畑 豊

Assisted reproductive technology (ART) has progressed rapidly, resulting in a great improvement in the clinical pregnancy ratio. When applying the protocol of piezo intracytoplasmic sperm injection (Piezo-ICSI), it is very important to puncture the zona pellucida and the oocyte cytoplasmic membrane without rupturing the oocyte cytoplasmic membrane. Previous studies have shown that the poor extensibility of the oocyte cytoplasmic membrane might be closely related to rupture. However, no consensus has been reached regarding how the quality of the oocyte for extensible ability or rupture possibility affects the surfaces of the oocyte on the microscopic frames. We conducted this study to provide evidence that artificial intelligence (AI) techniques are superior for predicting the tendency of oocyte rupture before puncturing on Piezo-ICSI. To inspect it, we provided a retrospective trial of 38 rupture oocytes and 55 nonruptured oocytes. This study marked the highest accuracy of 91.4% for predicting oocytes rupture using the support-vector machine method of machine learning. We conclude that AI technologies might serve an important role and provide a significant benefit to ART.

RESEARCH QUESTION: One of the problems during the intracytoplasmic sperm injection (ICSI) procedure is unintentional membrane rupture (UMR), which often predisposes to subsequent oocyte degeneration. Can the ICSI Position Detector (IPD) be useful in identifying the optimal puncture location to prevent UMR during ICSI? DESIGN: A total of 709 mature oocytes were included. Conventional ICSI was carried out and images were recorded by IPD; these were analysed retrospectively. RESULTS: Inseminated oocytes were retrospectively grouped according to the IPD, irrespective of whether oolemma was punctured at an area in which UMR is likely (non-appropriate group) or unlikely (appropriate group). In the appropriate group, rates of UMR (5.3% versus 18.2%) and degeneration (2.5% versus 8.7%) were significantly lower than those of the non-appropriate group, whereas rate of fertilization (87.1% versus 69.7%) was significantly higher than those of the non-appropriate group, respectively (P < 0.001). These differences remained even after propensity score matching to adjust for potential differences in characteristics between appropriate and non-appropriate groups. CONCLUSIONS: This study demonstrated that the IPD is useful to identify the optimal puncture location to circumvent UMR during the ICSI procedure, resulting in reduced UMR and oocyte degeneration, thereby, generating more embryos available for transfer or cryopreservation.

It has recently been shown that the aging population is refractory to the maintenance of swallowing function, which can seriously affect quality of life. Singing and vocal training contribute to mastication, swallowing and respiratory function. Previous studies have shown that singers have better vocal cord health. No consensus has been reached as to how vocal training affects swallowing ability. Our study was designed to establish evidence that singers are statistically superior at inducing the swallowing reflex. To test our hypothesis, we undertook a clinical trial on 55 singers and 141 non-singers (mean age: 60.1 ± 11.7 years). This cross-sectional study with propensity score matching resulted in significant differences in a repetitive saliva swallowing test among singers: 7.1 ± 2.4, n = 53 vs. non-singers: 5.9 ± 1.9, n = 53, p < 0.05. We conclude that singing can serve an important role in stabilizing the impact of voluntary swallowing on speech.

<title>Abstract</title>Corticokinematic coherence (CKC) between magnetoencephalographic and movement signals using an accelerometer is useful for the functional localization of the primary sensorimotor cortex (SM1). However, it is difficult to determine the tongue CKC because an accelerometer yields excessive magnetic artifacts. Here, we introduce a novel approach for measuring the tongue CKC using a deep learning-assisted motion capture system with videography, and compare it with an accelerometer in a control task measuring finger movement. Twelve healthy volunteers performed rhythmical side-to-side tongue movements in the whole-head magnetoencephalographic system, which were simultaneously recorded using a video camera and examined using a deep learning-assisted motion capture system. In the control task, right finger CKC measurements were simultaneously evaluated via motion capture and an accelerometer. The right finger CKC with motion capture was significant at the movement frequency peaks or its harmonics over the contralateral hemisphere; the motion-captured CKC was 84.9% similar to that with the accelerometer. The tongue CKC was significant at the movement frequency peaks or its harmonics over both hemispheres. The CKC sources of the tongue were considerably lateral and inferior to those of the finger. Thus, the CKC with deep learning-assisted motion capture can evaluate the functional localization of the tongue SM1.

