Effect Evaluation of Platelet-Rich Plasma Combined with Vacuum Sealing Drainage on Serum Inflammatory Factors in Patients with Pressure Ulcer by Intelligent Algorithm-Based CT Image

This work was to explore the efficacy of intelligent algorithm-based computed tomography (CT) to evaluate platelet-rich plasma (PRP) combined with vacuum sealing drainage (VSD) in the treatment of patients with pressure ulcers. Based on the u-net network structure, an image denoising algorithm based on double residual convolution neural network (Dr-CNN) was proposed to denoise the CT images. A total of 84 patients who were hospitalized in hospital were randomly divided into group A (without any intervention), group B (PRP treatment), group C (VSD treatment), and group D (PRP+VSD treatment). Procalcitonin (PCT) was detected by enzyme-linked immunosorbent assay (ELISA) combined with immunofluorescence method, C-reactive protein (CRP) was detected by rate reflectance turbidimetry (RRT), and interleukin-6 (IL-6) was detected by electrochemiluminescence method. The results showed that after treatment, 44 cases (52.38%) of pressure ulcers patients recovered, 24 cases (28.57%) had no change in stage, and 16 cases (19.04%) developed pressure ulcers. The pain scores of group D at 1 week (3.35 ± 0.56 points) and 2 weeks (2.76 ± 0.55 points) after treatment were significantly lower than those in group C (7.77 ± 0.58 points and 6.34 ± 0.44 points, respectively). The time of complete wound healing in group D (24.5 ± 2.32) was obviously lower in contrast to that in groups A, B, and C (35.54 ± 3.22 days, 30.23 ± 2 days, and 29.34 ± 2.15 days, respectively). In addition, the medical satisfaction of group D (8.74 ± 0.69) was significantly higher than that of groups A, B, and C (4.69 ± 0.85, 5.22 ± 0.31, and 5.18 ± 0.59, respectively). The levels of IL-6 and PCT in group D were lower than those in groups A, B, and C, and the differences were statistically significant (P < 0.01). The average values of peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM) of the Dr-CNN network model were 37.21 ± 1.09 dB and 0.925 ± 0.01, respectively, which were higher than other algorithms. The mean values of root mean square error (MSE) and normalized mean absolute distance (NMAD) of the Dr-CNN network model were 0.022 ± 0.002 and 0.126 ± 0.012, respectively, which were significantly lower than other algorithms (P < 0.05). The experimental results showed that PrP combined with VSD could significantly reduce the inflammatory response of patients with pressure ulcers. PRP combined with VSD could significantly reduce the pain of dressing change for patients. Moreover, the performance model of image denoising algorithm based on double residual convolutional neural network was better than other algorithms.

Three-Dimensional Skin CT Based on Intelligent Algorithm in the Analysis of Skin Lesion Sites Features in Children with Psoriasis

This research was to explore the application value of three-dimensional computed tomography (CT) based on artificial intelligent algorithm in analyzing the characteristics of skin lesions in children with psoriasis. In this study, 15 children with psoriasis were selected as the observation group, and 15 children with other skin diseases were selected as the control group. The CT images were optimized, and the feature selection was carried out based on artificial intelligent algorithm. Firstly, the results were compared with the results of simple skin three-dimensional CT to determine the effectiveness. Then, the two groups of three-dimensional skin CT image features of skin psoriasis-like hyperplasia, Munro microabscess, dermal papillary vascular dilation, and squamous epithelium based on intelligent algorithms were compared. After comparison, the detection rate of psoriasis-like hyperplasia, Munro microabscess, dermal papillary vascular dilation, and squamous epithelium in the observation group was higher than that in the control group, with significant difference and statistical significance (P < 0.05). In addition, the sensitivity of psoriasis-like hyperplasia, Munro microabscess, dermal papilla vascular dilatation, and squamous epithelium in children with psoriasis was 80.0%, 86.7%, 80.0%, and 93.3%, respectively. The specificity of psoriasis-like hyperplasia, Munro microabscess, dermal papilla vascular dilatation, and squamous epithelium in children with psoriasis was 86.7%, 93.3%, 60.0%, and 73.3%, respectively. The results showed that Munro microabscess and psoriasis-like hyperplasia had high sensitivity and specificity in all diagnostic items, which could be used as important features of skin lesion sites in the diagnosis of psoriasis in children. The research provides a basis for the clinical diagnosis of psoriasis in children, which is worthy of clinical promotion.

Postoperative Curative Effect of Cardiac Surgery Diagnosed by Compressed Sensing Algorithm-Based E-Health CT Image Information and Effect of Baduanjin Exercise on Cardiac Autonomic Nerve Function of Patients

This research was aimed at exploring the effect of CT images reconstructed by optimized compressed sensing algorithm on postoperative diagnosis of patients with hypertensive heart disease and the influence of Baduanjin on cardiac autonomic nerve function. Based on the compressed sensing algorithm, the maximum likelihood expectation maximization algorithm was introduced to optimize it, and the optimization algorithm was established. The optimized algorithm and filtered back projection algorithm (FBP) were compared regarding the root mean squared error (RMSE), peak signal-to-noise ratio (PSNR), and structural similar image metric (SSIM). A total of 126 patients with hypertensive heart disease who underwent CT examination in the hospital were selected as study subjects. According to whether Baduanjin intervention was adopted, patients were divided into observation group (conventional treatment +Baduanjin) and control group (conventional treatment), with 63 patients in each group. The effect of CT examination on postoperative diagnosis was analyzed. Systolic blood pressure (SBP), diastolic blood pressure (DBP), differential pressure (DP), respiratory rate and heart rate (HR), very low-frequency (VLF) power, low-frequency (LF) power, high-frequency (HF) power, total power (TP) of HR variability, and changes in LF/HF of patients before and after treatment were compared. The RMSE of the compressed sensing optimization algorithm (3.28 ± 0.36) was significantly lower than that of the FBP algorithm (9.25 ± 1.03) (P < 0.05). The SSIM and PNSR of the compressed sensing optimization algorithm were (0.87 ± 0.10) and (21.22 ± 1.60) dB, respectively. The SSIM was significantly higher than the FBP algorithm (P < 0.01), and the PNSR was also higher than the FBP algorithm (P < 0.05). The detection rate of CT for pleural effusion was 16 cases (25.40%) higher than 5 cases (7.94%) with TTE (P < 0.01). After treatment, SBP, DBP, HR, LF, VLF, LF/HF, and DP values in the observation group were lower than those in the control group (P < 0.05), and TP and HF were higher than those in the control group (P < 0.05). It suggested that a novel algorithm was established based on compressed sensing algorithm to improve image quality. CT image had important guiding significance for postoperative diagnosis of heart. Baduanjin intervention could improve the integrated function of patient's autonomic nervous system and the regulation ability of the vagus nerve.

Artificial Intelligence Algorithm-Based Computed Tomography Image of Both Kidneys in Diagnosis of Renal Dysplasia

The objective of this study was to explore the accuracy of low-dosage computed tomography (CT) images based on the expectation maximization algorithm denoising algorithm (EM algorithm) in the detection and diagnosis of renal dysplasia, so as to provide reasonable research basis for accuracy improvement of clinical diagnosis of renal dysplasia. 120 patients with renal dysplasia in hospital were randomly selected as the research objects, and they were divided into two groups by random number method, with 60 patients in each group. The low-dosage CT images of patients in the control group were not processed (nonalgorithm group), and the low-dosage CT images of patients in the observation group were denoised using the EM algorithm (algorithm group). In addition, it was compared with the results of the comprehensive diagnosis (gold standard) to analyze the accuracy of the diagnosis of the two groups of patients and the consistency with the results of the pathological diagnosis. The results were compared with those of the comprehensive diagnosis (gold standard) to analyze the accuracy of the diagnosis of the two groups of patients. The results showed that the peak signal-to-noise ratio (PSNR) (15.9 dB) of the EM algorithm was higher than the regularized adaptive matching pursuit (RAMP) algorithm (1.69 dB) and the mean filter (4.3 dB) (P < 0.05). The time consumption of EM algorithm (21 s) was shorter than that of PWLS algorithm (34 s) and MS-PWLS algorithm (39 s) (P < 0.05). The diagnosis accuracy of dysplasia of single kidney, absence of single kidney, horseshoe kidney, and duplex kidney was obviously higher in the algorithm group than the control group (P < 0.05), which were 66.67% vs. 90%, 60% vs. 88.89%, 71.42% vs. 100%, and 60% vs. 88.89%, respectively. The incidence of hypertension in patients with autosomal dominant polycystic kidney disease (ADPKD) (56.77%) was much higher than that of the other diseases (P < 0.05). After denoising by the EM algorithm, low-dosage CT image could improve the diagnostic accuracy of several types of renal dysplasia except ADPKD, showing certain clinical application value. In addition, ADPKD was easy to cause hypertension.

Relationship Between Appendectomy Incidence and Computed Tomography Scans Based on Korean Nationwide Data, 2003-2017

BACKGROUND

Advances in medicine and changes in the medical environment can affect the diagnosis and treatment of diseases. The main purpose of the present study was to investigate whether the difference in accessibility to diagnosis and treatment facilities influenced the occurrence of appendectomy in Korea.

