The anterior drawer test (ADT) is the gold standard examination for the diagnosis of anterior talofibular ligamentinsufficiency,although there is noquantitative evaluation of ADT that is generally usable and reliable.An electromagnetic sensor (EMS)has been used to quantitatively evaluate joint kinematics, and has a high potential to be applied to the ankle joint. The aim of this study was to validatethe EMS measurement of the ADTin comparison to the fluoroscopic evaluationand to evaluate the reproducibility of the EMS measurement.Six feet were included,and an examinerperformed the ADT5 times for each foot while the anterior translation of the ankle jointwas quantitative evaluatedusing EMS and fluoroscope simultaneously. The anterior translation of the ankle joint during the ADT in EMS and in fluoroscope was 8.1 ± 5.7 mm and 3.6 ± 2.4 mm.Astrong correlation was observed between the measurements using EMS and fluoroscope (p < 0.01, the correlation coefficient = 0.91). Another 20 feet were included, and three examiners performed the ADT five times for each foot with the EMS measurement. The intra and inter-examiner reliability was 0.99 and 0.89.The EMS could quantify the anterior translation during the ADT which corresponds to fluoroscopic evaluation.

Gait motion is strongly relating with human health. Therefore, it is important to analyze the gait motion. This study aims to develop a system to measure sole pressure, acceleration and angular velocity in gait motion. Then the system can measure the parameters by using seven force sensors and six-axis inertial sensor for each sole. The experiment was performed with three normal subjects by using the proposed system. An analysis method consists of three steps. The first step specifies gait cycle from the value of the acceleration. The second step calculates the excursion of ankle joint by integrating the angular velocity. The third step specifies foremost and hindmost points of gravity center in gait cycle from the seven force sensors and calculates anteroposterior distance of gravity centers. The target parameters were excursion of ankle joint and anteroposterior distance of gravity centers. In the experiment, stride lengths of the subjects were three patterns of 10 cm, 50 cm, and 100 cm. There was a tendency that the ankle excursion was small when the stride was small.

Comparison of anatomical or functional parameters is common theme in the evaluation of unilateral pathologies of the human knee joint, although the degree of bilateral symmetry is unknown. Currently, the evaluation is performed manually. Therefore, diversity of the evaluation happens due to the subjective judgment. To reduce the diversity, we propose an automated analysis method for anatomical knee structure from multi-detector row computerized tomography (MDCT) image. The experiment employed six different knees. Then, symmetry data were made from the six raw data by using mirroring processing. The six raw data and six mirroring data were evaluated by the proposed and manual method. The results of the proposed method indicated that there was no difference between the raw and mirroring data, while the results of the manual method clarified that there is almost two mm difference between them. It is concluded that the proposed method could identify the anatomical structure with no difference between bilateral knees.

In total knee arthroplasty (TKA), many surgical instruments are available. Many of these surgical instruments are similar in shape and size. For this reason, there have been accidents due to incorrect selection of implants. Furthermore, a shortage of nurses is expected worldwide. There will also be a shortage of scrub nurses, which will result in an increased burden on each scrub nurse. For these reasons, we have developed a surgical instrument detection system for TKA to reduce the burden on scrub nurses and the number of accidents, such as implant selection errors. This study also focuses on automating the acquisition of data for training. We also develop a method to reduce the additional training time when the number of detection targets increases. In this study, YOLOv5 is used as the object detection method. In experiments, we examine the accuracy of the training data automatically acquired and the accuracy of object detection for surgical instruments. In object detection, several training files are created and compared. The results show that the training data is sufficiently effective, and high accuracy is obtained in object detection. Object detection is performed in several cases, and one of the results shows an IoU of 0.865 and an F-measure of 0.930.