Echocardiographic artificial intelligence for pulmonary hypertension classification.

OBJECTIVE: The classification of pulmonary hypertension (PH) is crucial for determining the appropriate therapeutic strategy. We investigated whether machine learning (ML) algorithms may assist in echocardiographic PH prediction, where current guidelines recommend integrating several different parameters. METHODS: We obtained physical and echocardiographic data from 885 patients who underwent right heart catheterisation (RHC). Patients were classified into three groups: non-PH, precapillary PH and postcapillary PH, based on values obtained from RHC. Using 24 parameters, we created predictive models employing four different classifiers and selected the one with the highest area under the curve. We then calculated the macro-average classification accuracy for PH on the derivation cohort (n=720) and prospective validation data set (n=165), comparing the results with guideline-based echocardiographic assessment obtained from each cohort. RESULTS: Logistic regression with elastic net regularisation had the highest classification accuracy, with areas under the curves of 0.789, 0.766 and 0.742 for normal, precapillary PH and postcapillary PH, respectively. The ML model demonstrated significantly better predictive accuracy than the guideline-based echocardiographic assessment in the derivation cohort (59.4% vs 51.6%, p<0.01). In the independent validation data set, the ML model's accuracy was comparable to the guideline-based PH classification (59.4% vs 57.8%, p=0.638). CONCLUSIONS: This preliminary study suggests promising potential for our ML model in predicting echocardiographic PH. Further research and validation are needed to fully assess its clinical utility in PH diagnosis and treatment decision-making.

Unsupervised cluster analysis reveals different phenotypes in patients after transcatheter aortic valve replacement.

Aims: The aim of this study was to identify phenotypes with potential prognostic significance in aortic stenosis (AS) patients after transcatheter aortic valve replacement (TAVR) through a clustering approach. Methods and results: This multi-centre retrospective study included 1365 patients with severe AS who underwent TAVR between January 2015 and March 2019. Among demographics, laboratory, and echocardiography parameters, 20 variables were selected through dimension reduction and used for unsupervised clustering. Phenotypes and outcomes were compared between clusters. Patients were randomly divided into a derivation cohort (n = 1092: 80%) and a validation cohort (n = 273: 20%). Three clusters with markedly different features were identified. Cluster 1 was associated predominantly with elderly age, a high aortic valve gradient, and left ventricular (LV) hypertrophy; Cluster 2 consisted of preserved LV ejection fraction, larger aortic valve area, and high blood pressure; and Cluster 3 demonstrated tachycardia and low flow/low gradient AS. Adverse outcomes differed significantly among clusters during a median of 2.2 years of follow-up (P < 0.001). After adjustment for clinical and echocardiographic data in a Cox proportional hazards model, Cluster 3 (hazard ratio, 4.18; 95% confidence interval, 1.76-9.94; P = 0.001) was associated with increased risk of adverse outcomes. In sequential Cox models, a model based on clinical data and echocardiographic variables (χ2 = 18.4) was improved by Cluster 3 (χ2 = 31.5; P = 0.001) in the validation cohort. Conclusion: Unsupervised cluster analysis of patients after TAVR revealed three different groups for assessment of prognosis. This provides a new perspective in the categorization of patients after TAVR that considers comorbidities and extravalvular cardiac dysfunction.

Deep learning approach for analyzing chest x-rays to predict cardiac events in heart failure.

Background: A deep learning (DL) model based on a chest x-ray was reported to predict elevated pulmonary artery wedge pressure (PAWP) as heart failure (HF). Objectives: The aim of this study was to (1) investigate the role of probability of elevated PAWP for the prediction of clinical outcomes in association with other parameters, and (2) to evaluate whether probability of elevated PAWP based on DL added prognostic information to other conventional clinical prognostic factors in HF. Methods: We evaluated 192 patients hospitalized with HF. We used a previously developed AI model to predict HF and calculated probability of elevated PAWP. Readmission following HF and cardiac mortality were the primary endpoints. Results: Probability of elevated PAWP was associated with diastolic function by echocardiography. During a median follow-up period of 58 months, 57 individuals either died or were readmitted. Probability of elevated PAWP appeared to be associated with worse clinical outcomes. After adjustment for readmission score and laboratory data in a Cox proportional-hazards model, probability of elevated PAWP at pre-discharge was associated with event free survival, independent of elevated left atrial pressure (LAP) based on echocardiographic guidelines (p < 0.001). In sequential Cox models, a model based on clinical data was improved by elevated LAP (p = 0.005), and increased further by probability of elevated PAWP (p < 0.001). In contrast, the addition of pulmonary congestion interpreted by a doctor did not statistically improve the ability of a model containing clinical variables (compared p = 0.086). Conclusions: This study showed the potential of using a DL model on a chest x-ray to predict PAWP and its ability to add prognostic information to other conventional clinical prognostic factors in HF. The results may help to enhance the accuracy of prediction models used to evaluate the risk of clinical outcomes in HF, potentially resulting in more informed clinical decision-making and better care for patients.