METHODS

We collected data on 183,531 appendectomy patients between 2003 and 2017 using the National Health Insurance Services claims. Retrospective analysis of relationship between the age-standardized rate (ASR) of appendectomy and clinical variables affecting medical accessibility was performed. Pearson's correlation analyses were used.

RESULTS

The incidence of appendectomy decreased from 30,164 cases in 2003 to 7,355 cases in 2017. The rate of computerized tomography (CT) scans for diagnosis of appendicitis increased from 4.73% in 2003 to 86.96% in 2017. The ASR of appendectomy in uncomplicated and complicated appendicitis decreased from 48.71 in 2005 to 13.40 in 2010 and 8.37 in 2005 to 2.96 in 2009, respectively. The ASR of appendectomy was higher in the high-income group. The proportion and ASR of appendectomy in older age group increased steadily with years. The total admission days continued to decrease from 6.02 days in 2003 to 4.96 days in 2017.

CONCLUSION

The incidence of appendectomy was seemingly associated with the rate of CT scan. In particular, the incidence of appendectomy in uncomplicated appendicitis was markedly reduced. Through enhanced accessibility to CT scans, accurate diagnosis and treatment of appendicitis can be facilitated.

Artificial Intelligence Algorithm-Based MRI in Evaluating the Treatment Effect of Acute Cerebral Infarction

The study is aimed at exploring the application of artificial intelligence algorithm-based magnetic resonance imaging (MRI) in the diagnosis of acute cerebral infarction, expected to provide a reference for diagnosis and effect evaluation of acute cerebral infarction. In this study, 80 patients diagnosed with suspected acute cerebral infarction per Diagnostic Criteria for Cerebral Infarction were selected as the research subjects. MRI images were reconstructed by deep dictionary learning to improve their recognition ability. At the same time, the same diagnostic operation was performed by Computed Tomography (CT) images to compare with MRI. The results of the interalgorithm comparison showed the image reconstruction effect of the deep dictionary learning model is significantly better than SAE reconstruction, single-layer dictionary reconstruction model, and KAVD reconstruction. After comparison, the results of MRI based on artificial intelligence algorithm and CT evaluation were statistically significant (P < 0.05). In the lesion image, the diameter of MRI lesions (3.81 ± 0.77 cm) based on artificial intelligence algorithm and the diameter of lesions in CT (3.66 ± 1.65 cm) also had significant statistical significance (P < 0.05). The results showed that MRI based on deep learning was more sensitive than CT imaging for diagnosis and evaluation of patients with acute cerebral infarction, with only 1 case misdiagnosed. The rate of disease detection and lesion image quality had a higher improvement. The results can provide effective support for the clinical application of MRI based on artificial intelligence algorithm in the diagnosis of acute cerebral infarction.

Deep Learning-Based Chest CT Image Features in Diagnosis of Lung Cancer

This study was to evaluate the diagnostic value of deep learning-optimized chest CT in the patients with lung cancer. 90 patients who were diagnosed with lung cancer by surgery or puncture in hospital were selected as the research subjects. The Mask Region Convolutional Neural Network (Mask-RCNN) model was a typical end-to-end image segmentation model, and Dual Path Network (DPN) was used in nodule detection. The results showed that the accuracy of DPN algorithm model in detecting lung lesions in lung cancer patients was 88.74%, the accuracy of CT diagnosis of lung cancer was 88.37%, the sensitivity was 82.91%, and the specificity was 87.43%. Deep learning-based CT examination combined with serum tumor detection, factoring into Neurospecific enolase (N S E), cytokeratin 19 fragment (CYFRA21), Carcinoembryonic antigen (CEA), and squamous cell carcinoma (SCC) antigen, improved the accuracy to 97.94%, the sensitivity to 98.12%, and the specificity to 100%, all showing significant differences (P < 0.05). In conclusion, this study provides a scientific basis for improving the diagnostic efficiency of CT imaging in lung cancer and theoretical support for subsequent lung cancer diagnosis and treatment.

Evaluating the Patient With a Pulmonary Nodule: A Review

IMPORTANCE

Pulmonary nodules are identified in approximately 1.6 million patients per year in the US and are detected on approximately 30% of computed tomographic (CT) images of the chest. Optimal treatment of an individual with a pulmonary nodule can lead to early detection of cancer while minimizing testing for a benign nodule.

OBSERVATIONS

At least 95% of all pulmonary nodules identified are benign, most often granulomas or intrapulmonary lymph nodes. Smaller nodules are more likely to be benign. Pulmonary nodules are categorized as small solid (<8 mm), larger solid (≥8 mm), and subsolid. Subsolid nodules are divided into ground-glass nodules (no solid component) and part-solid (both ground-glass and solid components). The probability of malignancy is less than 1% for all nodules smaller than 6 mm and 1% to 2% for nodules 6 mm to 8 mm. Nodules that are 6 mm to 8 mm can be followed with a repeat chest CT in 6 to 12 months, depending on the presence of patient risk factors and imaging characteristics associated with lung malignancy, clinical judgment about the probability of malignancy, and patient preferences. The treatment of an individual with a solid pulmonary nodule 8 mm or larger is based on the estimated probability of malignancy; the presence of patient comorbidities, such as chronic obstructive pulmonary disease and coronary artery disease; and patient preferences. Management options include surveillance imaging, defined as monitoring for nodule growth with chest CT imaging, positron emission tomography-CT imaging, nonsurgical biopsy with bronchoscopy or transthoracic needle biopsy, and surgical resection. Part-solid pulmonary nodules are managed according to the size of the solid component. Larger solid components are associated with a higher risk of malignancy. Ground-glass pulmonary nodules have a probability of malignancy of 10% to 50% when they persist beyond 3 months and are larger than 10 mm in diameter. A malignant nodule that is entirely ground glass in appearance is typically slow growing. Current bronchoscopy and transthoracic needle biopsy methods yield a sensitivity of 70% to 90% for a diagnosis of lung cancer.

CONCLUSIONS AND RELEVANCE

Pulmonary nodules are identified in approximately 1.6 million people per year in the US and approximately 30% of chest CT images. The treatment of an individual with a pulmonary nodule should be guided by the probability that the nodule is malignant, safety of testing, the likelihood that additional testing will be informative, and patient preferences.

Detecting COVID-19 Pneumonia over Fuzzy Image Enhancement on Computed Tomography Images

COVID-19 is the worst pandemic that has hit the globe in recent history, causing an increase in deaths. As a result of this pandemic, a number of research interests emerged in several fields such as medicine, health informatics, medical imaging, artificial intelligence and social sciences. Lung infection or pneumonia is the regular complication of COVID-19, and Reverse Transcription Polymerase Chain Reaction (RT-PCR) and computed tomography (CT) have played important roles to diagnose the disease. This research proposes an image enhancement method employing fuzzy expected value to improve the quality of the image for the detection of COVID-19 pneumonia. The principal objective of this research is to detect COVID-19 in patients using CT scan images collected from different sources, which include patients suffering from pneumonia and healthy people. The method is based on fuzzy histogram equalization and is organized with the improvement of the image contrast using fuzzy normalized histogram of the image. The effectiveness of the algorithm has been justified over several experiments on different features of CT images of lung for COVID-19 patients, like Ground-Glass Opacity (GGO), crazy paving, and consolidation. Experimental investigations indicate that among the 254 patients, 81.89% had features on both lungs; 9.5% on the left lung; and 10.24% on the right lung. The predominantly affected lobe was the right lower lobe (79.53%).

Design Computer-Aided Diagnosis System Based on Chest CT Evaluation of Pulmonary Nodules

At present, the diagnosis and treatment of lung cancer have always been one of the research hotspots in the medical field. Early diagnosis and treatment of this disease are necessary means to improve the survival rate of lung cancer patients and reduce their mortality. The introduction of computer-aided diagnosis technology can easily, quickly, and accurately identify the lung nodule area as an imaging feature of early lung cancer for the clinical diagnosis of lung cancer and is helpful for the quantitative analysis of the characteristics of lung nodules and is useful for distinguishing benign and malignant lung nodules. Growth provides an objective diagnostic reference standard. This paper studies ITK and VTK toolkits and builds a system platform with MFC. By studying the process of doctors diagnosing lung nodules, the whole system is divided into seven modules: suspected lung shadow detection, image display and image annotation, and interaction. The system passes through the entire lung nodule auxiliary diagnosis process and obtains the number of nodules, the number of malignant nodules, and the number of false positives in each set of lung CT images to analyze the performance of the auxiliary diagnosis system. In this paper, a lung region segmentation method is proposed, which makes use of the obvious differences between the lung parenchyma and other human tissues connected with it, as well as the position relationship and shape characteristics of each human tissue in the image. Experiments are carried out to solve the problems of lung boundary, inaccurate segmentation of lung wall, and depression caused by noise and pleural nodule adhesion. Experiments show that there are 2316 CT images in 8 sets of images of different patients, and the number of nodules is 56. A total of 49 nodules were detected by the system, 7 were missed, and the detection rate was 87.5%. A total of 64 false-positive nodules were detected, with an average of 8 per set of images. This shows that the system is effective for CT images of different devices, pixel pitch, and slice pitch and has high sensitivity, which can provide doctors with good advice.

