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Background: Morphological parameters of the lumbar spine are valuable in assessing lumbar spine diseases. However, manual measurement of lumbar morphological parameters is time-consuming. Deep learning has automatic quantitative and qualitative analysis capabilities. To develop a deep learning-based model for the automatic quantitative measurement of morphological parameters from anteroposterior digital radiographs of the lumbar spine and to evaluate its performance. Methods: This study used 1,368 anteroposterior digital radiographs of the lumbar spine to train a deep learning model to measure the quantitative morphological indicators, including L1 to L5 vertebral body height (VBH) and L1-L2 to L4-L5 intervertebral disc height (IDH). The means of the manual measurements by three radiologists were used as the reference standard. The parameters predicted by the model were analyzed against the manual measurements using paired t-tests. Percentage of correct key points (PCK), intra-class correlation coefficient (ICC), Pearson correlation coefficient (r), mean absolute error (MAE), root mean square error (RMSE), and Bland-Altman plots were performed to assess the performance of the model. Results: Within the 3-mm distance threshold, the model had a PCK range of 99.77-99.46% for the L1 to L4 vertebrae and 77.37% for the L5 vertebrae. Except for VBH-L5 and IDH_L3-L4, IDH_L4-L5 (P<0.05), the estimated values of the model in the remaining parameters were not statistically significant compared with the reference standard (P>0.05). Except for VBH-L5 and IDH_L4-L5, the model showed good correlation and consistency with the reference standard (ICC =0.84-0.96, r=0.85-0.97, MAE =0.5-0.66, RMSE =0.66-0.95). The model outperformed other models (EfficientDet + Unet, EfficientDet + DarkPose, HRNet, and Unet) in predicting landmarks within a distance threshold of 1.5 to 5 mm. Conclusions: The model developed in this study can automatically measure the morphological parameters of the L1 to L4 vertebrae from anteroposterior digital radiographs of the lumbar spine. Its performance is close to the level of radiologists.

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PURPOSE: To develop a deep learning-based cascaded HRNet model, in order to automatically measure X-ray imaging parameters of lumbar sagittal curvature and to evaluate its prediction performance. METHODS: A total of 3730 lumbar lateral digital radiography (DR) images were collected from picture archiving and communication system (PACS). Among them, 3150 images were randomly selected as the training dataset and validation dataset, and 580 images as the test dataset. The landmarks of the lumbar curve index (LCI), lumbar lordosis angle (LLA), sacral slope (SS), lumbar lordosis index (LLI), and the posterior edge tangent angle of the vertebral body (PTA) were identified and marked. The measured results of landmarks on the test dataset were compared with the mean values of manual measurement as the reference standard. Percentage of correct key-points (PCK), intra-class correlation coefficient (ICC), Pearson correlation coefficient (r), mean absolute error (MAE), mean square error (MSE), root-mean-square error (RMSE), and Bland-Altman plot were used to evaluate the performance of the cascade HRNet model. RESULTS: The PCK of the cascaded HRNet model was 97.9-100% in the 3 mm distance threshold. The mean differences between the reference standard and the predicted values for LCI, LLA, SS, LLI, and PTA were 0.43 mm, 0.99°, 1.11°, 0.01 mm, and 0.23°, respectively. There were strong correlation and consistency of the five parameters between the cascaded HRNet model and manual measurements (ICC = 0.989-0.999, R = 0.991-0.999, MAE = 0.63-1.65, MSE = 0.61-4.06, RMSE = 0.78-2.01). CONCLUSION: The cascaded HRNet model based on deep learning algorithm could accurately identify the sagittal curvature-related landmarks on lateral lumbar DR images and automatically measure the relevant parameters, which is of great significance in clinical application.

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OBJECTIVE: To test the feasibility of the clot volume and right ventricular dysfunction for risk stratification of acute pulmonary embolism (APE) patients. METHODS: CT pulmonary angiography (CTPA) images of 158 APE patients were collected. After excluding 38 (24.1%) patients due to unsatisfactory quality, 120 APE patients (61 males and 59 females) were divided into high-risk (n = 37) and non-high-risk (n = 83) groups. Clot burden was measured by an automated programme (clot volume) and by two semi-quantitative systems (Qanadli and Mastora scores). The ratios of the right ventricular diameter to left ventricular diameter (RVd/LVd) and area (RVa/LVa) were obtained. The correlations amongst the above parameters were analysed. Receiver operating characteristic (ROC) curves were calculated to determine the efficacy of high-risk APE. Multivariate analyses were used to identify the independent predictors. RESULTS: Strong positive correlations were found between the clot volume and both Qanadli score (r2  = 0.696, P < 0.001) and Mastora score (r2  = 0.728, P < 0.001), and moderate correlations were found between the clot volume and both RVd/LVd (r2  = 0.392, P < 0.001) and RVa/LVa (r2  = 0.389, P < 0.001). The clot volume contributed the highest efficacy (AUC = 0.992) for the identification of high-risk cases, followed by Mastora score (0.968), Qanadli score (0.952), RVa/LVa (0.900) and RVd/LVd (0.892). The clot volume and RVd/LVd were two independent factors of high-risk APE. CONCLUSIONS: The clot volume is correlated with semi-quantitative clot burden scores and CT measured cardiac parameters. The clot volume and RVd/LVd were two independent factors of high-risk APE patients.

