[Perioperative management of laparoscopic cholecystectomy in a patient with paroxysmal nocturnal haemoglobinuria undergoing ravulizumab treatment].

Paroxysmal nocturnal hemoglobinuria (PNH) is characterized by hemolysis, thrombosis, and bone marrow failure. Infection, pregnancy, and surgical operation have the potential to evoke severe episodes of hemolysis and thrombosis. Therefore, the use of an antibody agent against complement component 5 (C5), eculizumab, one day before the operation is recommended. Ravulizumab is a newly approved long-acting antibody agent against C5. Thus, little is known about perioperative management with ravulizumab. We experienced a 43-year-old female patient who safely underwent laparoscopic cholecystectomy under ravulizumab treatment for PNH. Ravulizumab was administered one day before the operation. Laparoscopic cholecystectomy for cholelithiasis was performed under intravenous anesthesia, intermittent air compression of the lower extremities, and low pneumoperitoneum pressure. Additionally, heparin was administered, and the patient left the sickbed early without significant postoperative complications. Like eculizumab, complement inhibition by ravulizumab is also considered effective in the perioperative management of patients with PNH. However, close cooperation with surgeons and anesthesiologists and careful management based on clinical symptoms and laboratory data such as LDH, CH50, and D-dimer are essential.

Filtering with State-Observation Examples via Kernel Monte Carlo Filter.

This letter addresses the problem of filtering with a state-space model. Standard approaches for filtering assume that a probabilistic model for observations (i.e., the observation model) is given explicitly or at least parametrically. We consider a setting where this assumption is not satisfied; we assume that the knowledge of the observation model is provided only by examples of state-observation pairs. This setting is important and appears when state variables are defined as quantities that are very different from the observations. We propose kernel Monte Carlo filter, a novel filtering method that is focused on this setting. Our approach is based on the framework of kernel mean embeddings, which enables nonparametric posterior inference using the state-observation examples. The proposed method represents state distributions as weighted samples, propagates these samples by sampling, estimates the state posteriors by kernel Bayes' rule, and resamples by kernel herding. In particular, the sampling and resampling procedures are novel in being expressed using kernel mean embeddings, so we theoretically analyze their behaviors. We reveal the following properties, which are similar to those of corresponding procedures in particle methods: the performance of sampling can degrade if the effective sample size of a weighted sample is small, and resampling improves the sampling performance by increasing the effective sample size. We first demonstrate these theoretical findings by synthetic experiments. Then we show the effectiveness of the proposed filter by artificial and real data experiments, which include vision-based mobile robot localization.

Rib region detection for scanning path planning for fully automated robotic abdominal ultrasonography.

PURPOSE: Scanning path planning is an essential technology for fully automated ultrasound (US) robotics. During biliary scanning, the subcostal boundary is critical body surface landmarks for scanning path planning but are often invisible, depending on the individual. This study developed a method of estimating the rib region for scanning path planning toward fully automated robotic US systems. METHODS: We proposed a method for determining the rib region using RGB-D images and respiratory variation. We hypothesized that detecting the rib region would be possible based on changes in body surface position due to breathing. We generated a depth difference image by finding the difference between the depth image taken at the resting inspiratory position and the depth image taken at the maximum inspiratory position, which clearly shows the rib region. The boundary position of the subcostal was then determined by applying training using the YOLOv5 object detection model to this depth difference image. RESULTS: In the experiments with healthy subjects, the proposed method of rib detection using the depth difference image marked an intersection over union (IoU) of 0.951 and average confidence of 0.77. The average error between the ground truth and predicted positions was 16.5 mm in 3D space. The results were superior to rib detection using only the RGB image. CONCLUSION: The proposed depth difference imaging method, which measures respiratory variation, was able to accurately estimate the rib region without contact and physician intervention. It will be useful for planning the scan path during the biliary imaging.