Computed Tomography Image Features under Deep Learning Algorithm Applied in Staging Diagnosis of Bladder Cancer and Detection on Ceramide Glycosylation

The research is aimed at investigating computed tomography (CT) image based on deep learning algorithm and the application value of ceramide glycosylation in diagnosing bladder cancer. The images of ordinary CT detection were improved. In this study, 60 bladder cancer patients were selected and performed with ordinary CT detection, and the detection results were processed by CT based on deep learning algorithms and compared with pathological diagnosis. In addition, Western Blot technology was used to detect the expression of glucose ceramide synthase (GCS) in the cell membrane of tumor tissues and normal tissues of bladder. The comparison results found that, in simple CT clinical staging, the coincidence rates of T1 stage, T2a stage, T2b stage, T3 stage, and T4 stage were 28.56%, 62.51%, 78.94%, 84.61%, and 74.99%, respectively; and the total coincidence rate of CT clinical staging was 63.32%, which was greatly different from the clinical staging of pathological diagnosis (P < 0.05). In the clinical staging of algorithm-based CT test results, the coincidence rates of T1 stage and T2a stage were 50.01% and 91.65%, respectively; and those of T2b stage, T3 stage, and T4 stage were 100.00%; and the total coincidence rate was 96.69%, which was not obviously different from the clinical staging of pathological diagnosis (P > 0.05). Therefore, it could be concluded that the algorithm-based CT detection results were more accurate, and the use of CT scans based on deep learning algorithms in the preoperative staging and clinical treatment of bladder cancer showed reliable guiding significance and clinical value. In addition, it was found that the expression level of GCS in normal bladder tissues was much lower than that in bladder cancer tissues. This indicated that the changes in GCS were closely related to the development and prognosis of bladder cancer. Therefore, it was believed that GCS may be an effective target for the treatment of bladder cancer in the future, and further research was needed for specific conditions.

Comparison of lateral neck X-ray to neck CT in patients with suspicious bone impaction: "Old habits die hard"

OBJECTIVE

Bone impaction (BI) is a common cause for emergency room visits. Among foreign bodies, fish bone is considered the most common. The sensitivity of symptoms in predicting BI is relatively low, making imaging a central tool to aid diagnosis. Current imaging practices include both neck plain film radiography and none-contrast CT scans of the neck. We evaluated the accuracy of neck plain film radiography and CT scans of the neck for the diagnosis of BI.

METHODS

Retrospective review of all patients who presented to the emergency room between 2009 and 2016 with a suspicious history of BI whom underwent plain film neck radiography or CT. All Images were reviewed by two neuro-radiologist blinded to the clinical symptoms and findings. Symptoms, clinical findings and images results were compared to the final diagnosis.

RESULTS

89 patients (30.7%), out of 290 patients who presented with complaints of BI, were diagnosed with BI. Mean age was 44.7 years old. Plain film neck radiography failed to predict BI (sen. 14.4%, spe 89.8% accuracy 63.2%), neck CT has an improved accuracy and sensitivity in locating BI (sen. 83.3%, spe. 94.1% accuracy 92.5%). Interobserver agreement between the two neuro-radiologists was moderate (0.46) and substantial (0.77) in neck radiography and CT images, respectively. Neck radiography missed 60 (out of 61) oropharyngeal BI's.

CONCLUSION

Neck radiography has high inter-observer variability and low sensitivity for the diagnosis of BI. Neck CT should be the first imaging modality in patients with suspicious complaints for BI and negative physical exam.

Evaluation of Glucocorticoid Therapy in Asthma Children with Small Airway Obstruction Based on CT Features of Deep Learning

This study was aimed at exploring the treatment of asthma children with small airway obstruction in CT imaging features of deep learning and glucocorticoid. A total of 145 patients meeting the requirements in hospital were included in this study, and they were randomly assigned to receive aerosolized glucocorticoid (n = 45), aerosolized glucocorticoid combined with bronchodilator (n = 50), or oral steroids (n = 50) for 4 weeks after discharge. The lung function and fractional exhaled nitric oxide (FENO) indexes of the three groups were measured, respectively, and then the effective rates were compared to evaluate the clinical efficacy of glucocorticoids with different administration methods and combined medications in the short-term maintenance treatment after acute exacerbation of asthma. Deep learning algorithm was used for CT image segmentation. The CT image is sent to the workbench for processing on the workbench, and then the convolution operation is performed on each input pixel point during the image processing. After 4 weeks of maintenance treatment, FEF50 %, FEF75 %, and MMEF75/25 increased significantly, and FENO decreased significantly (P < 0.01). The improvement results of FEF50 %, FEF75 %, MMEF75/25, and FENO after maintenance treatment were as follows: the oral hormone group was the most effective, followed by the combined atomization inhalation group, and the hormone atomization inhalation group was the least effective. The differences among them were statistically significant (P < 0.05). The accuracy of artificial intelligence segmentation algorithm was 81%. All the hormones were more effective than local medication in the treatment of small airway function and airway inflammation. In the treatment of aerosol inhalation, the hormone combined with bronchiectasis drug was the most effective in improving small airway obstruction and reducing airway inflammation compared with single drug inhalation. Deep learning CT images are simple, noninvasive, and intuitively observe lung changes in asthma with small airway functional obstruction. Asthma with small airway functional obstruction has high clinical diagnosis and evaluation value.

Computed Tomography Image Characteristics before and after Interventional Treatment of Children's Lymphangioma under Artificial Intelligence Algorithm

The artificial intelligence algorithm was used to analyze the characteristics of computed tomography (CT) images before and after interventional treatment of children's lymphangioma. Retrospective analysis was performed, and 30 children with lymphangioma from the hospital were recruited as the study subjects. The ultrasound-guided bleomycin interventional therapy was adopted and applied to CT scanning through convolutional neural network (CNN). The CT imaging-related indicators before and after interventional therapy were detected, and feature analysis was performed. In addition, the CNN algorithm was adopted to segment the image of the tumor was clearer and more accurate. At the same time, the Dice similarity coefficient (DSC) of the CNN algorithm was 0.9, which had a higher degree of agreement. In terms of clinical symptoms, the cured children's lesions disappeared, the skin surface returned to normal color, and the treatment was smooth. In the two cases with effective treatment, the cystic mass at the lesion site was significantly smaller, and the nodules disappeared. CT images before interventional therapy showed that lymphangiomas in children were more common in the neck. The cystic masses at all lesion sites varied in diameter and size, and most of them were similar to round and irregular, with uniform density distribution. The boundary was clear, the cyst was solid, and there were different degrees of compression and spread to the surrounding structure. Most of them were polycystic, and a few of them were single cystic. After interventional treatment, CT images showed that 27 cases of cured children's lymphangioma completely disappeared. Lymphangioma was significantly reduced in two children with effective treatment. Edema around the tumor also decreased significantly. Patients who did not respond to the treatment received interventional treatment again, and the tumors disappeared completely on CT imaging. No recurrence or new occurrence was found in three-month follow-up. The total effective rate of interventional therapy for lymphangioma in children was 96.67%. The CNN algorithm can effectively compare the CT image features before and after interventional treatment for children's lymphangioma. It was suggested that the artificial intelligence algorithm-aided CT imaging examination was helpful to guide physicians in the accurate treatment of children's lymphangioma.

Fusion imaging in preoperative assessment of extent of disease in patients with advanced ovarian cancer: feasibility and agreement with laparoscopic findings

OBJECTIVES

Fusion imaging is an emerging technique that combines real-time ultrasound examination with images acquired previously using other modalities, such as computed tomography (CT), magnetic resonance imaging and positron emission tomography. The primary aim of this study was to evaluate the feasibility of fusion imaging in patients with suspicion of ovarian or peritoneal cancer. Secondary aims were: to compare the agreement of findings on fusion imaging, CT alone and ultrasound imaging alone with laparoscopic findings, in the assessment of extent of intra-abdominal disease; and to evaluate the time required for the fusion imaging technique.

METHODS

Patients with clinical and/or radiographic suspicion of advanced ovarian or peritoneal cancer who were candidates for surgery were enrolled prospectively between December 2019 and September 2020. All patients underwent a CT scan and ultrasound and fusion imaging to evaluate the presence or absence of the following abdominal-cancer features according to the laparoscopy-based scoring model (predictive index value (PIV)): supracolic omental disease, visceral carcinomatosis on the liver, lesser omental carcinomatosis and/or visceral carcinomatosis on the lesser curvature of the stomach and/or spleen, involvement of the paracolic gutter(s) and/or anterior abdominal wall, involvement of the diaphragm and visceral carcinomatosis on the small and/or large bowel (regardless of rectosigmoid involvement). The feasibility of the fusion examination in these patients was evaluated. Agreement of each imaging method (ultrasound, CT and fusion imaging) with laparoscopy (considered as reference standard) was calculated using Cohen's kappa coefficient.