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OBJECTIVE: Identification of interstitial lung disease (ILD) may be difficult in certain cases using pulmonary function tests (PFTs) or subjective radiological analysis. We evaluated the efficacy of quantitative computed tomography (CT) with 3-dimensional (3D) reconstruction for distinguishing ILD patients from healthy controls. MATERIALS AND METHODS: We retrospectively collected chest CT images of 102 ILD patients and 102 healthy matched controls, and measured the following parameters: lung parenchymal volume, emphysema indices low attenuation area LAA910 volume, LAA950 volume, LAA910%, and LAA950%, and mean lung density (MLD) for whole lung, left lung, right lung, and each lobe, respectively. The Mann-Whitney U test was used to compare quantitative CT parameters between groups. Receiver operating characteristic (ROC) curves, Bayesian stepwise discriminant analysis, and deep neural network analysis were used to test the discriminative performance of quantitative CT parameters. Binary logistic regression was performed to identify ILD markers. RESULTS: Total lung volume was lower in ILD patients than controls, while emphysema and MLD values were higher (P < 0.001) except LAA910 volume in right middle lobe. LAA910 volume, LAA950 volume, LAA910%, LAA950%, and MLD accurately distinguished ILD patients from healthy controls (AUC >0.5, P < 0.05), and high MLD was a significant marker for ILD (OR = 1.047, P < 0.05). CONCLUSIONS: This quantitative CT analysis can effectively identify ILD patients, providing an alternative to subjective image analysis and PFTs.

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PURPOSE: Automated pulmonary embolism (PE) segmentation is frequently used as a preprocessing step in the quantitative analysis of pulmonary embolism. Objective of this study is to analyze the potential limitation in automated PE segmentation using clinical cases. METHODS: A database of 304 computer tomography pulmonary angiography (CTPA) examinations was collected and confirmed to be PE. After processing using an automated scheme, two radiologists classified these cases into four groups of A, B, C and D, which represent 4 different segmentation results namely, (1) entire pulmonary artery identified without motivation artifacts, (2) entire pulmonary artery identified with motivation artifacts, (3) part of the pulmonary artery identified, and (4) none of the pulmonary artery identified. Then, the possible failed reasons in PE segmentation were analyzed and determined based on the image characterization of the diseases and the applied CTPA scanning protocols. RESULTS: In the study, 143 (47.0%., 30 (9.9%., 110 (36.2%. and 21 (6.9%. examinations were classified into groups A, B, C and D, respectively. Group C and D included the cases with failed segmentation. Fifteen failure reasons, including intrapulmonary abnormalities, extra-pulmonary abnormalities, diffuse pulmonary diseases, enlarged heart, absolute occluded vessels, embolism attached to artery wall, delayed scan time, skewed location, low scan dose, obvious artifact of superior vena cava, previous chest surgery, congenital deformities of the chest, incorrect positioning, missed images and other unknown reasons, were determined with corresponding case percentages ranging from 0.3%.o 9.2%. CONCLUSIONS: Automated segmentation failures were caused by specific lung diseases, anatomy varieties, improper scan time, improper scan dose, manual errors or other unknown reasons. Realization of those limitations is crucial for developing robust automated schemes to handle these issues in a single pass when a large number of CTPA examinations need to be analyzed.

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The present study was to develop and design a new sonography rigid bronchoscopy and corollary vacuum-assisted biopsy device system with less injury and complication. The system combined ultrasonic-probe with ultrasound catheter, a new medical ultrasound technique, and rigid bronchoscopy (RB) which is improved with an auxiliary vacuum-assisted biopsy device. The principle of the device is vacuum suction and rotary knife. The reduced outer diameter of the RB led to less pain and lower complications for the patient. With the help of ultrasonic-probe (30 MHz), lesions and blood vessels can be identified clearly and unintentional puncture and damage to blood vessels can be avoided. Plenty of lesions can be obtained quickly through the vacuum-assisted biopsy device without getting puncture needle in and out repeatedly. The novel endobronchial sonography rigid bronchoscopy and matched vacuum-assisted biopsy device has many remarkable advantages. It can enlarge the applied range of the RB from endobronchial to mediastinal lesions, avoiding unintentional puncture of vessels. Obtaining multiple samples with a higher accuracy rate than that by other sampling techniques, minimizing operation time, alleviating pain and decreasing the complication rate, the system makes up the technical deficiency for the diagnosis and treatment of the mediastinal lesions, to a certain degree.

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