Deep learning approach to estimate foot pressure distribution in walking with application for a cost-effective insole system.

BACKGROUND: Foot pressure distribution can be used as a quantitative parameter for evaluating anatomical deformity of the foot and for diagnosing and treating pathological gait, falling, and pressure sores in diabetes. The objective of this study was to propose a deep learning model that could predict pressure distribution of the whole foot based on information obtained from a small number of pressure sensors in an insole. METHODS: Twenty young and twenty older adults walked a straight pathway at a preferred speed with a Pedar-X system in anti-skid socks. A long short-term memory (LSTM) model was used to predict foot pressure distribution. Pressure values of nine major sensors and the remaining 90 sensors in a Pedar-X system were used as input and output for the model, respectively. The performance of the proposed LSTM structure was compared with that of a traditionally used adaptive neuro-fuzzy interference system (ANFIS). A low-cost insole system consisting of a small number of pressure sensors was fabricated. A gait experiment was additionally performed with five young and five older adults, excluding subjects who were used to construct models. The Pedar-X system placed parallelly on top of the insole prototype developed in this study was in anti-skid socks. Sensor values from a low-cost insole prototype were used as input of the LSTM model. The accuracy of the model was evaluated by applying a leave-one-out cross-validation. RESULTS: Correlation coefficient and relative root mean square error (RMSE) of the LSTM model were 0.98 (0.92 ~ 0.99) and 7.9 ± 2.3%, respectively, higher than those of the ANFIS model. Additionally, the usefulness of the proposed LSTM model for fabricating a low-cost insole prototype with a small number of sensors was confirmed, showing a correlation coefficient of 0.63 to 0.97 and a relative RMSE of 12.7 ± 7.4%. CONCLUSIONS: This model can be used as an algorithm to develop a low-cost portable smart insole system to monitor age-related physiological and anatomical alterations in foot. This model has the potential to evaluate clinical rehabilitation status of patients with pathological gait, falling, and various foot pathologies when more data of patients with various diseases are accumulated for training.

Machine Learning Strategies for Low-Cost Insole-Based Prediction of Center of Gravity during Gait in Healthy Males.

Whole-body center of gravity (CG) movements in relation to the center of pressure (COP) offer insights into the balance control strategies of the human body. Existing CG measurement methods using expensive measurement equipment fixed in a laboratory environment are not intended for continuous monitoring. The development of wireless sensing technology makes it possible to expand the measurement in daily life. The insole system is a wearable device that can evaluate human balance ability by measuring pressure distribution on the ground. In this study, a novel protocol (data preparation and model training) for estimating the 3-axis CG trajectory from vertical plantar pressures was proposed and its performance was evaluated. Input and target data were obtained through gait experiments conducted on 15 adult and 15 elderly males using a self-made insole prototype and optical motion capture system. One gait cycle was divided into four semantic phases. Features specified for each phase were extracted and the CG trajectory was predicted using a bi-directional long short-term memory (Bi-LSTM) network. The performance of the proposed CG prediction model was evaluated by a comparative study with four prediction models having no gait phase segmentation. The CG trajectory calculated with the optoelectronic system was used as a golden standard. The relative root mean square error of the proposed model on the 3-axis of anterior/posterior, medial/lateral, and proximal/distal showed the best prediction performance, with 2.12%, 12.97%, and 12.47%. Biomechanical analysis of two healthy male groups was conducted. A statistically significant difference between CG trajectories of the two groups was shown in the proposed model. Large CG sway of the medial/lateral axis trajectory and CG fall of the proximal/distal axis trajectory is shown in the old group. The protocol proposed in this study is a basic step to have gait analysis in daily life. It is expected to be utilized as a key element for clinical applications.

Acceleration Magnitude at Impact Following Loss of Balance Can Be Estimated Using Deep Learning Model.