Body Surface Radiation Exposure in Interventional Echocardiographers During Structural Heart Disease Procedures.

Background: The distribution of radiation exposure on the body surface of interventional echocardiographers during structural heart disease (SHD) procedures is unclear. Objectives: This study estimated and visualized radiation exposure on the body surface of interventional echocardiographers performing transesophageal echocardiography by computer simulations and real-life measurements of radiation exposure during SHD procedures. Methods: A Monte Carlo simulation was performed to clarify the absorbed dose distribution of radiation on the body surface of interventional echocardiographers. The real-life radiation exposure was measured during 79 consecutive procedures (44 transcatheter edge-to-edge repairs of the mitral valve and 35 transcatheter aortic valve replacements [TAVRs]). Results: The simulation demonstrated high-dose exposure areas (>20 µGy/h) in the right half of the body, especially the waist and lower body, in all fluoroscopic directions caused by scattered radiation from the bottom edge of the patient bed. High-dose exposure occurred when obtaining posterior-anterior and cusp-overlap views. The real-life exposure measurements were consistent with the simulation estimates: interventional echocardiographers were more exposed to radiation at their waist in transcatheter edge-to-edge repair than in TAVR procedures (median 0.334 µSv/mGy vs 0.053 µSv/mGy; P < 0.001) and in TAVR with self-expanding valves than in those with balloon-expandable valves (median 0.067 µSv/mGy vs 0.039 µSv/mGy; P < 0.01) when the posterior-anterior or the right anterior oblique angle fluoroscopic directions were used. Conclusions: During SHD procedures, the right waist and lower body of interventional echocardiographers were exposed to high radiation doses. Exposure dose varied between different C-arm projections. Interventional echocardiographers, especially young women, should be educated regarding radiation exposure during these procedures. (The development of radiation protection shield for catheter-based treatment of structural heart disease [for echocardiologists and anesthesiologists]; UMIN000046478).

Classification of chest X-ray images by incorporation of medical domain knowledge into operation branch networks.

BACKGROUND: This study was conducted to alleviate a common difficulty in chest X-ray image diagnosis: The attention region in a convolutional neural network (CNN) does not often match the doctor's point of focus. The method presented herein, which guides the area of attention in CNN to a medically plausible region, can thereby improve diagnostic capabilities. METHODS: The model is based on an attention branch network, which has excellent interpretability of the classification model. This model has an additional new operation branch that guides the attention region to the lung field and heart in chest X-ray images. We also used three chest X-ray image datasets (Teikyo, Tokushima, and ChestX-ray14) to evaluate the CNN attention area of interest in these fields. Additionally, after devising a quantitative method of evaluating improvement of a CNN's region of interest, we applied it to evaluation of the proposed model. RESULTS: Operation branch networks maintain or improve the area under the curve to a greater degree than conventional CNNs do. Furthermore, the network better emphasizes reasonable anatomical parts in chest X-ray images. CONCLUSIONS: The proposed network better emphasizes the reasonable anatomical parts in chest X-ray images. This method can enhance capabilities for image interpretation based on judgment.

New perforated radiation shield for anesthesiologists: Monte Carlo simulation of effects.

Catheterization for structural heart disease (SHD) requires fluoroscopic guidance, which exposes health care professionals to radiation exposure risk. Nevertheless, existing freestanding radiation shields for anesthesiologists are typically simple, uncomfortable rectangles. Therefore, we devised a new perforated radiation shield that allows anesthesiologists and echocardiographers to access a patient through its apertures during SHD catheterization. No report of the relevant literature has described the degree to which the anesthesiologist's radiation dose can be reduced by installing radiation shields. For estimating whole-body doses to anesthesiologists and air dose distributions in the operating room, we used a Monte Carlo system for a rapid dose-estimation system used with interventional radiology. The simulations were performed under four conditions: no radiation shield, large apertures, small apertures and without apertures. With small apertures, the doses to the lens, waist and neck surfaces were found to be comparable to those of a protective plate without an aperture, indicating that our new radiation shield copes with radiation protection and work efficiency. To simulate the air-absorbed dose distribution, results indicated that a fan-shaped area of the dose rate decrease was generated in the area behind the shield, as seen from the tube sphere. For the aperture, radiation was found to wrap around the backside of the shield, even at a height that did not match the aperture height. The data presented herein are expected to be of interest to all anesthesiologists who might be involved in SHD catheterization. The data are also expected to enhance their understanding of radiation exposure protection.

