Body weight is an indispensable parameter for determination of contrast medium dose, appropriate drug dosing, or management of radiation dose. However, we cannot always determine the accurate patient body weight at the time of computed tomography (CT) scanning, especially in emergency care. Time-efficient methods to estimate body weight with high accuracy before diagnostic CT scans currently do not exist. In this study, on the basis of 1831 chest and 519 abdominal CT scout images with the corresponding body weights, we developed and evaluated deep-learning models capable of automatically predicting body weight from CT scout images. In the model performance assessment, there were strong correlations between the actual and predicted body weights in both chest (ρ = 0.947, p < 0.001) and abdominal datasets (ρ = 0.869, p < 0.001). The mean absolute errors were 2.75 kg and 4.77 kg for the chest and abdominal datasets, respectively. Our proposed method with deep learning is useful for estimating body weights from CT scout images with clinically acceptable accuracy and potentially could be useful for determining the contrast medium dose and CT dose management in adult patients with unknown body weight.
Our computer-based method can detect the chronological change in joint space width between baseline and follow-up images as the joint space difference index (JSDI). The aim of this study was to verify the sensitivity and specificity of our computer-based method in assessment of joint space narrowing progression in rheumatoid patients. Twenty-seven patients (24 women and 3 men) with rheumatoid arthritis underwent radiography of the bilateral hand at baseline and at 1 year. The joint space narrowing (JSN) of a total of 252 metacarpophalangeal (MCP) joints and 229 carpal joints was assessed by our computer-based method, setting the Sharp/van der Heijde method as the gold standard. We constructed a receiver operating characteristic curve by using the Sharp/van der Heijde method as the gold standard and set the optimal cutoff on JSDI for MCP, carpal, and MCP/carpal joints. We then calculated the sensitivity and specificity for each cutoff in assessment of JSN progression. At the most discriminant cutoff, the sensitivity and specificity of the computer-based method for MCP joints was 78.6 versus 85.3 %, respectively (AUC = 0.837; P < 0.001). Carpal joints revealed a lower sensitivity and specificity with 64.7 and 86.8 % (AUC = 0.775; P < 0.001). Furthermore, the sensitivity and specificity for MCP/carpal joints was 71.0 versus 83.6 %, respectively (AUC = 0.778; P < 0.001). The computer-based method presented a reliable assessment of JSN progression with high sensitivity and specificity and may be useful in follow-up assessment of the joint damage in rheumatoid patients.
Objective: To investigate the validity of a computerbased method using temporal subtraction in carpal joints of patients with rheumatoid arthritis (RA), which can detect the difference in joint space between two images with the joint space difference index (JSDI). Methods: The study consisted of 43 patients with RA (39 females and 4 males) who underwent radiography at baseline and at 1-year follow-up. The joint space narrowing (JSN) of carpal joints on bilateral hand radiographs was assessed by our computer-based method, using the Sharp/van der Heijde method as the standard of reference. We compared the JSDI of joints with JSN progression in the follow-up period with that of those without JSN progression. In addition, we examined whether there is a significant difference in JSDI in terms of laterality or topology of the joint.Results: The JSDI of joints with JSN progression was significantly higher than that of those without JSN progression (Mann-Whitney U test, p , 0.001). There was no statistically significant difference in the JSDI between the left and right carpal joints, which was analysed for five different joints altogether and each joint separately (Mann-Whitney U test, p . 0.05). There was statistically significant difference in JSDI among different joints (Kruskal-Wallis test, p 5 0.003). Conclusion: These results suggest that our computerbased method may be useful to recognize the JSN progression on radiographs of rheumatoid wrists. Advances in knowledge: The computer-based temporal subtraction method can detect the JSN progression in the wrist, which is the single most commonly involved site in RA.
Patient misidentification in imaging examinations has become a serious problem in clinical settings. Such misidentification could be prevented if patient characteristics such as sex, age, and body weight could be predicted based on an image of the patient, with an alert issued when a mismatch between the predicted and actual patient characteristic is detected. Here, we tested a simple convolutional neural network (CNN)-based system that predicts patient sex from FDG PET-CT images. This retrospective study included 6,462 consecutive patients who underwent whole-body FDG PET-CT at our institute. The CNN system was used for classifying these patients by sex. Seventy percent of the randomly selected images were used to train and validate the system; the remaining 30% were used for testing. The training process was repeated five times to calculate the system’s accuracy. When images for the testing were given to the learned CNN model, the sex of 99% of the patients was correctly categorized. We then performed an image-masking simulation to investigate the body parts that are significant for patient classification. The image-masking simulation indicated the pelvic region as the most important feature for classification. Finally, we showed that the system was also able to predict age and body weight. Our findings demonstrate that a CNN-based system would be effective to predict the sex of patients, with or without age and body weight prediction, and thereby prevent patient misidentification in clinical settings.
Purpose: The purpose of this article was to verify the usefulness and feasibility of a single-phase scan for pulmonary artery/vein separation using a bolus-tracking technique and to evaluate the influence of patient characteristics on differentiation of computed tomography (CT) values between arteries and veins. Material and Methods: A total of 79 patients (60 male individuals and 19 female individuals, mean age 70 y) with suspected lung cancers or metastasis underwent contrast-enhanced chest CT and ultrasonic echocardiography. The CT values of the pulmonary arteries and veins were measured, and the difference in CT values was calculated. The relationships between the difference in CT values and age, weight, height, body surface area, body mass index, cardiac output, cardiac index, trigger time, trigger CT value, and pulmonary transit time were investigated using univariate linear regression analysis. Results: The CT values were 352.8±87.3 HU and 494.6±76.5 HU for the pulmonary arteries and veins, respectively (P<0.001). A significant but weak correlation was seen between the difference in CT values and the height (r=0.24), trigger time (r=0.35), cardiac index (r=−0.25), and pulmonary transit time (r=0.53) (P<0.05). There was no significant correlation between the difference in CT values and the remaining values. Conclusion: The single-phase scan protocol using a bolus-tracking technique is feasible to differentiate CT values between pulmonary arteries and veins. The influence of patient characteristics on the differentiation of CT values lacks impact. Thus, the suggested protocol may be suitable independent of these factors after further validation.
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