Pre-impact fall detection can detect a fall before a body segment hits the ground. When it is integrated with a protective system, it can directly prevent an injury due to hitting the ground. An impact acceleration peak magnitude is one of key measurement factors that can affect the severity of an injury. It can be used as a design parameter for wearable protective devices to prevent injuries. In our study, a novel method is proposed to predict an impact acceleration magnitude after loss of balance using a single inertial measurement unit (IMU) sensor and a sequential-based deep learning model. Twenty-four healthy participants participated in this study for fall experiments. Each participant worn a single IMU sensor on the waist to collect tri-axial accelerometer and angular velocity data. A deep learning method, bi-directional long short-term memory (LSTM) regression, is applied to predict a fall's impact acceleration magnitude prior to fall impact (a fall in five directions). To improve prediction performance, a data augmentation technique with increment of dataset is applied. Our proposed model showed a mean absolute percentage error (MAPE) of 6.69 ± 0.33% with r value of 0.93 when all three different types of data augmentation techniques are applied. Additionally, there was a significant reduction of MAPE by 45.2% when the number of training datasets was increased by 4-fold. These results show that impact acceleration magnitude can be used as an activation parameter for fall prevention such as in a wearable airbag system by optimizing deployment process to minimize fall injury in real time.

Machine Learning-Based Pre-Impact Fall Detection Model to Discriminate Various Types of Fall.

Pre-impact fall detection can send alarm service faster to reduce long-lie conditions and decrease the risk of hospitalization. Detecting various types of fall to determine the impact site or direction prior to impact is important because it increases the chance of decreasing the incidence or severity of fall-related injuries. In this study, a robust pre-impact fall detection model was developed to classify various activities and falls as multiclass and its performance was compared with the performance of previous developed models. Twelve healthy subjects participated in this study. All subjects were asked to place an inertial measuring unit module by fixing on a belt near the left iliac crest to collect accelerometer data for each activity. Our novel proposed model consists of feature calculation and infinite latent feature selection (ILFS) algorithm, auto labeling of activities, and application of machine learning classifiers for discrete and continuous time series data. Nine machine-learning classifiers were applied to detect falls prior to impact and derive final detection results by sorting the classifier. Our model showed the highest classification accuracy. Results for the proposed model that could classify as multiclass showed significantly higher average classification accuracy of 99.57 ± 0.01% for discrete data-based classifiers and 99.84 ± 0.02% for continuous time series-based classifiers than previous models (p < 0.01). In the future, multiclass pre-impact fall detection models can be applied to fall protector devices by detecting various activities for sending alerts or immediate feedback reactions to prevent falls.

Single Inertial Sensor-Based Neural Networks to Estimate COM-COP Inclination Angle During Walking.

A biomechanical understanding of gait stability is needed to reduce falling risk. As a typical parameter, the COM-COP (center of mass-center of pressure) inclination angle (IA) could provide valuable insight into postural control and balance recovery ability. In this study, an artificial neural network (ANN) model was developed to estimate COM-COP IA based on signals using an inertial sensor. Also, we evaluated how different types of ANN and the cutoff frequency of the low-pass filter applied to input signals could affect the accuracy of the model. An inertial measurement unit (IMU) including an accelerometer, gyroscope, and magnetometer sensors was fabricated as a prototype. The COM-COP IA was calculated using a 3D motion analysis system including force plates. In order to predict the COM-COP IA, a feed-forward ANN and long-short term memory (LSTM) network was developed. As a result, the feed-forward ANN showed a relative root-mean-square error (rRMSE) of 15% while the LSTM showed an improved accuracy of 9% rRMSE. Additionally, the LSTM displayed a stable accuracy regardless of the cutoff frequency of the filter applied to the input signals. This study showed that estimating the COM-COP IA was possible with a cheap inertial sensor system. Furthermore, the neural network models in this study can be implemented in systems to monitor the balancing ability of the elderly or patients with impaired balancing ability.

How to quantify the transition phase during golf swing performance: Torsional load affects low back complaints during the transition phase.