[Questionnaire Survey of Japanese Medical Physicists on Working Conditions in 2020].

The questionnaire survey was conducted in 2020 to investigate the working conditions of qualified medical physicists in Japan. We developed a web-based system for administering the questionnaire and surveyed 1,228 qualified medical physicists. The number of received responses was 405. We summarized the results of the survey by job category. The obtained results showed that most of the people working as certified medical physicists met the following conditions: (1) position of healthcare occupation, (2) direct supervisor is a medical doctor or a medical physicist, (3) licensed or passed an examination for a Class I Radiation Protection Supervisor, (4) without the license of professional radiotherapy technologist, (5) master's or doctor's degree, (6) being assigned to the section that is different from the radiological technologist section. The average annual salary was approximately 600,000 yen higher for those employed as medical physicists than for those employed as radiotherapy technologists. The percentage of work performed by a certified medical physicist in radiation therapy greatly varies depending on whether the physicist is dedicated to treatment planning and equipment quality control. Alternatively, the proportion of the true duties of medical physicists in charge of radiation therapy, as considered by qualified medical physicists in radiation therapy, was the same regardless of whether they were working full-time or not. The results of this survey updated the working status of certified medical physicists in Japan. We will continue to conduct the survey periodically and update the information to contribute to the improvement of the working conditions of medical physicists and policy recommendations.

Gradient boosting decision tree becomes more reliable than logistic regression in predicting probability for diabetes with big data.

We sought to verify the reliability of machine learning (ML) in developing diabetes prediction models by utilizing big data. To this end, we compared the reliability of gradient boosting decision tree (GBDT) and logistic regression (LR) models using data obtained from the Kokuho-database of the Osaka prefecture, Japan. To develop the models, we focused on 16 predictors from health checkup data from April 2013 to December 2014. A total of 277,651 eligible participants were studied. The prediction models were developed using a light gradient boosting machine (LightGBM), which is an effective GBDT implementation algorithm, and LR. Their reliabilities were measured based on expected calibration error (ECE), negative log-likelihood (Logloss), and reliability diagrams. Similarly, their classification accuracies were measured in the area under the curve (AUC). We further analyzed their reliabilities while changing the sample size for training. Among the 277,651 participants, 15,900 (7978 males and 7922 females) were newly diagnosed with diabetes within 3 years. LightGBM (LR) achieved an ECE of 0.0018 ± 0.00033 (0.0048 ± 0.00058), a Logloss of 0.167 ± 0.00062 (0.172 ± 0.00090), and an AUC of 0.844 ± 0.0025 (0.826 ± 0.0035). From sample size analysis, the reliability of LightGBM became higher than LR when the sample size increased more than [Formula: see text]. Thus, we confirmed that GBDT provides a more reliable model than that of LR in the development of diabetes prediction models using big data. ML could potentially produce a highly reliable diabetes prediction model, a helpful tool for improving lifestyle and preventing diabetes.

Deep Learning for Detection of Exercise-Induced Pulmonary Hypertension Using Chest X-Ray Images.

Background: Stress echocardiography is an emerging tool used to detect exercise-induced pulmonary hypertension (EIPH). However, facilities that can perform stress echocardiography are limited by issues such as cost and equipment. Objective: We evaluated the usefulness of a deep learning (DL) approach based on a chest X-ray (CXR) to predict EIPH in 6-min walk stress echocardiography. Methods: The study enrolled 142 patients with scleroderma or mixed connective tissue disease with scleroderma features who performed a 6-min walk stress echocardiographic test. EIPH was defined by abnormal cardiac output (CO) responses that involved an increase in mean pulmonary artery pressure (mPAP). We used the previously developed AI model to predict PH and calculated PH probability in this cohort. Results: EIPH defined as ΔmPAP/ΔCO >3.3 and exercise mPAP >25 mmHg was observed in 52 patients, while non-EIPH was observed in 90 patients. The patients with EIPH had a higher mPAP at rest than those without EIPH. The probability of PH based on the DL model was significantly higher in patients with EIPH than in those without EIPH. Multivariate analysis showed that gender, mean PAP at rest, and the probability of PH based on the DL model were independent predictors of EIPH. A model based on baseline parameters (age, gender, and mPAP at rest) was improved by adding the probability of PH predicted by the DL model (AUC: from 0.65 to 0.74; p = 0.046). Conclusion: Applying the DL model based on a CXR may have a potential for detection of EIPH in the clinical setting.