RESULTS

Fifty-two patients were enrolled into the study. Fusion imaging was feasible in 51 (98%) of these patients (in one patient, it was not possible for technical reasons). Two patients were excluded because laparoscopy was not performed, leaving 49 women in the final analysis. Kappa values for CT, ultrasound and fusion imaging, using laparoscopy as the reference standard, in assessing the PIV parameters were, respectively: 0.781, 0.845 and 0.896 for the great omentum; 0.329, 0.608 and 0.847 for the liver surface; 0.472, 0.549 and 0.756 for the lesser omentum and/or stomach and/or spleen; 0.385, 0.588 and 0.795 for the paracolic gutter(s) and/or anterior abdominal wall; 0.385, 0.497 and 0.657 for the diaphragm; and 0.336, 0.410 and 0.469 for the bowel. The median time needed to perform the fusion examination was 20 (range, 10-40) min.

CONCLUSION

Fusion of CT images and real-time ultrasound imaging is feasible in patients with suspicion of ovarian or peritoneal cancer and improves the agreement with surgical findings when compared with ultrasound or CT scan alone. © 2021 International Society of Ultrasound in Obstetrics and Gynecology.

Fusion imaging in preoperative assessment of extent of disease in patients with advanced ovarian cancer: feasibility and agreement with laparoscopic findings

OBJECTIVES

Fusion imaging is an emerging technique that combines real-time ultrasound examination with images acquired previously using other modalities, such as computed tomography (CT), magnetic resonance imaging and positron emission tomography. The primary aim of this study was to evaluate the feasibility of fusion imaging in patients with suspicion of ovarian or peritoneal cancer. Secondary aims were: to compare the agreement of findings on fusion imaging, CT alone and ultrasound imaging alone with laparoscopic findings, in the assessment of extent of intra-abdominal disease; and to evaluate the time required for the fusion imaging technique.

METHODS

Patients with clinical and/or radiographic suspicion of advanced ovarian or peritoneal cancer who were candidates for surgery were enrolled prospectively between December 2019 and September 2020. All patients underwent a CT scan and ultrasound and fusion imaging to evaluate the presence or absence of the following abdominal-cancer features according to the laparoscopy-based scoring model (predictive index value (PIV)): supracolic omental disease, visceral carcinomatosis on the liver, lesser omental carcinomatosis and/or visceral carcinomatosis on the lesser curvature of the stomach and/or spleen, involvement of the paracolic gutter(s) and/or anterior abdominal wall, involvement of the diaphragm and visceral carcinomatosis on the small and/or large bowel (regardless of rectosigmoid involvement). The feasibility of the fusion examination in these patients was evaluated. Agreement of each imaging method (ultrasound, CT and fusion imaging) with laparoscopy (considered as reference standard) was calculated using Cohen's kappa coefficient.

RESULTS

Fifty-two patients were enrolled into the study. Fusion imaging was feasible in 51 (98%) of these patients (in one patient, it was not possible for technical reasons). Two patients were excluded because laparoscopy was not performed, leaving 49 women in the final analysis. Kappa values for CT, ultrasound and fusion imaging, using laparoscopy as the reference standard, in assessing the PIV parameters were, respectively: 0.781, 0.845 and 0.896 for the great omentum; 0.329, 0.608 and 0.847 for the liver surface; 0.472, 0.549 and 0.756 for the lesser omentum and/or stomach and/or spleen; 0.385, 0.588 and 0.795 for the paracolic gutter(s) and/or anterior abdominal wall; 0.385, 0.497 and 0.657 for the diaphragm; and 0.336, 0.410 and 0.469 for the bowel. The median time needed to perform the fusion examination was 20 (range, 10-40) min.

CONCLUSION

Fusion of CT images and real-time ultrasound imaging is feasible in patients with suspicion of ovarian or peritoneal cancer and improves the agreement with surgical findings when compared with ultrasound or CT scan alone. © 2021 International Society of Ultrasound in Obstetrics and Gynecology.

Correlation between Computed Tomography Clinical Diagnosis and Findings in Pediatric Computed Tomography

&lt;b&gt;Background and Objective:&lt;/b&gt; Pediatric Computed Tomography (CT) is a fast, accurate imaging examination using ionizing radiation to create detailed images of pathological conditions. The radiation benefit should be outweighing the risk through the procure justification and dose optimization. The study aimed to investigate the correlation between the physician's initial diagnosis and the CT findings to build procedure justification for a pediatric patient's head scan. &lt;b&gt;Materials and Methods:&lt;/b&gt; The study included 81 children examined clinically and by CT scan to diagnose cranial and cerebral pathology. Eighty-one pediatric patients were investigated by CT scan and clinical diagnosis. &lt;b&gt;Results:&lt;/b&gt; The patient age ranged between 1-15 years old, (44%) were male and (56%) females. The patients referred to the CT scan from emergency department n = 10 (7%), outpatient clinics n = 66, (84%) and inpatients clinics n = 5, (9%). The study showed that 46% of patients were normal with no CT findings. Almost half of the cases were negative and did not confirm the clinical diagnosis. &lt;b&gt;Conclusion:&lt;/b&gt; The study concluded that most head CT scans in children were not justified. An effort towards improving the refereeing physician's awareness about radiation dose and request justification should be conducted.

CT Image Feature under Intelligent Algorithm in the Evaluation of Continuous Blood Purification in the Treatment and Nursing of Pulmonary Infection-Caused Severe Sepsis

This study was to explore the CT image features based on intelligent algorithm to evaluate continuous blood purification in the treatment of severe sepsis caused by pulmonary infection and nursing. 50 patients in the hospital were selected as the research objects. Convolutional neural network algorithm was used to segment CT images of severe sepsis caused by pulmonary infection. They were randomly divided into 25 cases of experimental group and 25 cases of control group. The experimental group was given continuous blood purification treatment, combined with comprehensive nursing. The control group was given routine treatment and basic nursing. Fasting plasma glucose (FPG) and fasting insulin (FIN), interleukin-6 (IL-6), tumor necrosis factor (TNF-α), high-sensitivity c-reactive protein (hs-CRP) levels, CD3 +, CD4 +, CD4 +/CD8 + levels, ICU monitoring time, malnutrition inflammation score (MIS), and incidence of adverse events were compared between the two groups before and after treatment. There was no difference in FPG and FIN between the two groups before treatment. After treatment, the FPG and FIN of the experimental group were lower than those of the control group, and there was statistical significance (P < 0.05). There was no difference in IL-6, TNF-α, and hs-CRP between the two groups before treatment. After treatment, IL-6, TNF-α, and hs-CRP in the experimental group were lower than those in the control group. There was no difference in the percentage of CD3 +, CD4 +, and CD4 +/CD8 + between the two groups before treatment. After treatment, the CD3 +, CD4 +, and CD4 +/CD8 + in the experimental group were higher than those in the control group. The ICU monitoring time, MIS, and incidence of adverse events in the experimental group were lower than those in the control group (P > 0.05). Convolutional neural network algorithm can accurately identify and segment CT images of patients with severe sepsis, which has high clinical application value. Continuous blood purification therapy can effectively control blood glucose level, improve immune function, and reduce the content of inflammatory factors in patients with severe sepsis caused by pulmonary infection. Effective nursing measures can improve the therapeutic effect.

CT Image Features of the FBP Reconstruction Algorithm in the Evaluation of Fasting Blood Sugar Level of Diabetic Pulmonary Tuberculosis Patients and Early Diet Nursing

The study was aimed at exploring the application value of the CT image based on a filtered back projection (FBP) algorithm in the diagnosis of patients with diabetes complicated with tuberculosis and at analyzing the influence of dietary nursing on patients with diabetes complicated with tuberculosis. In this study, the FBP algorithm was used to optimize CT images to effectively obtain reconstructed ROI images. Then, the deviation from measurement values of reconstructed images at different pixel levels was analyzed. 138 patients with diabetes complicated with tuberculosis were selected as research subjects to compare the number of lung segments involved and the CT imaging manifestations at different fasting glucose levels. All patients were divided into the control group (routine drug treatment) and observation group (diet intervention on the basis of drug treatment) by random number table method, and the effect of different nursing methods on the improvement of patients' clinical symptoms was discussed. The results showed that the distance measurement value decreased with the increase in pixel level, there was no significant difference in the number of lung segments involved in patients with different fasting glucose levels (P > 0.05), and there were statistically significant differences in the incidence of segmental lobar shadow, bronchial air sign, wall-less cavity, thick-walled cavity, pulmonary multiple cavity, and bronchial tuberculosis in patients with different fasting glucose levels (P < 0.05). Compared with the control group, 2 h postprandial blood glucose level in the observation group was significantly improved (P < 0.05), there was a statistical significance in the number with reduced pleural effusion and the number with reduced tuberculosis foci in the two groups (P < 0.05), and the level of hemoglobin in the observation group was 7.1 ± 1.26, significantly lower than that in the control group (8.91 ± 2.03, P < 0.05). It suggested that the changes of CT images based on the FBP reconstruction algorithm were correlated with fasting blood glucose level. Personalized diet nursing intervention can improve the clinical symptoms of patients, which provides a reference for the clinical diagnosis and treatment of patients with diabetes complicated with tuberculosis.