The transition phase of a golf swing is considered to be a decisive instant required for a powerful swing. However, at the same time, the low back torsional loads during this phase can have a considerable effect on golf-related low back pain (LBP). Previous efforts to quantify the transition phase were hampered by problems with accuracy due to methodological limitations. In this study, vector-coding technique (VCT) method was proposed as a comprehensive methodology to quantify the precise transition phase and examine low back torsional load. Towards this end, transition phases were assessed using three different methods (VCT, lead hand speed and X-factor stretch) and compared; then, low back torsional load during the transition phase was examined. As a result, the importance of accurate transition phase quantification has been documented. The largest torsional loads were observed in healthy professional golfers (10.23 ± 1.69 N · kg-1), followed by professional golfers with a history of LBP (7.93 ± 1.79 N · kg-1), healthy amateur golfers (1.79 ± 1.05 N · kg-1) and amateur golfers with a history of LBP (0.99 ± 0.87 N · kg-1), which order was equal to that of the transition phase magnitudes of each group. These results indicate the relationship between the transition phase and LBP history and the dependency of the torsional load magnitude on the transition phase.

Improved determination of dynamic balance using the centre of mass and centre of pressure inclination variables in a complete golf swing cycle.

Golf requires proper dynamic balance to accurately control the club head through a harmonious coordination of each human segment and joint. In this study, we evaluated the ability for dynamic balance during a golf swing by using the centre of mass (COM)-centre of pressure (COP) inclination variables. Twelve professional, 13 amateur and 10 novice golfers participated in this study. Six infrared cameras, two force platforms and SB-Clinic software were used to measure the net COM and COP trajectories. In order to evaluate dynamic balance ability, the COM-COP inclination angle, COM-COP inclination angular velocity and normalised COM-COP inclination angular jerk were used. Professional golfer group revealed a smaller COM-COP inclination angle and angular velocity than novice golfer group in the lead/trail direction (P < 0.01). In the normalised COM-COP inclination angular jerk, the professional golfer group showed a lower value than the other two groups in all directions. Professional golfers tend to exhibit improved dynamic balance, and this can be attributed to the neuromusculoskeletal system that maintains balance with proper postural control. This study has the potential to allow for an evaluation of the dynamic balance mechanism and will provide useful basic information for swing training and prevention of golf injuries.

Inter-joint coordination between hips and trunk during downswings: Effects on the clubhead speed.

Understanding of the inter-joint coordination between rotational movement of each hip and trunk in golf would provide basic knowledge regarding how the neuromuscular system organises the related joints to perform a successful swing motion. In this study, we evaluated the inter-joint coordination characteristics between rotational movement of the hips and trunk during golf downswings. Twenty-one right-handed male professional golfers were recruited for this study. Infrared cameras were installed to capture the swing motion. The axial rotation angle, angular velocity and inter-joint coordination were calculated by the Euler angle, numerical difference method and continuous relative phase, respectively. A more typical inter-joint coordination demonstrated in the leading hip/trunk than trailing hip/trunk. Three coordination characteristics of the leading hip/trunk reported a significant relationship with clubhead speed at impact (r < -0.5) in male professional golfers. The increased rotation difference between the leading hip and trunk in the overall downswing phase as well as the faster rotation of the leading hip compared to that of the trunk in the early downswing play important roles in increasing clubhead speed. These novel inter-joint coordination strategies have the great potential to use a biomechanical guideline to improve the golf swing performance of unskilled golfers.

Kinematic relationship between rotation of lumbar spine and hip joints during golf swing in professional golfers.