A case of conservatively managed gastric perforation at a recurrent hiatal hernia site after laparoscopic paraesophageal repair.

INTRODUCTION AND IMPORTANCE: Gastric perforation due to a hiatal hernia is a rare cause of acute abdominal pain that often requires surgical intervention. Conservative management for this condition is an effective option in certain cases, although fewer reports of this exist. Herein, we report a unique case of gastric perforation caused by a recurrent hiatal hernia that was successfully treated with conservative management. CASE PRESENTATION: A 74-year-old man developed a high fever and an elevated inflammatory response on the third day after a laparoscopic paraesophageal hernia repair using a mesh. Computed tomography confirmed the recurrence of the hiatal hernia, with gastric fundal prolapse into the mediastinum and surgical emphysema in the gastric wall. This was followed by a gastric perforation within the mediastinum. The patient was treated using an ileus tube through the perforation site. CLINICAL DISCUSSION: In similar cases, if the clinical symptoms are mild, there are no signs of serious infection, and the perforation remains in the mediastinum and can be appropriately drained, conservative treatment is considered an option. CONCLUSION: Under favorable conditions, conservative management can be an option for gastric perforation in patients with recurrent hiatal hernias, which is a serious potential postoperative complication.

Study on method of organ section retention and tracking through deep learning in automated diagnostic and therapeutic robotics.

PURPOSE: In high-intensity focused ultrasound (HIFU) treatment of the kidney and liver, tracking the organs is essential because respiratory motions make continuous cauterization of the affected area difficult and may cause damage to other parts of the body. In this study, we propose a tracking system for rotational scanning, and propose and evaluate a method for estimating the angles of organs in ultrasound images. METHODS: We proposed AEMA, AEMAD, and AEMAD++ as methods for estimating the angles of organs in ultrasound images, using RUDS and a phantom to acquire 90-degree images of a kidney from the long-axis image to the short-axis image as a data set. Six datasets were used, with five for preliminary preparation and one for testing, while the initial position was shifted by 2 mm in the contralateral axis direction. The test data set was evaluated by estimating the angle using each method. RESULTS: The accuracy and processing speed of angle estimation for AEMA, AEMAD, and AEMAD++ were 23.8% and 0.33 FPS for AEMAD, 32.0% and 0.56 FPS for AEMAD, and 29.5% and 3.20 FPS for AEMAD++, with tolerance of ± 2.5 degrees. AEMAD++ offered the best speed and accuracy. CONCLUSION: In the phantom experiment, AEMAD++ showed the effectiveness of tracking the long-axis image of the kidney in rotational scanning. In the future, we will add either the area of surrounding organs or the internal structure of the kidney as a new feature to validate the results.

A VS ultrasound diagnostic system with kidney image evaluation functions.

PURPOSE: An inevitable feature of ultrasound-based diagnoses is that the quality of the ultrasound images produced depends directly on the skill of the physician operating the probe. This is because physicians have to constantly adjust the probe position to obtain a cross section of the target organ, which is constantly shifting due to patient respiratory motions. Therefore, we developed an ultrasound diagnostic robot that works in cooperation with a visual servo system based on deep learning that will help alleviate the burdens imposed on physicians. METHODS: Our newly developed robotic ultrasound diagnostic system consists of three robots: an organ tracking robot (OTR), a robotic bed, and a robotic supporting arm. Additionally, we used different image processing methods (YOLOv5s and BiSeNet V2) to detect the target kidney location, as well as to evaluate the appropriateness of the obtained ultrasound images (ResNet 50). Ultimately, the image processing results are transmitted to the OTR for use as motion commands. RESULTS: In our experiments, the highest effective tracking rate (0.749) was obtained by YOLOv5s with Kalman filtering, while the effective tracking rate was improved by about 37% in comparison with cases without such filtering. Additionally, the appropriateness probability of the ultrasound images obtained during the tracking process was also the highest and most stable. The second highest tracking efficiency value (0.694) was obtained by BiSeNet V2 with Kalman filtering and was a 75% improvement over the case without such filtering. CONCLUSION: While the most efficient tracking achieved is based on the combination of YOLOv5s and Kalman filtering, the combination of BiSeNet V2 and Kalman filtering was capable of detecting the kidney center of gravity closer to the kidney's actual motion state. Furthermore, this model could also measure the cross-sectional area, maximum diameter, and other detailed information of the target kidney, which meant it is more practical for use in actual diagnoses.