Medical Image Diagnostic Value of Computed Tomography for Bladder Tumors

OBJECTIVE

To study computed tomography (CT) imaging characteristics of bladder tumors, to explore the value of CT in tumor diagnosis, and to identify the relevant factors of CT missed diagnosis so that medical staff can be more accurate in the diagnosis of bladder tumors.

METHODS

To retrospectively analyze the CT manifestations of 153 bladder tumor cases confirmed by paraffin pathology in our hospital and to study the difference between the benign and CT imaging features. CT indicators mainly include the number, location, morphology, calcification, bladder wall smoothness, CT value, degree of enhancement, and invasion of surrounding tissues and organs. Then, we retrospectively analyze 17 cases of CT missed diagnosis of bladder tumors, analyze related factors, and discuss the role of CT in the diagnosis of bladder tumors.

RESULTS

This study has shown that with the help of CT images, the diagnosis rate of bladder tumors has been greatly improved. Of the 153 patients studied, noninvasive urothelial carcinoma accounted for 18.95% of all benign and malignant bladder tumors, invasive urothelial carcinoma accounted for 67.93%, prostatic metastatic carcinoma and inflammatory myofibroblastoma accounted for 8.47%, pheochromocytoma accounted for 1.31%, inverted papilloma accounted for 1.31%, tubular choriocarcinoma accounted for 0.63%, and endocystitis accounted for 1.31%. In addition, the blood supply level, CT index bladder wall smoothness, and CT value are also statistically significant (P < 0.05).

CONCLUSIONS

CT is of high value in the diagnosis of bladder tumors, and benign and malignant bladder tumors have CT and CT imaging features. The size of bladder tumors is related to the missed diagnosis rate of CT. The application of CT examination technology can improve the accuracy of diagnosis of bladder tumors.

Radiomics-guided deep neural networks stratify lung adenocarcinoma prognosis from CT scans

Deep learning (DL) is a breakthrough technology for medical imaging with high sample size requirements and interpretability issues. Using a pretrained DL model through a radiomics-guided approach, we propose a methodology for stratifying the prognosis of lung adenocarcinomas based on pretreatment CT. Our approach allows us to apply DL with smaller sample size requirements and enhanced interpretability. Baseline radiomics and DL models for the prognosis of lung adenocarcinomas were developed and tested using local (n = 617) cohort. The DL models were further tested in an external validation (n = 70) cohort. The local cohort was divided into training and test cohorts. A radiomics risk score (RRS) was developed using Cox-LASSO. Three pretrained DL networks derived from natural images were used to extract the DL features. The features were further guided using radiomics by retaining those DL features whose correlations with the radiomics features were high and Bonferroni-corrected p-values were low. The retained DL features were subject to a Cox-LASSO when constructing DL risk scores (DRS). The risk groups stratified by the RRS and DRS showed a significant difference in training, testing, and validation cohorts. The DL features were interpreted using existing radiomics features, and the texture features explained the DL features well.

Volumetric evaluation of CT images of adrenal glands in primary aldosteronism

OBJECTIVES

To investigate whether adrenal volumetry provides better agreement with adrenal vein sampling (AVS) than conventional CT for subtyping PA. Furthermore, we evaluated whether the size of this contralateral adrenal was a prognostic factor for clinical outcome after unilateral adrenalectomy.

METHODS

We retrospectively analyzed volumes of both adrenal glands of the 180 CT-scans (88/180 with unilateral and 92/180 with bilateral disease) of the patients with PA included in the SPARTACUS trial of which 85 also had undergone an AVS. In addition, we examined CT-scans of 20 healthy individuals to compare adrenal volumes with published normal values.

RESULTS

Adrenal volume was higher for the left than the right adrenal (mean and SD: 6.49 ± 2.77 ml versus 5.25 ± 1.87 ml for the right adrenal; p < 0.001). Concordance between volumetry and AVS in subtyping was 58.8%, versus 51.8% between conventional CT results and AVS (p = NS). The volumes of the contralateral adrenals in the patients with unilateral disease (right 4.78 ± 1.37 ml; left 6.00 ± 2.73 ml) were higher than those of healthy controls reported in the literature (right 3.62 ± 1.23 ml p < 0.001; left 4.84 ± 1.67 ml p = 0.02). In a multivariable analysis the contralateral volume was not associated with biochemical or clinical success, nor with the defined daily doses of antihypertensive agents at 1 year follow-up.

CONCLUSIONS

Volumetry of the adrenal glands is not superior to current assessment of adrenal size by CT for subtyping patients with PA. Furthermore, in patients with unilateral disease the size of the contralateral adrenal is enlarged but its size is not associated with outcome.

Deep Learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) With CT Images

A novel coronavirus (COVID-19) recently emerged as an acute respiratory syndrome, and has caused a pneumonia outbreak world-widely. As the COVID-19 continues to spread rapidly across the world, computed tomography (CT) has become essentially important for fast diagnoses. Thus, it is urgent to develop an accurate computer-aided method to assist clinicians to identify COVID-19-infected patients by CT images. Here, we have collected chest CT scans of 88 patients diagnosed with COVID-19 from hospitals of two provinces in China, 100 patients infected with bacteria pneumonia, and 86 healthy persons for comparison and modeling. Based on the data, a deep learning-based CT diagnosis system was developed to identify patients with COVID-19. The experimental results showed that our model could accurately discriminate the COVID-19 patients from the bacteria pneumonia patients with an AUC of 0.95, recall (sensitivity) of 0.96, and precision of 0.79. When integrating three types of CT images, our model achieved a recall of 0.93 with precision of 0.86 for discriminating COVID-19 patients from others. Moreover, our model could extract main lesion features, especially the ground-glass opacity (GGO), which are visually helpful for assisted diagnoses by doctors. An online server is available for online diagnoses with CT images by our server (http://biomed.nscc-gz.cn/model.php). Source codes and datasets are available at our GitHub (https://github.com/SY575/COVID19-CT).

Deep Learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) With CT Images

A novel coronavirus (COVID-19) recently emerged as an acute respiratory syndrome, and has caused a pneumonia outbreak world-widely. As the COVID-19 continues to spread rapidly across the world, computed tomography (CT) has become essentially important for fast diagnoses. Thus, it is urgent to develop an accurate computer-aided method to assist clinicians to identify COVID-19-infected patients by CT images. Here, we have collected chest CT scans of 88 patients diagnosed with COVID-19 from hospitals of two provinces in China, 100 patients infected with bacteria pneumonia, and 86 healthy persons for comparison and modeling. Based on the data, a deep learning-based CT diagnosis system was developed to identify patients with COVID-19. The experimental results showed that our model could accurately discriminate the COVID-19 patients from the bacteria pneumonia patients with an AUC of 0.95, recall (sensitivity) of 0.96, and precision of 0.79. When integrating three types of CT images, our model achieved a recall of 0.93 with precision of 0.86 for discriminating COVID-19 patients from others. Moreover, our model could extract main lesion features, especially the ground-glass opacity (GGO), which are visually helpful for assisted diagnoses by doctors. An online server is available for online diagnoses with CT images by our server (http://biomed.nscc-gz.cn/model.php). Source codes and datasets are available at our GitHub (https://github.com/SY575/COVID19-CT).

Genetic Algorithm in Data Mining of Colorectal Images

There is currently no effective analytical method in colorectal image analysis, which leads to certain errors in colorectal image analysis. In order to improve the accuracy of colorectal imaging detection, this study used a genetic algorithm as the data mining algorithm and combined it with image processing technology to perform image analysis. At the same time, combined with the actual requirements of image detection, the gray theory model is used as the basic theory of image processing, and the image detection prediction model is constructed to predict the data. In addition, in order to study the effectiveness of the algorithm, the experiment is carried out to analyze the validity of the data of the study, and the predicted value is compared with the actual value. The research shows that the proposed algorithm has certain accuracy and can provide theoretical reference for subsequent related research.

A Liver Segmentation Method Based on the Fusion of VNet and WGAN

Accurate segmentation of liver images is an essential step in liver disease diagnosis, treatment planning, and prognosis. In recent years, although liver segmentation methods based on 2D convolutional neural networks have achieved good results, there is still a lack of interlayer information that causes severe loss of segmentation accuracy to a certain extent. Meanwhile, making the best of high-level and low-level features more effectively in a 2D segmentation network is a challenging problem. Therefore, we designed and implemented a 2.5-dimensional convolutional neural network, VNet_WGAN, to improve the accuracy of liver segmentation. First, we chose three adjacent layers of a liver model as the input of our network and adopted two convolution kernels in series connection, which can integrate cross-sectional spatial information and interlayer information of liver models. Second, a chain residual pooling module is added to fuse multilevel feature information to optimize the skip connection. Finally, the boundary loss function in the generator is employed to compensate for the lack of marginal pixel accuracy in the Dice loss function. The effectiveness of the proposed method is verified on two datasets, LiTS and CHAOS. The Dice coefficients are 92% and 90%, respectively, which are better than those of the compared segmentation networks. In addition, the experimental results also show that the proposed method can reduce computational consumption while retaining higher segmentation accuracy, which is significant for liver segmentation in practice and provides a favorable reference for clinicians in liver segmentation.

Variation in pediatric cervical spine imaging across trauma centers-A cause for concern?