BACKGROUND: Understanding the kinematics of the lumbar spine and hip joints during a golf swing is a basic step for identifying swing-specific factors associated with low back pain. The objective of this study was to examine the kinematic relationship between rotational movement of the lumbar spine and hip joints during a golf swing. METHODS: Fifteen professional golfers participated in this study with employment of six infrared cameras to record their golf swings. Anatomical reference system of the upper torso, pelvis and thigh segments, and the location of each hip and knee joint were defined by the protocols of the kinematic model of previous studies. Lumbar spine and hip joint rotational angle was calculated utilizing the Euler angle method. Cross-correlation and angle-angle plot was used to examine the degree of kinematic relationship between joints. RESULTS: A fairly strong coupling relationship was shown between the lumbar spine and hip rotational movements with an average correlation of 0.81. Leading hip contribution to overall rotation was markedly high in the early stage of the downswing, while the lumbar spine contributed greater towards the end of the downswing; however, the relative contributions of the trailing hip and lumbar spine were nearly equal during the entire downswing. CONCLUSIONS: Most of the professional golfers participated in this study used a similar coordination strategy when moving their hips and lumbar spine during golf swings. The rotation of hips was observed to be more efficient in producing the overall rotation during the downswing when compared to the backswing. These results provide quantitative information to better understand the lumbar spine and hip joint kinematic characteristics of professional golfers. This study will have great potential to be used as a normal control data for the comparison with kinematic information among golfers with low back pain and for further investigation of golf swing-specific factors associated with injury.

Predicting Complete Ground Reaction Forces and Moments During Gait With Insole Plantar Pressure Information Using a Wavelet Neural Network.

In general, three-dimensional ground reaction forces (GRFs) and ground reaction moments (GRMs) that occur during human gait are measured using a force plate, which are expensive and have spatial limitations. Therefore, we proposed a prediction model for GRFs and GRMs, which only uses plantar pressure information measured from insole pressure sensors with a wavelet neural network (WNN) and principal component analysis-mutual information (PCA-MI). For this, the prediction model estimated GRFs and GRMs with three different gait speeds (slow, normal, and fast groups) and healthy/pathological gait patterns (healthy and adolescent idiopathic scoliosis (AIS) groups). Model performance was validated using correlation coefficients (r) and the normalized root mean square error (NRMSE%) and was compared to the prediction accuracy of the previous methods using the same dataset. As a result, the performance of the GRF and GRM prediction model proposed in this study (slow group: r = 0.840-0.989 and NRMSE% = 10.693-15.894%; normal group: r = 0.847-0.988 and NRMSE% = 10.920-19.216%; fast group: r = 0.823-0.953 and NRMSE% = 12.009-20.182%; healthy group: r = 0.836-0.976 and NRMSE% = 12.920-18.088%; and AIS group: r = 0.917-0.993 and NRMSE% = 7.914-15.671%) was better than that of the prediction models suggested in previous studies for every group and component (p < 0.05 or 0.01). The results indicated that the proposed model has improved performance compared to previous prediction models.

Quasi-stiffness of the knee joint in flexion and extension during the golf swing.

Biomechanical understanding of the knee joint during a golf swing is essential to improve performance and prevent injury. In this study, we quantified the flexion/extension angle and moment as the primary knee movement, and evaluated quasi-stiffness represented by moment-angle coupling in the knee joint. Eighteen skilled and 23 unskilled golfers participated in this study. Six infrared cameras and two force platforms were used to record a swing motion. The anatomical angle and moment were calculated from kinematic and kinetic models, and quasi-stiffness of the knee joint was determined as an instantaneous slope of moment-angle curves. The lead knee of the skilled group had decreased resistance duration compared with the unskilled group (P < 0.05), and the resistance duration of the lead knee was lower than that of the trail knee in the skilled group (P < 0.01). The lead knee of the skilled golfers had greater flexible excursion duration than the trail knee of the skilled golfers, and of both the lead and trail knees of the unskilled golfers. These results provide critical information for preventing knee injuries during a golf swing and developing rehabilitation strategies following surgery.

Kinematic evaluation of movement smoothness in golf: relationship between the normalized jerk cost of body joints and the clubhead.

