods: We compared the position measurement accuracy of a geomagnetic method alone, Wi-Fi alone, BLE beacons alone, geomagnetic plus Wi-Fi, and geomagnetic plus BLE in a general inpatient ward, using a geomagnetic positioning algorithm by GiPStech. The existing Wi-Fi infrastructure was used, and 20 additional BLE beacons were installed. Our first experiment compared these methods' accuracy for 8 test routes, while the second experiment verified a combined geomagnetic/BLE beacon method using 3 routes based on actual daily activities. Results: The experimental results demonstrated that the most accurate method was geomagnetic/BLE, followed by geomagnetic/Wi-Fi, and then geomagnetic alone. Discussion: The geomagnetic method's positioning accuracy varied widely, but combining it with BLE beacons reduced the average position error to approximately 1.2 m, and the positioning accuracy could be improved further. We believe this could effectively target humans (patients) where errors of up to 3 m can generally be tolerated. Conclusion: In conjunction with BLE beacons, geomagnetic positioning could be sufficiently effective for many in-hospital localization tasks.

DOI： 10.1093/jamia/ocaa204

Experimental pilot study for augmented reality-enhanced elbow arthroscopy. 国際誌

Yamamoto M, Oyama S, Otsuka S, Murakami Y, Yokota H, Hirata H

Scientific reports 11 巻 ( 1 ) 頁： 4650 - 4650 2021年2月

担当区分：責任著者記述言語：英語掲載種別：研究論文（学術雑誌）出版者・発行元：Scientific Reports

The purpose of this study was to develop and evaluate a novel elbow arthroscopy system with superimposed bone and nerve visualization using preoperative computed tomography (CT) and magnetic resonance imaging (MRI) data. We obtained bone and nerve segmentation data by CT and MRI, respectively, of the elbow of a healthy human volunteer and cadaveric Japanese monkey. A life size 3-dimensional (3D) model of human organs and frame was constructed using a stereo-lithographic 3D printer. Elbow arthroscopy was performed using the elbow of a cadaveric Japanese monkey. The augmented reality (AR) range of error during rotation of arthroscopy was examined at 20 mm scope–object distances. We successfully performed AR arthroscopy using the life-size 3D elbow model and the elbow of the cadaveric Japanese monkey by making anteromedial and posterior portals. The target registration error was 1.63 ± 0.49 mm (range 1–2.7 mm) with respect to the rotation angle of the lens cylinder from 40° to − 40°. We attained reasonable accuracy and demonstrated the operation of the designed system. Given the multiple applications of AR-enhanced arthroscopic visualization, it has the potential to be a next-generation technology for arthroscopy. This technique will contribute to the reduction of serious complications associated with elbow arthroscopy.

DOI： 10.1038/s41598-021-84062-7

Development Of A Machine Learning Combination With Deep Learning For Diagnosing Idiopathic Pulmonary Fibrosis In Chronic Interstitial Lung Disease

Furukawa, T; Oyama, S; Yokota, H; Kondoh, Y; Shiratori, Y; Hasegawa, Y

EUROPEAN RESPIRATORY JOURNAL 56 巻 2020年9月

近赤外線カメラを利用した血管及び神経の術中画像分離

大山慎太郎, 森下壮一郎, 横田秀夫, 平田仁

日本手外科学会雑誌 34 巻 ( 5 ) 頁： 898 - 901 2018年2月

担当区分：筆頭著者記述言語：日本語掲載種別：研究論文（学術雑誌）出版者・発行元：(一社)日本手外科学会

筆者らは手術中に肉眼で区別することが困難な状況の血管及び神経等重要組織の把握に役に立つ,近赤外線光カメラを使用した非蛍光での組織分離手法を開発した.本手法は近赤外光の組織における散乱・吸収特性に基づき,生体組織の上からハロゲン光を照射し,生体組織で吸収・反射されて戻ってくる近赤外スペクトルを画像として計測し,独立成分分析を基にした画像解析を行う事で重要組織を判別する手法である.ニホンザルより採取した腋窩組織における神経血管束の画像分離を行い,3名の手外科医に対する正解率の変化を調査した結果,提案手法における神経血管束走行の把握に関する一定の診断貢献効果が認められた.(著者抄録)

添付ファイル：手の外科論文.pdf

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新しい医療技術近赤外光・テラヘルツ光を用いた新しい体外診断法

大山慎太郎, 平田仁

整形・災害外科 60 巻 ( 13 ) 頁： 1611 - 1616 2017年12月

Biomechanical Reconstruction Using the Tacit Learning System: Intuitive Control of Prosthetic Hand Rotation. 査読有り国際誌