BACKGROUND

Traumatic pediatric cervical spine injury can be challenging to diagnose, and the clinical algorithms meant to aid physicians differ from adult trauma protocols. Despite the existence of standardized guidelines, imaging decisions may vary according to physician education, subjective assessment, and experience with pediatric trauma patients. Our study investigates the rates of pediatric posttraumatic cervical spine imaging across trauma centers, hypothesizing that more specialized centers will have lower rates of advanced cervical spine imaging.

METHODS

The 2015 to 2016 Trauma Quality Improvement Program database was reviewed for patients younger than 18 years- to assess rates of cervical spine imaging on presentation across different trauma centers. Propensity stratification logistic regression was performed controlling for patient- and center-specific variables. p Values less than 0.05 were considered significant.

RESULTS

Of 110,769 pediatric trauma patients, 35.2% were female, and the average age was 9.6 years. Overall, 3.6% had cervical spine computed tomography (CT) and less than 1% had cervical spine MRI or X-ray. Compared with all others, Level I trauma centers were significantly less likely to use cervical spine CT for the initial evaluation of younger (≤14 years) but not older trauma patients (adjusted odds ratio [AOR], 0.89; 95% confidence interval [CI], 0.80-0.99; AOR, 0.97; 95% CI, 0.87-1.09); Level I centers had higher odds of cervical spine MRI use, but only for patients 14 years or younger (AOR, 1.63; 95% CI, 1.09-2.44). Pediatric-designated trauma centers had significantly lower odds of cervical spine CT (≤14 years: AOR, 0.70; 95% CI, 0.63-0.78; >14 years: AOR, 0.67; 95% CI, 0.67-0.75) and higher odds of cervical spine X-ray (≤14 years: AOR, 4.75; 95% CI, 3.55-6.36; >14 years: AOR, 4.50; 95% CI, 2.72-7.45) for all ages, but higher odds of cervical spine MRI for younger patients only (≤14 years: AOR, 2.10; 95% CI, 1.38-3.21).

CONCLUSION

Level I and pediatric designations were associated with lower rates of cervical spine CT. Pediatric centers were also more likely to use cervical spine X-ray. This variability of imaging use further supports the need to disseminate and educate providers on pediatric-specific cervical spine evaluation guidelines.

LEVEL OF EVIDENCE

Prognostic and epidemiological, level III.

A Novel Medical Image Denoising Method Based on Conditional Generative Adversarial Network

Medical image quality is highly relative to clinical diagnosis and treatment, leading to a popular research topic of medical image denoising. Image denoising based on deep learning methods has attracted considerable attention owing to its excellent ability of automatic feature extraction. Most existing methods for medical image denoising adapted to certain types of noise have difficulties in handling spatially varying noise; meanwhile, image detail losses and structure changes occurred in the denoised image. Considering image context perception and structure preserving, this paper firstly introduces a medical image denoising method based on conditional generative adversarial network (CGAN) for various unknown noises. In the proposed architecture, noise image with the corresponding gradient image is merged as network conditional information, which enhances the contrast between the original signal and noise according to the structural specificity. A novel generator with residual dense blocks makes full use of the relationship among convolutional layers to explore image context. Furthermore, the reconstruction loss and WGAN loss are combined as the objective loss function to ensure the consistency of denoised image and real image. A series of experiments for medical image denoising are conducted with the denoising results of PSNR = 33.2642 and SSIM = 0.9206 on JSRT datasets and PSNR = 35.1086 and SSIM = 0.9328 on LIDC datasets. Compared with the state-of-the-art methods, the superior performance of the proposed method is outstanding.

Computed tomography features of COVID-19 in children: A systematic review and meta-analysis

BACKGROUND

There are few reports on the chest computed tomography (CT) imaging features of children with coronavirus disease 2019 (COVID-19), and most reports involve small sample sizes.

OBJECTIVES

To systematically analyze the chest CT imaging features of children with COVID-19 and provide references for clinical practice.

DATA SOURCES

We searched PubMed, Web of Science, and Embase; data published by Johns Hopkins University; and Chinese databases CNKI, Wanfang, and Chongqing Weipu.

METHODS

Reports on chest CT imaging features of children with COVID-19 from January 1, 2020 to August 10, 2020, were analyzed retrospectively and a meta-analysis carried out using Stata12.0 software.

RESULTS

Thirty-seven articles (1747 children) were included in this study. The heterogeneity of meta-analysis results ranged from 0% to 90.5%. The overall rate of abnormal lung CT findings was 63.2% (95% confidence interval [CI]: 55.8%-70.6%), with a rate of 61.0% (95% CI: 50.8%-71.2%) in China and 67.8% (95% CI: 57.1%-78.4%) in the rest of the world in the subgroup analysis. The incidence of ground-glass opacities was 39.5% (95% CI: 30.7%-48.3%), multiple lung lobe lesions was 65.1% (95% CI: 55.1%-67.9%), and bilateral lung lesions was 61.5% (95% CI: 58.8%-72.2%). Other imaging features included nodules (25.7%), patchy shadows (36.8%), halo sign (24.8%), consolidation (24.1%), air bronchogram signs (11.2%), cord-like shadows (9.7%), crazy-paving pattern (6.1%), and pleural effusion (9.1%). Two articles reported 3 cases of white lung, another reported 2 cases of pneumothorax, and another 1 case of bullae.

CONCLUSIONS

The lung CT results of children with COVID-19 are usually normal or slightly atypical. The lung lesions of COVID-19 pediatric patients mostly involve both lungs or multiple lobes, and the common manifestations are patchy shadows, ground-glass opacities, consolidation, partial air bronchogram signs, nodules, and halo signs; white lung, pleural effusion, and paving stone signs are rare. Therefore, chest CT has limited value as a screening tool for children with COVID-19 and can only be used as an auxiliary assessment tool.

Advantages of CT nano-contrast agent in tumor diagnosis: A retrospective study

The purpose of this retrospective study was to explore the advantages of computed tomography (CT) nano-contrast agent in tumor diagnosis.A total of 100 patients with malignant tumor who were diagnosed in Shaanxi Province Public Hospital between January 2018 and January 2019 were included in this retrospective study. They were randomly divided into observation and control groups with 50 patients in each group. The patients in the observation group used new type of nano-contrast agent for examination, and the patients in the control group used traditional iohexol contrast agent for examination. The detection rate, misdiagnosis rate, and incidence of adverse reactions were observed. In addition, single photon emission computed tomography or CT scan was performed on patients to observe the radioactive concentration.The detection rate was 100% in the observation group and 84% in the control group, and the difference between the 2 groups was statistically significant (χ2 = 8.763, P = .001). The incidence of adverse reactions was 2% in the observation group and 30% in the control group, and the difference between the 2 groups was significantly different (χ2 = 12.683, P = .000). The radioactive concentration in the observation group was markedly higher than that in the control group (t = 19.692, P = .001).The use of CT nano-contrast agent in tumor diagnosis had higher detection rate of tumor and radioactive concentration, and it had lower misdiagnosis rate and adverse reaction rate than traditional iohexol contrast agent.

Variation in radiation dosing among pediatric trauma patients undergoing head computed tomography scan

BACKGROUND

When head injured children undergo head computed tomography (CT), radiation dosing can vary considerably between institutions, potentially exposing children to excess radiation, increasing risk for malignancies later in life. We compared radiation delivery from head CTs at a level 1 pediatric trauma center (PTC) versus scans performed at referring adult general hospitals (AGHs). We hypothesized that children at our PTC receive a significantly lower radiation dose than children who underwent CT at AGHs for similar injury profiles.

METHODS

We retrospectively reviewed the charts of all patients younger than 18 years who underwent CT for head injury at our PTC or at an AGH before transfer between January 1 and December 31, 2019. We analyzed demographic and clinical data. Our primary outcome was head CT radiation dose, as calculated by volumetric CT dose index (CTDIvol) and dose-length product (DLP; the product of CTDIvol and scan length). We used unadjusted bivariate and multivariable linear regression (adjusting for age, weight, sex) to compare doses between Children's Hospital Los Angeles and AGHs.

RESULTS

Of 429 scans reviewed, 193 were performed at our PTC, while 236 were performed at AGHs. Mean radiation dose administered was significantly lower at our PTC compared with AGHs (CTDIvol 20.3/DLP 408.7 vs. CTDIvol 30.6/DLP 533, p < 0.0001). This was true whether the AGH was a trauma center or not. After adjusting for covariates, findings were similar for both CTDIvol and DLP. Patients who underwent initial CT at an AGH and then underwent a second CT at our PTC received less radiation for the second CT (CTDIvol 25.6 vs. 36.5, p < 0.0001).

CONCLUSIONS

Head-injured children consistently receive a lower radiation dose when undergoing initial head CT at a PTC compared with AGHs. This provides a basis for programs aimed at establishing protocols to deliver only as much radiation as necessary to children undergoing head CT.

LEVEL OF EVIDENCE

Care Management/Therapeutic, level IV.