Correct assessment of the bone healing process is required for the management of limb immobilization during the treatment of bone injuries, including fractures and defects. Although the monitoring of bone healing using ultrasound poses several advantages regarding cost and ionizing radiation exposure compared with other dominant imaging methods, such as radiography and computed tomography (CT), traditional ultrasound B-mode imaging lacks reliability and objectivity. However, the body structures can be quantitatively observed by ultrasound frequency-based methods, and therefore, the disadvantages of B-mode imaging can be overcome. In this study, we created a femoral bone hole model of a rat and observed the bone healing process using the quantitative ultrasound method and micro-CT, which provides a reliable assessment of the tissue microstructure of the bone. This study analyzed the correlation between these two assessments. The results revealed that the quantitative ultrasound measurements correlated with the CT measurements for rat bone healing. This ultrasound frequency-based method could have the potential to serve as a novel modality for quantitative monitoring of bone healing with the advantages of being less invasive and easily accessible. Impact statement Bone healing monitoring with ultrasound is advantageous as it is less invasive and easily accessible; however, the traditional B-mode method lacks reliability and objectivity. This study demonstrated that the proposed ultrasound frequency-based monitoring method can quantitatively observe bone healing and strongly correlates with the computed tomography measurements for rat bone healing. This method has the potential to become a reliable modality for monitoring bone healing.

Enhanced conformational sampling, a genetic-algorithm-guided multidimensional virtual-system coupled molecular dynamics, can provide equilibrated conformational distributions of a receptor protein and a flexible ligand at room temperature. The distributions provide not only the most stable but also semistable complex structures and propose a ligand-receptor binding process. This method was applied to a system consisting of a receptor protein, 14-3-3ε, and a flexible peptide, phosphorylated myeloid leukemia factor 1 (pMLF1). The results present comprehensive binding pathways of pMLF1 to 14-3-3ε. We identified four thermodynamically stable clusters of MLF1 on the 14-3-3ε surface and free-energy barriers among some clusters. The most stable cluster includes two high-density spots connected by a narrow corridor. When pMLF1 passes the corridor, a salt-bridge relay (switching) related to the phosphorylated residue of pMLF1 occurs. Conformations in one high-density spot are similar to the experimentally determined complex structure. Three-dimensional distributions of residues in the intermolecular interface rationally explain the binding constant changes resulting from the alanine mutation experiment for the residues. We also performed a simulation of nonphosphorylated peptide and 14-3-3ε, which demonstrated that the complex structure was unstable, suggesting that phosphorylation of the peptide is crucially important for binding to 14-3-3ε.

<sec> <title>Aims:</title> The purpose of this study was to classify complicated uterine movements obtained by MRI scanner and investigate the relationship between uterine peristalsis and female infertility. </sec> <sec> <title>Methods:</title> Uterine movements are classified into six fundamental movements by their motility form and directions. Computer simulation of the uterine movements is performed. </sec> <sec> <title>Results:</title> Comparison results between the real MRI images and the simulated images showed that any five in our dataset uterine movement was successfully reproduced by a combination of these six fundamental movements. The point and surface vibration model appropriately mimicked the movements with the propagation velocity of 0.68 [mm/sec]. </sec> <sec> <title>Conclusion:</title> By analyzing six fundamental movements using data from 26 MRI scans, it was found that two fundamental movements were identified as candidate factors for female infertility. </sec>

We introduced a conformational sampling method in an earlier report: The multi-dimensional virtual-system coupled molecular dynamics (mD-VcMD) enhances conformational sampling of a biomolecular system by computer simulations. Herein, new sampling method, a subzone-based mD-VcMD, is presented as an extension of mD-VcMD. Then, the subzone-based method is extended further using a genetic algorithm (GA) named the GA-guided mD-VcMD. In these methods, iterative simulation runs are performed to increase the sampled region gradually. The new methods have the following benefits: (1) They are free from a production run: i.e., all snapshots from all iterations are useful for analyses. (2) They are free from fine tuning of a weight function (probability distribution function or potential of mean force). (3) A canonical ensemble (i.e., a thermally equilibrated ensemble) is generated from a simple procedure. A thermodynamic weight is assigned to each snapshot. (4) Selective sampling can be performed for particularly addressing a poorly sampled region without breaking the proportion of the canonical ensemble if the poorly sampled conformational region emerges in sampling. By applying the methods to a simple system that involves an energy barrier between potential-energy minima, we demonstrated that the new methods have considerably higher sampling efficiency than the original mD-VcMD does.