BACKGROUND: When the human body is introduced to a new motion or movement, it learns the placement of different body parts, sequential muscle control, and coordination between muscles to achieve necessary positions, and it hones this new skill over time and repetition. Previous studies have demonstrated definite differences in the smoothness of body movements with different levels of training, i.e., amateurs compared with professionals. Therefore, we tested the hypothesis that skilled golfers swing a driver with a smoother motion than do unskilled golfers. In addition, the relationship between the smoothness of body joints and that of the clubhead was evaluated to provide further insight into the mechanism of smooth golf swing. METHODS: Two subject groups (skilled and unskilled) participated in the experiment. The skilled group comprised 20 male professional golfers registered with the Korea Professional Golf Association, and the unskilled group comprised 19 amateur golfers who enjoy golf as a hobby. Six infrared cameras (VICON460 system) were used to record the 3D trajectories of markers attached to the clubhead and body segments, and the resulting data was evaluated with kinematic analysis. A physical quantity called jerk was calculated to investigate differences in smoothness during downswing between the two study groups. RESULTS: The hypothesis that skilled golfers swing a driver with a smoother motion than do unskilled golfers was supported. The normalized jerk of the clubhead of skilled golfers was lower than that of unskilled golfers in the anterior/posterior, medial/lateral, and proximal/distal directions. Most human joints, especially in the lower body, had statistically significant lower normalized jerk values in the skilled group. In addition, the normalized jerk of the skilled group's lower body joints had a distinct positive correlation with the normalized jerk of the clubhead with r = 0.657 (p < 0.01). CONCLUSIONS: The result of this study showed that skilled golfers have smoother swings than unskilled golfers during the downswing and revealed that the smoothness of a clubhead trajectory is related more to the smoothness of the lower body joints than that of the upper body joints. These findings can be used to understand the mechanisms behind smooth golf swings and, eventually, to improve golf performance.

Three-dimensional echocardiography-based prediction of posterior leaflet resection.

Clinical long-term outcomes have shown that partial leaflet resection followed by ring annuloplasty is a reliable and reproducible surgical repair technique for treatment of mitral valve (MV) leaflet prolapse. We report a 61-year-old male for three-dimensional transesophageal echocardiography (3DTEE)-based virtual posterior leaflet resection and ring annuloplasty. Severe mitral regurgitation was found and computational evaluation demonstrated substantial leaflet malcoaptation and high stress concentration. Following virtual resection and ring annuloplasty, posterior leaflet prolapse markedly decreased, sufficient leaflet coaptation was restored, and high stress concentration disappeared. Virtual MV repair strategies using 3DTEE have the potential to help optimize MV repair.

Microbiome and Prostate Cancer: Emerging Diagnostic and Therapeutic Opportunities.

This review systematically addresses the correlation between the microbiome and prostate cancer and explores its diagnostic and therapeutic implications. Recent research has indicated an association between the urinary and gut microbiome composition and prostate cancer incidence and progression. Specifically, the urinary microbiome is a potential non-invasive biomarker for early detection and risk evaluation, with altered microbial profiles in prostate cancer patients. This represents an advancement in non-invasive diagnostic approaches to prostate cancer. The role of the gut microbiome in the efficacy of various cancer therapies has recently gained attention. Gut microbiota variations can affect the metabolism and effectiveness of standard treatment modalities, including chemotherapy, immunotherapy, and hormone therapy. This review explores the potential of gut microbiome modification through dietary interventions, prebiotics, probiotics, and fecal microbiota transplantation for improving the treatment response and mitigating adverse effects. Moreover, this review discusses the potential of microbiome profiling for patient stratification and personalized treatment strategies. While the current research identifies the pivotal role of the microbiome in prostate cancer, it also highlights the necessity for further investigations to fully understand these complex interactions and their practical applications in improving patient outcomes in prostate cancer management.

Deep-Learning-Based Recovery of Missing Optical Marker Trajectories in 3D Motion Capture Systems.