Artificial intelligence for distinguishment of hammering sound in total hip arthroplasty.

Recent studies have focused on hammering sound analysis during insertion of the cementless stem to decrease complications in total hip arthroplasty. However, the nature of the hammering sound is complex to analyse and varies widely owing to numerous possible variables. Therefore, we performed a preliminary feasibility study that aimed to clarify the accuracy of a prediction model using a machine learning algorithm to identify the final rasping hammering sound recorded during surgery. The hammering sound data of 29 primary THA without complication were assessed. The following definitions were adopted. Undersized rasping: all undersized stem rasping before the rasping of the final stem size, Final size rasping: rasping of the final stem size, Positive example: hammering sound during final size rasping, Negative example A: hammering sound during minimum size stem rasping, Negative example B: hammering sound during all undersized rasping. Three datasets for binary classification were set. Finally, binary classification was analysed in six models for the three datasets. The median values of the ROC-AUC in models A-F among each dataset were dataset a: 0.79, 0.76, 0.83, 0.90, 0.91, and 0.90, dataset B: 0.61, 0.53, 0.67, 0.69, 0.71, and 0.72, dataset C: 0.60, 0.48, 0.57, 0.63, 0.67, and 0.63, respectively. Our study demonstrated that artificial intelligence using machine learning was able to distinguish the final rasping hammering sound from the previous hammering sound with a relatively high degree of accuracy. Future studies are warranted to establish a prediction model using hammering sound analysis with machine learning to prevent complications in THA.

A novel complementation method of an acoustic shadow region utilizing a convolutional neural network for ultrasound-guided therapy.

PURPOSE: Noise-free ultrasound images are essential for organ monitoring during regional ultrasound-guided therapy. When the affected area is located under the ribs, however, acoustic shadow is caused by the reflection of sound from hard tissues such as bone, and the image is output with missing information in this region. Therefore, in the present study, we attempt to complement the image in the missing area. METHODS: The overall flow of the complementation method to generate a shadow-free composite image is as follows. First, we constructed a binary classification method for the presence or absence of acoustic shadow on a phantom kidney based on a convolutional neural network. Second, we created a composite shadow-free image by searching for a suitable image from a time-series database and superimposing the corresponding area without shadow onto the missing area of the target image. In addition, we constructed and verified an automatic kidney mask generation method utilizing U-Net. RESULTS: The complementation accuracy for kidney tracking could be enhanced by template matching. Zero-mean normalized cross-correlation (ZNCC) values after complementation were higher than that of before complementation under four different data generation conditions: (i) changing the position of the bed of the robotic ultrasound diagnostic system in the translational direction, (ii) changing the probe angle in the translational direction, (iii) with the addition of rotational motion of the probe to condition (ii). Although there was large variation in the shape of the kidney contour in condition (iii), the proposed method improved the ZNCC value from 0.5437 to 0.5807. CONCLUSIONS: The effectiveness of the proposed method was demonstrated in phantom experiments. Verification of its effectiveness in real organs is necessary in future study.

Evaluation of ultrasonic fibrosis diagnostic system using convolutional network for ordinal regression.