© 2019 IEEE. In order to enhance and improve labor productivity, we have developed an automated system for evaluating the worker's skills by using labeled latent Dirichlet allocation (L-LDA). Since the L-LDA learns the characteristic motions automatically, we do not need to find and define any features of the motion. The elemental processes are analyzed by the L-LDA. The worker's skills are evaluated based on the analyzed time series elemental process data. The evaluated worker's skills are correctness, stability, speed, and rhythm of the work. The results confirmed that our proposed evaluation system is capable of automatically providing a new analysis over the conventional evaluation method with only working time. For example, an evaluation experiment was done to one subject. This result showed the speed category to be lower than the other categories. Then, we know that the subject lacks parallelism work skill. These results give new knowledge that never obtained by the conventional evaluation method.

© 2019 IEEE. In this paper, we described an automated analyzing method for the elemental processes. This method predicted the elemental processes from the sensor data by using labeled latent Dirichlet allocation (L-LDA) that is supervised topic model. The L-LDA studies automatically characteristic motion. We do not need to define characteristic motion by applying the L-LDA to motion analysis. The sensor data are motion sensors of both hands and a pressure sensor of working space. Numerical data obtained from the sensors were converted into word data by the threshold process using statistically determined thresholds. The automated analysis by the L-LDA was conducted by using the word data. We confirmed that recall by the method was over 86.9% by the evaluation experiment.

© 2019 IEEE. This paper aims to determine the optimal puncture position of ovum by evaluating rupture membrane of cytoplasm. We employed 139 ovum images on the Piezo-ICSI (Intracytoplasmic sperm injection). In it, grayscale images before puncture and their actual puncture position were obtained from the movie file (Rupture:31, No Rupture:108), and Local Binary Pattern (LBP) feature is calculated at analysis area around the puncture position. LBP feature dimensions are reduced, and data are classified by hierarchical clustering method using feature of three dimensions. As a result, the data classified into two clusters (Clusters A and B). Cluster A has 7 Ruptures and 50 No Ruptures, Cluster B has 24 Ruptures and 58 No Ruptures. Then, the sensitivity is 0.77. Therefore, it is possible to evaluate rupture membrane of cytoplasm from shape feature of membrane. The optimal puncture position could be determined by the features.

© 2019 IEEE. This paper analyzed the electrocardiograms obtained from workers with different proficient levels and considered the stress index. As an example of a simple work, we analyzed the process of combining three cases (Case combination) and the step of inserting nine parts (DIP insertion) into the foundation. We have classified the subjects as beginners and experienced groups with different levels of proficiency, and performed frequency analysis on electrocardiograms measured during each process. Following that we calculated the heart beat interval time R-R interval (RRI) from the measurement result and calculated low-frequency (LF) and high-frequency (HF) by PSD estimation. Moreover, we calculated the ratio LF/ HF of sympathetic activity (LF) and parasympathetic activity (HF), and compared it with those of beginners and experts. As a result, we confirmed that the value of LF/HF during work based on beginner's resting time was larger than that of experienced person.

© 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.

© 2018 IEEE. Currently, rice diseases caused by various weather disorders and pests are serious problems. In this research, we will develop a system to classify healthy rice plants and diseased rice plants by processing images from rice planted in paddy field. Symptoms appear clearly in rice diseases, we focused on their color information. The 256 level intensities of Red, Blue, Green of a pixel are employed as a feature amount, and a classifier using SVM was constructed. In addition, we reduced the learning time by using principal component analysis, and obtained accuracy exceeding 90%.

© 2018 IEEE. This paper describes a fuzzy proficiency evaluation system by using myoelectric potential. This system evaluates proficiency level in soldering operation as an example of manufacturing process an industry. We classified them as beginners and experts, and confirmed the difference by proficiency level by analyzing the surface myoelectric potential measured during work. As the result, the difference found at the integrated myoelectric potential during the operation. This difference decreased with the number of operations, and this was confirmed by the degree of proficiency. Therefore depending on proficiency level as a numerical value in which the difference appeared, the displacement of the integrated myoelectric potential of the working time and the previous work was converted into fuzzy degree, and the degree of proficiency was defined and evaluated using their affiliation degree. As the results, we succeeded in designing an evaluation system in which differences appeared in proficiency level.