Are body surface area based estimates of liver volume applicable to children with overweight or obesity? An in vivo validation study

The liver is the primary organ responsible for clearing most drugs from the body and thus determines systemic drug concentrations over time. Drug clearance by the liver appears to be directly related to organ size. In children, organ size changes as children age and grow. Liver volume has been correlated with body surface area (BSA) in healthy children and adults and has been estimated by functions of BSA. However, these relationships were derived from "typical" populations and it is unknown whether they extend to estimations of liver volumes for population "outliers," such as children with overweight or obesity, who today represent one-third of the pediatric population. Using computerized tomography or magnetic resonance imaging, this study measured liver volumes in 99 children (2-21 years) with normal weight, overweight, or obesity and compared organ measurements with estimates calculated using an established liver volume equation. A previously developed equation relating BSA to liver volume adequately estimates liver volumes in children, regardless of weight status.

Prediction of postnatal outcome in fetuses with congenital lung malformation: 2-year follow-up study

OBJECTIVES

To identify, in fetuses with a congenital lung malformation (CLM), prenatal predictors of the need for postnatal respiratory support and the need for surgery by calculating the CLM volume ratio (CVR), and to evaluate the concordance between the prenatal appearance and the postnatal type of CLM.

METHODS

This was an analysis of prenatal, perinatal and postnatal data from fetuses diagnosed with a CLM at the Erasmus University Medical Center - Sophia Children's Hospital in Rotterdam, The Netherlands, between January 2007 and December 2016. For all included fetuses, CVR was measured retrospectively on stored ultrasound images obtained at 18 + 1 to 24 + 6 weeks (US1), 25 + 0 to 29 + 6 weeks (US2) and/or 30 + 0 to 35 + 6 weeks' gestation (US3). Postnatal diagnosis of CLM was based on computed tomography or histology. Primary outcomes were the need for respiratory support within 24 h and surgery within 2 years after birth.

RESULTS

Of the 80 fetuses with a CLM included in this study, 14 (18%) required respiratory support on the first postnatal day, and 17 (21%) required surgery within 2 years. Only the CVR at US2 was predictive of the need for respiratory support, with a cut-off value of 0.39. Four of 16 (25%) fetuses which showed full regression of the CLM prenatally required respiratory support within 24 h after birth. The CVR at US1, US2 and US3 was predictive of surgery within 2 years. Overall, the prenatal appearance of the CLM showed low concordance with the postnatal type. Prenatally suspected microcystic congenital pulmonary airway malformation (CPAM) was shown on computed tomography after birth to be congenital lobar overinflation in 15/35 (43%) cases. Respiratory support within 24 h after birth and surgical resection within 28 days after birth were needed in all cases of macrocystic CPAM.

CONCLUSIONS

CVR can predict the need for respiratory support within 24 h after birth and for surgery within 2 years. Regression of a CLM prenatally does not rule out respiratory problems after birth. © 2020 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology. - Legal Statement: This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

Synergistic tomographic image reconstruction: part 2

This special issue is the second part of a themed issue that focuses on synergistic tomographic image reconstruction and includes a range of contributions in multiple disciplines and application areas. The primary subject of study lies within inverse problems which are tackled with various methods including statistical and computational approaches. This volume covers algorithms and methods for a wide range of imaging techniques such as spectral X-ray computed tomography (CT), positron emission tomography combined with CT or magnetic resonance imaging, bioluminescence imaging and fluorescence-mediated imaging as well as diffuse optical tomography combined with ultrasound. Some of the articles demonstrate their utility on real-world challenges, either medical applications (e.g. motion compensation for imaging patients) or applications in material sciences (e.g. material decomposition and characterization). One of the desired outcomes of the special issues is to bring together different scientific communities which do not usually interact as they do not share the same platforms such as journals and conferences. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 2'.

Core Imaging Library - Part II: multichannel reconstruction for dynamic and spectral tomography

The newly developed core imaging library (CIL) is a flexible plug and play library for tomographic imaging with a specific focus on iterative reconstruction. CIL provides building blocks for tailored regularized reconstruction algorithms and explicitly supports multichannel tomographic data. In the first part of this two-part publication, we introduced the fundamentals of CIL. This paper focuses on applications of CIL for multichannel data, e.g. dynamic and spectral. We formalize different optimization problems for colour processing, dynamic and hyperspectral tomography and demonstrate CIL's capabilities for designing state-of-the-art reconstruction methods through case studies and code snapshots. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 2'.

Core Imaging Library - Part I: a versatile Python framework for tomographic imaging

We present the Core Imaging Library (CIL), an open-source Python framework for tomographic imaging with particular emphasis on reconstruction of challenging datasets. Conventional filtered back-projection reconstruction tends to be insufficient for highly noisy, incomplete, non-standard or multi-channel data arising for example in dynamic, spectral and in situ tomography. CIL provides an extensive modular optimization framework for prototyping reconstruction methods including sparsity and total variation regularization, as well as tools for loading, preprocessing and visualizing tomographic data. The capabilities of CIL are demonstrated on a synchrotron example dataset and three challenging cases spanning golden-ratio neutron tomography, cone-beam X-ray laminography and positron emission tomography. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 2'.

Synergistic multi-spectral CT reconstruction with directional total variation

This work considers synergistic multi-spectral CT reconstruction where information from all available energy channels is combined to improve the reconstruction of each individual channel. We propose to fuse these available data (represented by a single sinogram) to obtain a polyenergetic image which keeps structural information shared by the energy channels with increased signal-to-noise ratio. This new image is used as prior information during a channel-by-channel minimization process through the directional total variation. We analyse the use of directional total variation within variational regularization and iterative regularization. Our numerical results on simulated and experimental data show improvements in terms of image quality and in computational speed. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 2'.

Core Imaging Library - Part I: a versatile Python framework for tomographic imaging

We present the Core Imaging Library (CIL), an open-source Python framework for tomographic imaging with particular emphasis on reconstruction of challenging datasets. Conventional filtered back-projection reconstruction tends to be insufficient for highly noisy, incomplete, non-standard or multi-channel data arising for example in dynamic, spectral and in situ tomography. CIL provides an extensive modular optimization framework for prototyping reconstruction methods including sparsity and total variation regularization, as well as tools for loading, preprocessing and visualizing tomographic data. The capabilities of CIL are demonstrated on a synchrotron example dataset and three challenging cases spanning golden-ratio neutron tomography, cone-beam X-ray laminography and positron emission tomography. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 2'.

Prevalence and clinical consequences of atelectasis in SARS-CoV-2 pneumonia: a computed tomography retrospective cohort study

BACKGROUND

The aim of the study is to estimate the prevalence of atelectasis assessed with computer tomography (CT) in SARS-CoV-2 pneumonia and the relationship between the amount of atelectasis with oxygenation impairment, Intensive Care Unit admission rate and the length of in-hospital stay.

PATIENTS AND METHODS

Two-hundred thirty-seven patients admitted to the hospital with SARS-CoV-2 pneumonia diagnosed by clinical, radiology and molecular tests in the nasopharyngeal swab who underwent a chest computed tomography because of a respiratory worsening from Apr 1 to Apr 30, 2020 were included in the study. Patients were divided into three groups depending on the presence and amount of atelectasis at the computed tomography: no atelectasis, small atelectasis (< 5% of the estimated lung volume) or large atelectasis (> 5% of the estimated lung volume). In all patients, clinical severity, oxygen-therapy need, Intensive Care Unit admission rate, the length of in-hospital stay and in-hospital mortality data were collected.

RESULTS

Thirty patients (19%) showed small atelectasis while eight patients (5%) showed large atelectasis. One hundred and seventeen patients (76%) did not show atelectasis. Patients with large atelectasis compared to patients with small atelectasis had lower SatO2/FiO2 (182 vs 411 respectively, p = 0.01), needed more days of oxygen therapy (20 vs 5 days respectively, p = 0,02), more frequently Intensive Care Unit admission (75% vs 7% respectively, p < 0.01) and a longer period of hospitalization (40 vs 14 days respectively p < 0.01).

CONCLUSION

In patients with SARS-CoV-2 pneumonia, atelectasis might appear in up to 24% of patients and the presence of larger amount of atelectasis is associated with worse oxygenation and clinical outcome.

Radiation dose levels in chest computed tomography scans of coronavirus disease 2019 pneumonia: A survey of 2119 patients in Chongqing, southwest China

ABSTRACT

To investigate computed tomography (CT) diagnostic reference levels for coronavirus disease 2019 (COVID-19) pneumonia by collecting radiation exposure parameters of the most performed chest CT examinations and emphasize the necessity of low-dose CT in COVID-19 and its significance in radioprotection.The survey collected RIS data from 2119 chest CT examinations for 550 COVID-19 patients performed in 92 hospitals from January 23, 2020 to May 1, 2020. Dose data such as volume computed tomography dose index, dose-length product, and effective dose (ED) were recorded and analyzed. The radiation dose levels in different hospitals have been compared, and average ED and cumulative ED have been studied.The median dose-length product, volume computed tomography dose index, and ED measurements were 325.2 mGy cm with a range of 6.79 to 1098 mGy cm, 9.68 mGy with a range of 0.62 to 33.80 mGy, and 4.55 mSv with a range of 0.11 to 15.37 mSv for COVID-19 CT scanning protocols in Chongqing, China. The distribution of all observed EDs of radiation received by per patient undergoing CT protocols during hospitalization yielded a median cumulative ED of 17.34 mSv (range, 2.05-53.39 mSv) in the detection and management of COVID-19 patients. The average number of CT scan times for each patient was 4.0 ± 2.0, and the average time interval between 2 CT scans was 7.0 ± 5.0 days. The average cumulative ED of chest CT examinations for COVID-19 patients in Chongqing, China greatly exceeded public limit and the annual dose limit of occupational exposure in a short period.For patients with known or suspected COVID-19, a chest CT should be performed on the principle of rapid-scan, low-dose, single-phase protocol instead of routine chest CT protocol to minimize radiation doses and motion artifacts.