Oyama S, Shimoda S, Alnajjar FS, Iwatsuki K, Hoshiyama M, Tanaka H, Hirata H

Frontiers in neurorobotics 10 巻頁： 19 - 19 2016年

記述言語：英語掲載種別：研究論文（学術雑誌）出版者・発行元：Frontiers in Neurorobotics

Background: For mechanically reconstructing human biomechanical function, intuitive proportional control, and robustness to unexpected situations are required. Particularly, creating a functional hand prosthesis is a typical challenge in the reconstruction of lost biomechanical function. Nevertheless, currently available control algorithms are in the development phase. The most advanced algorithms for controlling multifunctional prosthesis are machine learning and pattern recognition of myoelectric signals. Despite the increase in computational speed, these methods cannot avoid the requirement of user consciousness and classified separation errors. “Tacit Learning System” is a simple but novel adaptive control strategy that can self-adapt its posture to environment changes. We introduced the strategy in the prosthesis rotation control to achieve compensatory reduction, as well as evaluated the system and its effects on the user. Methods: We conducted a non-randomized study involving eight prosthesis users to perform a bar relocation task with/without Tacit Learning System support. Hand piece and body motions were recorded continuously with goniometers, videos, and a motion-capture system. Findings: Reduction in the participants’ upper extremity rotatory compensation motion was monitored during the relocation task in all participants. The estimated profile of total body energy consumption improved in five out of six participants. Interpretation: Our system rapidly accomplished nearly natural motion without unexpected errors. The Tacit Learning System not only adapts human motions but also enhances the human ability to adapt to the system quickly, while the system amplifies compensation generated by the residual limb. The concept can be extended to various situations for reconstructing lost functions that can be compensated.

DOI： 10.3389/fnbot.2016.00019

Eyes and movement differences in unconscious state during microscopic procedures. 国際誌

Fujita A, Oyama S, Tatebe M, Shimoda S, Tokutake K, Yamamoto M, Hirata H

Scientific reports 15 巻 ( 1 ) 頁： 6712 - 6712 2025年2月

Deep Learning for Predicting Acute Exacerbation and Mortality of Interstitial Lung Disease. 国際誌

Teramachi R, Furukawa T, Kondoh Y, Karasuyama M, Hozumi H, Kataoka K, Oyama S, Suda T, Shiratori Y, Ishii M

Annals of the American Thoracic Society 2024年12月

Artificial intelligence-based personalized survival prediction using clinical and radiomics features in patients with advanced non-small cell lung cancer. 国際誌

Koyama J, Morise M, Furukawa T, Oyama S, Matsuzawa R, Tanaka I, Wakahara K, Yokota H, Kimura T, Shiratori Y, Kondoh Y, Hashimoto N, Ishii M

BMC cancer 24 巻 ( 1 ) 頁： 1417 - 1417 2024年11月

記述言語：英語掲載種別：研究論文（学術雑誌）出版者・発行元：BMC Cancer

Background: Multiple first-line treatment options have been developed for advanced non-small cell lung cancer (NSCLC) in each subgroup determined by predictive biomarkers, specifically driver oncogene and programmed cell death ligand-1 (PD-L1) status. However, the methodology for optimal treatment selection in individual patients is not established. This study aimed to develop artificial intelligence (AI)-based personalized survival prediction model according to treatment selection. Methods: The prediction model was built based on random survival forest (RSF) algorithm using patient characteristics, anticancer treatment histories, and radiomics features of the primary tumor. The predictive accuracy was validated with external test data and compared with that of cox proportional hazard (CPH) model. Results: A total of 459 patients (training, n = 299; test, n = 160) with advanced NSCLC were enrolled. The algorithm identified following features as significant factors associated with survival: age, sex, performance status, Brinkman index, comorbidity of chronic obstructive pulmonary disease, histology, stage, driver oncogene status, tumor PD-L1 expression, administered anticancer agent, six markers of blood test (sodium, lactate dehydrogenase, etc.), and three radiomics features associated with tumor texture, volume, and shape. The C-index of RSF model for test data was 0.841, which was higher than that of CPH model (0.775, P < 0.001). Furthermore, the RSF model enabled to identify poor survivor treated with pembrolizumab because of tumor PD-L1 high expression and those treated with driver oncogene targeted therapy according to driver oncogene status. Conclusions: The proposed AI-based algorithm accurately predicted the survival of each patient with advanced NSCLC. The AI-based methodology will contribute to personalized medicine. Trial registration: The trial design was retrospectively registered study performed in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of Nagoya University Graduate School of Medicine (approval: 2020 − 0287).

DOI： 10.1186/s12885-024-13190-w

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