PURPOSE: Diagnosis of liver fibrosis is important for establishing treatment and assessing the risk of carcinogenesis. Ultrasound imaging is an excellent diagnostic method as a screening test in terms of non-invasiveness and simplicity. The purpose of this study was to automatically diagnose liver fibrosis using ultrasound images to reduce the burden on physicians. METHODS: We proposed and implemented a system for extracting regions of liver parenchyma utilizing U-Net. Using regions of interest, the stage of fibrosis was classified as F0, F1, F2, F3, or F4 utilizing CORALNet, an ordinal regression model based on ResNet18. The effectiveness of the proposed system was verified. RESULTS: The system implemented using U-Net had a maximum mean Dice coefficient of 0.929. The results of classification of liver fibrosis utilizing CORALNet had a mean absolute error (MAE) of 1.22 and root mean square error (RMSE) of 1.60. The per-case results had a MAE of 1.55 and RMSE of 1.34. CONCLUSION: U-Net extracted regions of liver parenchyma from the images with high accuracy, and CORALNet showed effectiveness using ordinal information to classify fibrosis in the images. As a future task, we will study a model that is less dependent on teaching data.

Estimating 3-dimensional liver motion using deep learning and 2-dimensional ultrasound images.

PURPOSE: The main purpose of this study is to construct a system to track the tumor position during radiofrequency ablation (RFA) treatment. Existing tumor tracking systems are designed to track a tumor in a two-dimensional (2D) ultrasound (US) image. As a result, the three-dimensional (3D) motion of the organs cannot be accommodated and the ablation area may be lost. In this study, we propose a method for estimating the 3D movement of the liver as a preliminary system for tumor tracking. Additionally, in current 3D movement estimation systems, the motion of different structures during RFA could reduce the tumor visibility in US images. Therefore, we also aim to improve the estimation of the 3D movement of the liver by improving the liver segmentation. We propose a novel approach to estimate the relative 6-axial movement (x, y, z, roll, pitch, and yaw) between the liver and the US probe in order to estimate the overall movement of the liver. METHOD: We used a convolutional neural network (CNN) to estimate the 3D displacement from two-dimensional US images. In addition, to improve the accuracy of the estimation, we introduced a segmentation map of the liver region as the input for the regression network. Specifically, we improved the extraction accuracy of the liver region by using a bi-directional convolutional LSTM U-Net with densely connected convolutions (BCDU-Net). RESULTS: By using BCDU-Net, the accuracy of the segmentation was dramatically improved, and as a result, the accuracy of the movement estimation was also improved. The mean absolute error for the out-of-plane direction was 0.0645 mm/frame. CONCLUSION: The experimental results show the effectiveness of our novel method to identify the movement of the liver by BCDU-Net and CNN. Precise segmentation of the liver by BCDU-Net also contributes to enhancing the performance of the liver movement estimation.

Prediction of future gastric cancer risk using a machine learning algorithm and comprehensive medical check-up data: A case-control study.

A comprehensive screening method using machine learning and many factors (biological characteristics, Helicobacter pylori infection status, endoscopic findings and blood test results), accumulated daily as data in hospitals, could improve the accuracy of screening to classify patients at high or low risk of developing gastric cancer. We used XGBoost, a classification method known for achieving numerous winning solutions in data analysis competitions, to capture nonlinear relations among many input variables and outcomes using the boosting approach to machine learning. Longitudinal and comprehensive medical check-up data were collected from 25,942 participants who underwent multiple endoscopies from 2006 to 2017 at a single facility in Japan. The participants were classified into a case group (y = 1) or a control group (y = 0) if gastric cancer was or was not detected, respectively, during a 122-month period. Among 1,431 total participants (89 cases and 1,342 controls), 1,144 (80%) were randomly selected for use in training 10 classification models; the remaining 287 (20%) were used to evaluate the models. The results showed that XGBoost outperformed logistic regression and showed the highest area under the curve value (0.899). Accumulating more data in the facility and performing further analyses including other input variables may help expand the clinical utility.

Prediction of Glucose Metabolism Disorder Risk Using a Machine Learning Algorithm: Pilot Study.