© 2018 IEEE. In this study, we analyzed a body information feature in the soldering process in order to develop an evaluative method of the worker skills. First, we found the three types body information features. The found features were the work position, the way to hold a soldering iron, and the effect of the dominant eye and the age. Using the features of the work position and the way to hold a soldering iron, we classified the work rank into four classes. As a result of evaluating operation time and the product quality for these classes, it was confirmed that operation time and the product quality were improved as the work rank improves.

© 2018 IEEE. In this paper, an ultrasound-measurement-based method of detecting stenosises in fallopian tubal models is presented. The proposed method employs silicone rubber tubes and custom phantoms made of polymer gels as models of fallopian tubes. When the targets are the former (or latter), stenosis models are formed by lumps of polyester sewing threads (or polymer gels with high reflection intensity). The proposed method copes with detecting stenosis models as classification problems by applying support vector machines (SVMs for short). It prepare data for SVM learning and stenosis detection from waveforms in the range specified by two time points at which waves are probably reflected by inner upper and lower surfaces of tubal models. The data preparation is conducted by generating the frequency distribution of the number of regular short intervals into which this range is divided versus normalized values associated with amplitude of the waveforms in such intervals. When it is explored whether given data are prepared from a tube with the stenosis model, a discrimination model constructed by SVM learning works as a two-class classifier. Using the model as a multi-class classifier makes it possible to distinguish data of tubes with stenosis models from each other. It is revealed from experimental results that the proposed method has substantially high capability in detecting stenosis models.

© 2018 IEEE. In this paper, we describe a method for extracting features for classifying 'with egg' or 'no egg' in follicles from ultrasonic images during oviposition surgery. In the previous research, it was suggested that classification could be preformed by using the deformation at the time of stabbing a follicle as a feature value. In this paper, the feature value was acquired from the image not stabbed with a needle in the follicle. At that time, we used several images from the time just before the needle touched the follicle. As a result of classification using the feature value obtained by the proposed method. The classification accuracy was achieved about 70%.

© 2018 IEEE. In this study, we analyzed the feature of the uterine ultrasonic image in order to develop an image evaluative method to predict success of the pregnancy for female infertility patient. It is known that 'Leaf pattern' is an indicator of success of pregnancy. A result using LBP found that a region of the endometrium composed by Edge. We confirmed that an angle of Edge was difference between 'Homogeneous pattern and Pregnancy' and 'Homogeneous pattern and No Pregnancy We evaluated a predictive rate of the pregnancy by using this angle. As an evaluation result, the overall accuracy was 69.7%, accuracy of Leaf pattern only was 60.0% and accuracy of Homogeneous pattern only 78.9%. We found that the high predictive rate was obtained for Homogeneous pattern by using this angle.

In recent years, more and more people have been getting married at a later age, and this trend is only going to increase in the near future. As a result, many infertility treatments have been researched upon and developed. One such treatment is in-vitro fertilization, wherein a follicle is directly absorbed from the ovary of the woman and impregnated with a sperm cell under a microscope. Of the total number of follicles in a woman's body, 20-30% are usually observed to contain vacuoles, which do not stimulate vitelline growth. As such, in most cases, one cannot judge whether a follicle has an ovum or a vacuole unless we examine it under a microscope. This study proposes a method capable of distinguishing follicles on the basis of local movement.

© 2017 IEEE. In this paper, a method of calculating inner and outer diameters of fallopian tubes is presented, provided that silicone rubber tubes are models for fallopian tubes. It is based on the ultrasound measurement using a single probe of which the nominal frequency is 5 MHz, and assumes that ultrasound waves emanating from the probe are reflected at the top outer surface, surfaces between water and silicone rubber, and bottom outer surface of the tube. It obtains envelope curves associated with the reflected waves, by applying Hilbert transformation to the waves. It next estimates top four maximal values on the envelope curves to acquire time points when the wave reflections occur. The inner and outer diameters of the tubes are easily estimated by substituting the above time points in simple formulas. Experimental results reveal that the proposed method can achieve substantial accuracy in estimating inner and outer diameters of the target tubes.