Motion capture (MoCap) technology, essential for biomechanics and motion analysis, faces challenges from data loss due to occlusions and technical issues. Traditional recovery methods, based on inter-marker relationships or independent marker treatment, have limitations. This study introduces a novel U-net-inspired bi-directional long short-term memory (U-Bi-LSTM) autoencoder-based technique for recovering missing MoCap data across multi-camera setups. Leveraging multi-camera and triangulated 3D data, this method employs a sophisticated U-shaped deep learning structure with an adaptive Huber regression layer, enhancing outlier robustness and minimizing reconstruction errors, proving particularly beneficial for long-term data loss scenarios. Our approach surpasses traditional piecewise cubic spline and state-of-the-art sparse low rank methods, demonstrating statistically significant improvements in reconstruction error across various gap lengths and numbers. This research not only advances the technical capabilities of MoCap systems but also enriches the analytical tools available for biomechanical research, offering new possibilities for enhancing athletic performance, optimizing rehabilitation protocols, and developing personalized treatment plans based on precise biomechanical data.

Deep Learning Model to Evaluate Sensorimotor System Ability in Patients With Dizziness for Postural Control.

Balanced posture without dizziness is achieved via harmonious coordination of visual, vestibular, and somatosensory systems. Specific frequency bands of center of pressure (COP) signals during quiet standing are closely related to the sensory inputs of the sensorimotor system. In this study, we proposed a deep learning-based novel protocol using the COP signal frequencies to estimate the equilibrium score (ES), a sensory system contribution. Sensory organization test was performed with normal controls (n=125), patients with Meniere's disease (n=72) and vestibular neuritis (n=105). The COP signals preprocessed via filtering, detrending and augmenting during quiet standing were converted to frequency domains utilizing Short-time Fourier Transform. Four different types of CNN backbone including GoogleNet, ResNet-18, SqueezeNet, and VGG16 were trained and tested using the frequency transformed data of COP and the ES under conditions #2 to #6. Additionally, the 100 original output classes (1 to 100 ESs) were encoded into 50, 20, 10 and 5 sub-classes to improve the performance of the prediction model. Absolute difference between the measured and predicted ES was about 1.7 (ResNet-18 with encoding of 20 sub-classes). The average error of each sensory analysis calculated using the measured ES and predicted ES was approximately 1.0%. The results suggest that the sensory system contribution of patients with dizziness can be quantitatively assessed using only the COP signal from a single test of standing posture. This study has potential to reduce balance testing time (spent on six conditions with three trials each in sensory organization test) and the size of computerized dynamic posturography (movable visual surround and force plate), and helps achieve the widespread application of the balance assessment.

Upper torso and pelvis linear velocity during the downswing of elite golfers.

BACKGROUND: During a golf swing, analysis of the movement in upper torso and pelvis is a key step to determine a motion control strategy for accurate and consistent shots. However, a majority of previous studies that have evaluated this movement limited their analysis only to the rotational movement of segments, and translational motions were not examined. Therefore, in this study, correlations between translational motions in the 3 axes, which occur between the upper torso and pelvis, were also examined. METHODS: The experiments were carried out with 14 male pro-golfers (age: 29 ± 8 years, career: 8.2 ± 4.8years) who registered in the Korea Professional Golf Association (KPGA). Six infrared cameras (VICON; Oxford Metrics, Oxford, UK) and SB-Clinc software (SWINGBANK Ltd, Korea) were used to collect optical marker trajectories. The center of mass (CoM) of each segment was calculated based on kinematic principal. In addition, peak value of CoM velocity and the time that each peak occurred in each segment during downswing was calculated. Also, using cross-correlation analysis, the degree of coupling and time lags of peak values occurred between and within segments (pelvis and upper torso) were investigated. RESULTS: As a result, a high coupling strength between upper torso and pelvis with an average correlation coefficient = 0.86 was observed, and the coupling between segments was higher than that within segments (correlation coefficient = 0.81 and 0.77, respectively). CONCLUSIONS: Such a high coupling at the upper torso and pelvis can be used to reduce the degree of motion control in the central nervous system and maintain consistent patterns in the movement. The result of this study provides important information for the development of optimal golf swing movement control strategies in the future.

