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Featured researches published by Takatoshi Morooka.


international conference on intelligent robotics and applications | 2016

Post-operative Implanted Knee Kinematics Prediction in Total Knee Arthroscopy Using Clinical Big Data

Belayat Hossain; Manabu Nii; Takatoshi Morooka; Makiko Okuno; Shiichi Yoshiya; Syoji Kobashi

Total knee arthroscopy TKA is a very effective surgery for damaged knee joint treatment. Because, there are some TKA operation methods and TKA implant products, it is difficult to decide an appropriate one at the pre-operative planning. This study introduces a novel approach to assist surgeon for the pre-operative planning, and proposes a prediction method of post-operative knee joint kinematics. The method is based on principal component analysis PCA for characteristics extraction, and machine learning algorithms. The proposed method was validated by leave-one-out cross validation test in 46 osteoarthritis OA knee patients. The results show that the proposed method can predict the post-operative knee joint kinematics from the pre-operative one with a mean correlation coefficient of 0.69, and a root-mean-squared-error RMSE of 1.8i¾?mm.


international conference on robot, vision and signal processing | 2013

On A Priori Knowledge in Particle Filter for In-Vivo Analysis of Implanted Knee

Shohei Tada; Syoji Kobashi; Kei Kuramoto; Fumiaki Imamura; Takatoshi Morooka; Shinichi Yoshiya; Yutaka Hata

Total knee arthroplasty (TKA) is an orthopedic surgery which replaces the damaged knee joint with the artificial one. To diagnose the function of the implanted knee joint, it is effective to estimate 3-D knee kinematics in vivo. There are some conventional methods for estimating kinematics of the implanted knee using 2-D/3-D image registration for X-ray fluoroscopic images and 3-D geometrical models of the knee implant. This paper proposes a method for analyzing knee kinematics based on particle filter which became high precision using priori knowledge. The experimental results showed that the proposed method left the grade that was better than non-priori-knowledge method.


European Journal of Orthopaedic Surgery and Traumatology | 2018

Intraoperative kinematic analysis of posterior stabilized total knee arthroplasty with asymmetric helical post-cam design

Takatoshi Morooka; Makiko Okuno; Daisuke Seino; Takuya Iseki; Shigeo Fukunishi; Syoji Kobashi; Shinichi Yoshiya

PurposeTo investigate intraoperative kinematics during passive flexion using a surgical navigation system for knees undergoing posterior stabilized (PS) total knee arthroplasty (TKA) with an asymmetric helical post-cam design using navigation system.MethodsIn total, 45 knees with both pre- and postoperative kinematic data available were included in the study. Intraoperative kinematic measurements were performed during the course of surgery using the software incorporated in the navigation system. Measurements were performed at the following two time points: (1) before TKA procedure and (2) after TKA implantation. Among the kinematic parameters studied, anterior/posterior translation and axial rotation during flexion were subjected to the analysis.ResultsBefore surgery, physiologic anterior/posterior translational pattern of the tibia during flexion (rollback of the femur) was found in only 15.6% of the knees. After TKA implantation, postoperative kinematic measurement showed no significant change in the tibial translational during knee flexion. Similarly, with regard to rotation, non-physiologic external tibial rotation in early flexion was observed in the majority of the knees before surgery, and this abnormal kinematic pattern remained after the TKA procedure.ConclusionsThe intraoperative three-dimensional motion analysis using a navigation system showed that the physiologic kinematic pattern (anterior translation and internal rotation of the tibia during flexion) of the knee was distorted in osteoarthritic knees undergoing TKA. The abnormal kinematic pattern before surgery was not fully corrected even after implantation of the PS TKA designed to induce natural knee motion; however, no clear relationship between the intraoperative kinematic pattern and knee flexion angle at one year was demonstrated, and the effect of knee kinematics on postoperative knee function and patient’s satisfaction is still unclear.


systems, man and cybernetics | 2017

Particle filter based implanted knee kinematics analysis for the postoperative evaluation

Kento Morita; Manabu Nii; Norikazu Ikoma; Takatoshi Morooka; Shinichi Yoshiya; Syoji Kobashi

Total knee arthroplasty (TKA) improves patients 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.


international conference on machine learning and cybernetics | 2016

Prediction of post-operative implanted knee function using machine learning in clinical big data

Syoji Kobashi; Belayat Hossain; Manabu Nii; Shunichiro Kambara; Takatoshi Morooka; Makiko Okuno; Shinichi Yoshiya

Total knee arthroplasty (TKA) is one of the common knee surgeries. Because there are some types of TKA implant, it is hard to select appropriate type of TKA implant for individual patient. For the sake of pre-operative planning, this study presents a novel approach, which predicts post-operative implanted knee function of individuals. It is based on a clinical big data analysis. The big data is composed by a set of pre-operative knee mobility function and post-operative knee function. The method constructs a post-operative knee function prediction model by means of a machine learning approach. It extracts features using principal component analysis, and constructs a mapping function from pre-operative feature space to post-operative feature space. The method was validated by applying to prediction of post-operative anterior-posterior translation in 52 TKA operated knees. Leave-one-out cross validation test revealed the prediction performances with a mean correlation coefficients of 0.79 and a mean root-mean-squared-error of 3.44 mm.


Case reports in orthopedics | 2014

Double Threaded Screw Fixation for Bilateral Stress Fracture of the Medial Malleolus

Ryo Kanto; Shigeo Fukunishi; Takatoshi Morooka; Daisuke Seino; Takayuki Takashima; Shinichi Yoshiya; Juichi Tanaka

An 18-year-old college basketball player presented with continued ankle pain. A radiographic examination showed bilateral medial malleolus stress fractures. Considering the prolonged history and refractory nature of this injury, surgery was adopted as a treatment option. At surgery, the fracture site was percutaneously fixed using two cannulated double threaded screws. Surgery for each side was sequentially performed two months apart. Prompt bony healing was attained after surgery, and the patient could return to his previous sports level six months after the first surgery without subsequent recurrence.


Journal of Arthroplasty | 2016

Local Infiltration Analgesia Versus Continuous Femoral Nerve Block in Pain Relief After Total Knee Arthroplasty: A Randomized Controlled Trial

Kenji Kurosaka; Sachiyuki Tsukada; Daisuke Seino; Takatoshi Morooka; Hiroshi Nakayama; Shinichi Yoshiya


soft computing | 2016

Implanted Knee Kinematics Analysis by 2-D/3-D Registration Using Particle Filter

Kento Morita; Manabu Nii; Fumiaki Imamura; Takatoshi Morooka; Shinichi Yoshiya; Syoji Kobashi


Journal of Advanced Computational Intelligence and Intelligent Informatics | 2018

Implanted Knee Joint Kinematics Recognition in Digital Radiograph Images Using Particle Filter

Kento Morita; Manabu Nii; Norikazu Ikoma; Takatoshi Morooka; Shinichi Yoshiya; Syoji Kobashi


Intelligent Automation and Soft Computing | 2018

Surgical Outcome Prediction in Total Knee Arthroplasty using Machine Learning

Belayat Hossain; Takatoshi Morooka; Makiko Okuno; Manabu Nii; Shinichi Yoshiya; Syoji Kobashi

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Makiko Okuno

Hyogo College of Medicine

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Daisuke Seino

Hyogo College of Medicine

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Fumiaki Imamura

Hyogo College of Medicine

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Norikazu Ikoma

Nippon Institute of Technology

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