Label-free quality control and identification of human keratinocyte stem cells by deep learning-based automated cell tracking.

Stem cell-based products have clinical and industrial applications. Thus, there is a need to develop quality control methods to standardize stem cell manufacturing. Here, we report a deep learning-based automated cell tracking (DeepACT) technology for noninvasive quality control and identification of cultured human stem cells. The combination of deep learning-based cascading cell detection and Kalman filter algorithm-based tracking successfully tracked the individual cells within the densely packed human epidermal keratinocyte colonies in the phase-contrast images of the culture. DeepACT rapidly analyzed the motion of individual keratinocytes, which enabled the quantitative evaluation of keratinocyte dynamics in response to changes in culture conditions. Furthermore, DeepACT can distinguish keratinocyte stem cell colonies from non-stem cell-derived colonies by analyzing the spatial and velocity information of cells. This system can be widely applied to stem cell cultures used in regenerative medicine and provides a platform for developing reliable and noninvasive quality control technology.

Mixed Reality Visualization of Radiation Dose for Health Professionals and Patients in Interventional Radiology.

For interventional radiology, dose management has persisted as a crucially important issue to reduce radiation exposure to patients and medical staff. This study designed a real-time dose visualization system for interventional radiology designed with mixed reality technology and Monte Carlo simulation. An earlier report described a Monte-Carlo-based estimation system, which simulates a patient's skin dose and air dose distributions, adopted for our system. We also developed a system of acquiring fluoroscopic conditions to input them into the Monte Carlo system. Then we combined the Monte Carlo system with a wearable device for three-dimensional holographic visualization. The estimated doses were transferred sequentially to the device. The patient's dose distribution was then projected on the patient body. The visualization system also has a mechanism to detect one's position in a room to estimate the user's exposure dose to detect and display the exposure level. Qualitative tests were conducted to evaluate the workload and usability of our mixed reality system. An end-to-end system test was performed using a human phantom. The acquisition system accurately recognized conditions that were necessary for real-time dose estimation. The dose hologram represents the patient dose. The user dose was changed correctly, depending on conditions and positions. The perceived overall workload score (33.50) was lower than the scores reported in the literature for medical tasks (50.60) for computer activities (54.00). Mixed reality dose visualization is expected to improve exposure dose management for patients and health professionals by exhibiting the invisible radiation exposure in real space.

Deep Learning for Detection of Elevated Pulmonary Artery Wedge Pressure Using Standard Chest X-Ray.

BACKGROUND: To accurately diagnose and control heart failure (HF), it is important to carry out a simple assessment of elevated pulmonary arterial wedge pressure (PAWP). The aim of this study was to develop and validate an objective method for detecting elevated PAWP by applying deep learning (DL) to a chest x-ray (CXR). METHODS: We enrolled 1013 consecutive patients with a right-heart catheter between October 2009 and February 2020. We developed a convolutional neural network to identify patients with elevated PAWP (> 18 mm Hg) as the actual value of PAWP to be used in the dataset for training. In the prospective validation dataset used to detect elevated PAWP, the area under the receiver operating characteristic curve (AUC) was calculated using the DL model that evaluated the CXR. RESULTS: In the prospective validation dataset, the AUC of the DL model with CXR was not significantly different from the AUC produced by brain natriuretic peptide (BNP) and the echocardiographic left-ventricular diastolic dysfunction (DD) algorithm (DL model: 0.77 vs BNP: 0.77 vs DD algorithm: 0.70; respectively; P = NS for all comparisons); it was, however, significantly higher than the AUC of the cardiothoracic ratio (DL model vs cardiothoracic ratio [CTR]: 0.66, P = 0.044). The model based on 3 parameters (BNP, DD algorithm, and CTR) was improved by adding the DL model (AUC: from 0.80 to 0.86; P = 0.041). CONCLUSIONS: Applying the DL model based on a CXR (a classical, universal, and low-cost test) is useful for screening for elevated PAWP.