HER2 positivity predicts BCG unresponsiveness and adaptive immune cell exhaustion in EORTC risk-stratified cohort of bladder cancer.

Introduction: Predicting the response to Bacillus Calmette-Guérin (BCG) therapy in high-risk patients with non-muscle invasive bladder cancer (NMIBC) is crucial, as failure may necessitate interventions, such as radical cystectomy or salvage therapy. With the recent classification of genetic class 2a (which has HER2 protein abundance as its signature mutation of ERBB2), evaluating its prognostic role and relationship with BCG response could yield important results. Methods: This retrospective study included 160 patients with NMIBC who underwent transurethral resection of bladder tumors at Gangneung Asan Hospital between 2000 and 2013 and were stratified based on the European Organization for Research and Treatment of Cancer (EORTC) risk criteria. In addition, we analyzed a subset of 67 patients who had received BCG induction therapy to identify factors predictive of BCG treatment response. Univariate and multivariate analyses were used to assess the impact of clinicopathological factors, HER2 positivity, and EORTC risk on recurrence, progression, survival, and BCG response. Each variable's prognostic significance was determined using the Kaplan-Meier analysis. The tumor microenvironments (TMEs) were evaluated in relation to HER2 and EORTC risk. Results: Patients with HER2+ had a higher median age, a greater prevalence of high-grade tumors, and more frequent recurrences. The univariate analysis demonstrated that the HER2+, intermediate (vs. low-risk) high (vs. low-risk), and EORTC recurrence risk groups were significantly associated with recurrence. In patients treated with BCG, only the HER2+ status predicted recurrence. In the univariate analysis for progression, age, high EORTC progression risk (vs. low-to-intermediate), HER2+, and programmed death-ligand 1 positive (PD-L1+) were significant factors. In multivariate analyses for progression, age, high EORTC progression risk, and PD-L1+ were significant factors for progression. HER2 expression was associated with the TME, influencing the proportion of PD-L1+ cells, as well as other markers of PD-1, CD8, and Ki67. Conclusion: The HER2+ status may be related to genetic characteristics that appear more frequently in older age, which suggests a potential for predicting the recurrence and response to BCG treatment. Additionally, analyzing TME trends of aggressive adaptive immune response characterized by HER2 expression provides insight into recurrence and BCG response mechanisms.

A Novel Optimization-Based Convolution Neural Network to Estimate the Contribution of Sensory Inputs to Postural Stability During Quiet Standing.

Adequate postural control is maintained by integrating signals from the visual, somatosensory, and vestibular systems. The purpose of this study is to propose a novel convolutional neural network (CNN)-based protocol that can evaluate the contributions of each sensory input for postural stability (calculated a sensory analysis index) using center of pressure (COP) signals in a quiet standing posture. Raw COP signals in the anterior/posterior and medial/lateral directions were extracted from 330 patients in a quiet standing with their eyes open for 20 seconds. The COP signals augmented using jittering and pooling techniques were transformed into the frequency domain. The sensory analysis indices were used as the output information from the deep learning models. A ResNet-50 CNN was combined with the k-nearest neighbor, random forest, and support vector machine classifiers for the training model. Additionally, a novel optimization process was proposed to include an encoding design variable that can group outputs into sub-classes along with hyperparameters. The results of optimization considering only hyperparameters showed low performance, with an accuracy of 55% or less and F-1 scores of 54% or less in all models. However, when optimization was performed using the encoding design variable, the performance was markedly increased in the CNN-classifier combined models (r = 0.975). These results suggest it is possible to evaluate the contribution of sensory inputs for postural stability using COP signals during a quiet standing. This study will facilitate the expanded dissemination of a system that can quantitatively evaluate the balance ability and rehabilitation progress of patients with dizziness.