BACKGROUND: A 75-g oral glucose tolerance test (OGTT) provides important information about glucose metabolism, although the test is expensive and invasive. Complete OGTT information, such as 1-hour and 2-hour postloading plasma glucose and immunoreactive insulin levels, may be useful for predicting the future risk of diabetes or glucose metabolism disorders (GMD), which includes both diabetes and prediabetes. OBJECTIVE: We trained several classification models for predicting the risk of developing diabetes or GMD using data from thousands of OGTTs and a machine learning technique (XGBoost). The receiver operating characteristic (ROC) curves and their area under the curve (AUC) values for the trained classification models are reported, along with the sensitivity and specificity determined by the cutoff values of the Youden index. We compared the performance of the machine learning techniques with logistic regressions (LR), which are traditionally used in medical research studies. METHODS: Data were collected from subjects who underwent multiple OGTTs during comprehensive check-up medical examinations conducted at a single facility in Tokyo, Japan, from May 2006 to April 2017. For each examination, a subject was diagnosed with diabetes or prediabetes according to the American Diabetes Association guidelines. Given the data, 2 studies were conducted: predicting the risk of developing diabetes (study 1) or GMD (study 2). For each study, to apply supervised machine learning methods, the required label data was prepared. If a subject was diagnosed with diabetes or GMD at least once during the period, then that subject's data obtained in previous trials were classified into the risk group (y=1). After data processing, 13,581 and 6760 OGTTs were analyzed for study 1 and study 2, respectively. For each study, a randomly chosen subset representing 80% of the data was used for training 9 classification models and the remaining 20% was used for evaluating the models. Three classification models, A to C, used XGBoost with various input variables, some including OGTT data. The other 6 classification models, D to I, used LR for comparison. RESULTS: For study 1, the AUC values ranged from 0.78 to 0.93. For study 2, the AUC values ranged from 0.63 to 0.78. The machine learning approach using XGBoost showed better performance compared with traditional LR methods. The AUC values increased when the full OGTT variables were included. In our analysis using a particular setting of input variables, XGBoost showed that the OGTT variables were more important than fasting plasma glucose or glycated hemoglobin. CONCLUSIONS: A machine learning approach, XGBoost, showed better prediction accuracy compared with LR, suggesting that advanced machine learning methods are useful for detecting the early signs of diabetes or GMD. The prediction accuracy increased when all OGTT variables were added. This indicates that complete OGTT information is important for predicting the future risk of diabetes and GMD accurately.

Early diagnosis of hepatic intravascular lymphoma: a case report and literature review.

Hepatic intravascular large B-cell lymphoma (IVL) is a rare disease entity that involves invasion into various organs. Due to the aggressive behavior and poor prognosis of the disease and the difficulty in making an early diagnosis, some cases are diagnosed at autopsy. Early suspicion and the use of imaging studies and liver biopsies are key for diagnosing IVL; however, no reports have described the results of imaging studies due to the limited number of cases. We herein report the results of imaging studies of hepatic IVL, including the findings PET-CT, dynamic-CT, EOB-MRI and CEUS. These results may help physicians to make an early diagnosis and improve the prognosis.

Accuracy of Loopy belief propagation in Gaussian models.

This paper considers the loopy belief propagation (LBP) algorithm applied to Gaussian graphical models. It is known for Gaussian belief propagation that, if LBP converges, LBP computes the exact posterior means but incorrect variances. In this paper, we analytically derive the posterior variances for some special structured graphs and clarify the accuracy of LBP. For the graphs of a single cycle, we derive a rigorous solution for the posterior variances and thereby find the quantity that determines the accuracy of LBP. Based on this result, we state a necessary condition for LBP convergence. The quantity above also plays an important role in graphs of a single cycle with arbitrary trees. For arbitrary topological graphs, we consider the situation where correlations between any pair of nodes are comparatively small and show analytically the principal values that determine the accuracy of LBP.