© 2016 IEEE. This paper first describes a relation between the frequency and the diameter of columnar soft matter by using broadband ultrasonic single-probe testing. We found an inverse relation between the diameter (100-300 μm) and frequency (1.0-6.0 MHz) of the ultrasonic reflection waveform from columnar soft matter in water. This fact shows that we can determine diameters that are not possible to determine by the standard pulse-echo method. An ultrasonic wavelength with center frequency of 15 MHz is limited to measurements of less than 100-μm thickness in water (wave speed: around 1500 m/s). However, the relation found in this paper enables us to determine these diameters by broadband ultrasonic testing with center frequency of 5.0 MHz, beyond the previous wavelength limitation. Second, this paper proposes a visualization method that uses the discovered relation. An image is produced by using particular frequency components derived via short-time Fourier transform. We employ a phantom consisting of two types of nylon lines, one with thin and the other with thick diameters. The experimental result clearly demonstrated line width and depth beyond the wavelength limitation.

© 2017 IEEE. This paper proposes an automated method to track the tag intersections in the Tagged Magnetic Resonance Image (MRI) to diagnose Heart failure. Heart Failure brings about declining of a function to supply blood by contraction of myocardium. Physician always analyzes myocardial motion using the images. The proposed method employs Mean shift belief propagation (MSBP) that integrates all information about image to track tag intersections. This method enables us to extract tag intersections with disappearing tags over time. The experimental results show a clinical ability of tracking tag intersections.

© 2017 IEEE. A novel method of muscular thickness measurement based on reflection echo from the boundary surface of different layer of target area using ultrasonic wave is presented. By exploring the traveling direction of ultrasonic wave at the interface of fat-muscle or muscle-fat, the difference of the generated reflection echo can be measured. In the proposed method, ultrasonic waves were irradiated to a biological phantom using ultrasound single probe with center frequency of 5.0 MHz to acquire the reflection echo. The boundary surface was identified by applying continuous wavelet transform (CWT) where Mexican hat served as mother wavelet. In an experimental environment, the proposed method was applied to a 3-layer phantom consist of fat-muscle-fat and muscle thickness was measured. The mean absolute error of muscle thickness was measured as 0.03237 mm.

© 2017 IEEE. This paper described a method of automatic extraction of abnormal points from electric cable. The cable changes the color depending on the weather, environment and used period. We define low brightness area and classify size of it. We divide them into abnormal and normal with discriminant analysis method. As the result, we can detect all arc-marks for no rust cables. On the other hand, we can detect almost all arc-mark and much rust for rust cables. We examined that discriminant analysis method successfully detected abnormal points automatically.

Copyright © 2017 The Institute of Electronics, Information and Communication Engineers. To promote effective guidance by health checkup results, this paper predict a likelihood of developing lifestyle-related diseases from health check data. In this paper, we focus on the fluctuation of hemoglobin A1c (HbA1c) value, which deeply connected with diabetes onset. Here we predict incensement of HbA1c value and examine which kind of health checkup item has important role for HbA1c fluctuation. Our experimental results show that, when we classify the subjects according to their gender and triglyceride (TG) fluctuation value, we will effectively evaluate the risk of diabetes onset for each class.

Copyright © 2017 The Institute of Electronics, Information and Communication Engineers. This paper analyzes the relationship between the changes of Body Mass Index (BMI) and those of the other health checkup data in one year. We divide all data of the subjects into 13 groups by their BMI changes. We calculate these variations in each group and classify the variations into gender, age, and BMI. As the result by gender, men were more influenced by the changes of BMI than women at Hb-A1c, AC, GPT, GTP, and TG. As the result of classification by age, they were influenced by the changes of BMI at Hb-A1c, GPT, and DTP by age. As the result of classification by BMI, inspection values such as GOT, GPT, and GTP decreased according to the decrement of BMI. Next we show the result on genderage, gender-BMI, and age-BMI clusters. Our results showed that subjects should reduce BMI values in order to improve lifestyle-related diseases. Several inspection values would be improved according to decrement of BMI. Conversely, it may be difficult for subjects with under 18 of BMI to manage them by BMI. We show a possibility that we could prevent the lifestyle disease by controlling BMI.