Causal relations of health indices inferred statistically using the DirectLiNGAM algorithm from big data of Osaka prefecture health checkups.

Causal relations among many statistical variables have been assessed using a Linear non-Gaussian Acyclic Model (LiNGAM). Using access to large amounts of health checkup data from Osaka prefecture obtained during the six fiscal years of years 2012-2017, we applied the DirectLiNGAM algorithm as a trial to extract causal relations among health indices for age groups and genders. Results show that LiNGAM yields interesting and reasonable results, suggesting causal relations and correlation among the statistical indices used for these analyses.

Deep learning to predict elevated pulmonary artery pressure in patients with suspected pulmonary hypertension using standard chest X ray.

Accurate diagnosis of pulmonary hypertension (PH) is crucial to ensure that patients receive timely treatment. We hypothesized that application of artificial intelligence (AI) to the chest X-ray (CXR) could identify elevated pulmonary artery pressure (PAP) and stratify the risk of heart failure hospitalization with PH. We retrospectively enrolled a total of 900 consecutive patients with suspected PH. We trained a convolutional neural network to identify patients with elevated PAP (> 20 mmHg) as the actual value of PAP. The endpoints in this study were admission or occurrence of heart failure with elevated PAP. In an independent evaluation set for detection of elevated PAP, the area under curve (AUC) by the AI algorithm was significantly higher than the AUC by measurements of CXR images and human observers (0.71 vs. 0.60 and vs. 0.63, all p < 0.05). In patients with AI predicted PH had 2-times the risk of heart failure with PH compared with those without AI predicted PH. This preliminary work suggests that applying AI to the CXR in high risk groups has limited performance when used alone in identifying elevated PAP. We believe that this report can serve as an impetus for a future large study.

Calculating and estimating second cancer risk from breast radiotherapy using Monte Carlo code with internal body scatter for each out-of-field organ.

Out-of-field organs are not commonly designated as dose calculation targets during radiation therapy treatment planning, but they might entail risks of second cancer. Risk components include specific internal body scatter, which is a dominant source of out-of-field doses, and head leakage, which can be reduced by external shielding. Our simulation study quantifies out-of-field organ doses and estimates second cancer risks attributable to internal body scatter in whole-breast radiotherapy (WBRT) with or without additional regional nodal radiotherapy (RNRT), respectively, for right and left breast cancer using Monte Carlo code PHITS. Simulations were conducted using a complete whole-body female model. Second cancer risk was estimated using the calculated doses with a concept of excess absolute risk. Simulation results revealed marked differences between WBRT alone and WBRT plus RNRT in out-of-field organ doses. The ratios of mean doses between them were as large as 3.5-8.0 for the head and neck region and about 1.5-6.6 for the lower abdominal region. Potentially, most out-of-field organs had excess absolute risks of less than 1 per 10,000 persons-year. Our study surveyed the respective contributions of internal body scatter to out-of-field organ doses and second cancer risks in breast radiotherapy on this intact female model.

[Information Mathematics for Machine Learning].

This manuscript is a supplement to the machine learning course at JSMP Medical Physics Summer School held in 2019. The idea of Kulbuck-Leibler divergence, a key concept in machine learning, is introduced with mutual information.

First experience of 192Ir source stuck event during high-dose-rate brachytherapy in Japan.

PURPOSE: To share the experience of an iridium-192 (192Ir) source stuck event during high-dose-rate (HDR) brachytherapy for cervical cancer. MATERIAL AND METHODS: In 2014, we experienced the first source stuck event in Japan when treating cervical cancer with HDR brachytherapy. The cause of the event was a loose screw in the treatment device that interfered with the gear reeling the source. This event had minimal clinical effects on the patient and staff; however, after the event, we created a normal treatment process and an emergency process. In the emergency processes, each staff member is given an appropriate role. The dose rate distribution calculated by the new Monte Carlo simulation system was used as a reference to create the process. RESULTS: According to the calculated dose rate distribution, the dose rates inside the maze, near the treatment room door, and near the console room were ≅ 10-2 [cGy · h-1], 10-3 [cGy · h-1], and << 10-3 [cGy · h-1], respectively. Based on these findings, in the emergency process, the recorder was evacuated to the console room, and the rescuer waited inside the maze until the radiation source was recovered. This emergency response manual is currently a critical workflow once a year with vendors. CONCLUSIONS: We reported our experience of the source stuck event. Details of the event and proposed emergency process will be helpful in managing a patient safety program for other HDR brachytherapy users.