The impact of measuring split kidney function on post-donation kidney function: A retrospective cohort study

Background

Studies have reported agreement between computed tomography (CT) and renography for the determination of split kidney function. However, their correlation with post-donation kidney function remains unclear. We compared CT measurements with renography in assessment of split kidney function (SKF) and their correlations with post-donation kidney function.

Methods

A single-centre, retrospective cohort study of 248 donors from January 1, 2009-July 31, 2019 were assessed. Pearson correlations were used to assess post-donation kidney function with renography and CT-based measurements. Furthermore, we examined high risk groups with SKF difference greater than 10% on renography and donors with post-donation eGFR less than 60 mL/min/1.73m².

Results

62% of donors were women with a mean (standard deviation) pre-donation eGFR 99 (20) and post-donation eGFR 67 (22) mL/min/1.73m² at 31 months of follow-up. Post-donation kidney function was poorly correlated with both CT-based measurements and renography, including the subgroup of donors with post-donation eGFR less than 60 mL/min/1.73m² (r less than 0.4 for all). There was agreement between CT-based measurements and renography for SKF determination (Bland-Altman agreement [bias, 95% limits of agreement] for renography vs: CT volume, 0.76%, -7.60–9.15%; modified ellipsoid,1.01%, -8.38–10.42%; CC dimension, 0.44%, -7.06–7.94); however, CT missed SKF greater than 10% found by renography in 20 out 26 (77%) of donors.

Conclusions

In a single centre study of 248 living donors, we found no correlation between CT or renography and post-donation eGFR. Further research is needed to determine optimal ways to predict remaining kidney function after donation.

Deep Bidirectional Classification Model for COVID-19 Disease Infected Patients

In December of 2019, a novel coronavirus (COVID-19) appeared in Wuhan city, China and has been reported in many countries with millions of people infected within only four months. Chest computed Tomography (CT) has proven to be a useful supplement to reverse transcription polymerase chain reaction (RT-PCR) and has been shown to have high sensitivity to diagnose this condition. Therefore, radiological examinations are becoming crucial in early examination of COVID-19 infection. Currently, CT findings have already been suggested as an important evidence for scientific examination of COVID-19 in Hubei, China. However, classification of patient from chest CT images is not an easy task. Therefore, in this paper, a deep bidirectional long short-term memory network with mixture density network (DBM) model is proposed. To tune the hyperparameters of the DBM model, a Memetic Adaptive Differential Evolution (MADE) algorithm is used. Extensive experiments are drawn by considering the benchmark chest-Computed Tomography (chest-CT) images datasets. Comparative analysis reveals that the proposed MADE-DBM model outperforms the competitive COVID-19 classification approaches in terms of various performance metrics. Therefore, the proposed MADE-DBM model can be used in real-time COVID-19 classification systems.

A transfer learning approach to facilitate ComBat-based harmonization of multicentre radiomic features in new datasets

PURPOSE

To facilitate the demonstration of the prognostic value of radiomics, multicenter radiomics studies are needed. Pooling radiomic features of such data in a statistical analysis is however challenging, as they are sensitive to the variability in scanner models, acquisition protocols and reconstruction settings, which is often unavoidable in a multicentre retrospective analysis. A statistical harmonization strategy called ComBat was utilized in radiomics studies to deal with the "center-effect". The goal of the present work was to integrate a transfer learning (TL) technique within ComBat-and recently developed alternate versions of ComBat with improved flexibility (M-ComBat) and robustness (B-ComBat)-to allow the use of a previously determined harmonization transform to the radiomic feature values of new patients from an already known center.

MATERIAL AND METHODS

The proposed TL approach were incorporated in the four versions of ComBat (standard, B, M, and B-M ComBat). The proposed approach was evaluated using a dataset of 189 locally advanced cervical cancer patients from 3 centers, with magnetic resonance imaging (MRI) and positron emission tomography (PET) images, with the clinical endpoint of predicting local failure. The impact performance of the TL approach was evaluated by comparing the harmonization achieved using only parts of the data to the reference (harmonization achieved using all the available data). It was performed through three different machine learning pipelines.

RESULTS

The proposed TL technique was successful in harmonizing features of new patients from a known center in all versions of ComBat, leading to predictive models reaching similar performance as the ones developed using the features harmonized with all the data available.

CONCLUSION

The proposed TL approach enables applying a previously determined ComBat transform to new, previously unseen data.

An Overview of Deep Learning Techniques on Chest X-Ray and CT Scan Identification of COVID-19

Pneumonia is an infamous life-threatening lung bacterial or viral infection. The latest viral infection endangering the lives of many people worldwide is the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes COVID-19. This paper is aimed at detecting and differentiating viral pneumonia and COVID-19 disease using digital X-ray images. The current practices include tedious conventional processes that solely rely on the radiologist or medical consultant's technical expertise that are limited, time-consuming, inefficient, and outdated. The implementation is easily prone to human errors of being misdiagnosed. The development of deep learning and technology improvement allows medical scientists and researchers to venture into various neural networks and algorithms to develop applications, tools, and instruments that can further support medical radiologists. This paper presents an overview of deep learning techniques made in the chest radiography on COVID-19 and pneumonia cases.

Head Computed Tomography Scans in Elderly Patients with Low Velocity Head trauma after a Fall

BACKGROUND

Recommendations for a head computed tomography (CT) scan in elderly patients without a loss of consciousness after a traumatic brain injury and without neurological findings on admission and who are not taking oral anticoagulant therapy, are discordant.

OBJECTIVES

To determine variables associated with intracranial hemorrhage (ICH) and the need for neurosurgery in elderly patients after low velocity head trauma.

METHODS

In a regional hospital, we retrospectively selected 206 consecutive patients aged ≥ 65 years with head CT scans ordered in the emergency department because of low velocity head trauma. Outcome variables were an ICH and neurological surgery. Independent variables included age, sex, disability, neurological findings, facial fractures, mental status, headache, head sutures, loss of consciousness, and anticoagulation therapy.

RESULTS

Fourteen patients presented with ICH (6.8%, 3.8-11.1%) and three (1.5%, 0.3-4.2%) with a neurosurgical procedure. One patient with a coma (0.5, 0.0-2.7) died 2 hours after presentation. All patients who required surgery or died had neurological findings. Reducing head CT scans by 97.1% (93.8-98.9%) would not have missed any patient with possible surgical utility. Twelve of the 14 patients (85.7%) with an ICH had neurological findings, post-trauma loss of consciousness or a facial fracture were not present in 83.5% (95% confidence interval 77.7-88.3) of the cohort.

CONCLUSIONS

None of our patients with neurological findings required neurosurgery. Careful palpation of the facial bones to identify facial fractures might aid in the decision whether to perform a head CT scan.

Trends in utilization of whole-body computed tomography in blunt trauma after MVC: Analysis of the Trauma Quality Improvement Program database

BACKGROUND

The use of whole-body computed tomography (WBCT) in awake, clinically stable injured patients is controversial. It is associated with unnecessary radiation exposure and increased cost. We evaluate use of computed tomography (CT) imaging during the initial evaluation of injured patients at American College of Surgeons Levels I and II trauma centers (TCs) after blunt trauma.

METHODS

We identified adult blunt trauma patients after motor vehicle crash (MVC) from the American College of Surgeons Trauma Quality Improvement Program (TQIP) database between 2007 and 2016 at Level I or II TCs. We defined awake clinically stable patients as those with systolic blood pressure of 100 mm Hg or higher with a Glasgow Coma Scale score of 15. Computed tomography imaging had to have been performed within 2 hours of arrival. Whole-body computed tomography was defined as simultaneous CT of the head, chest and abdomen, and selective CT if only one to two aforementioned regions were imaged. Patients were stratified by Injury Severity Score (ISS).

RESULTS

There were 217,870 records for analysis; 131,434 (60.3%) had selective CT, and 86,436 (39.7%) had WBCT. Overall, there was an increasing trend in WBCT utilization over the study period (p < 0.001). In patients with ISS less than 10, WBCT was utilized more commonly at Level II versus Level I TCs in patients discharged from the emergency department (26.9% vs. 18.3%, p < 0.001), which had no surgical procedure(s) (81.4% vs. 80.3%, p < 0.001) and no injury of the head (53.7% vs. 52.4%, p = 0.008) or abdomen (83.8% vs. 82.1%, p = 0.001). The risk-adjusted odds of WBCT was two times higher at Level II TC vs. Level I (odds ratio, 1.88; 95% confidence interval 1.82-1.94; p < 0.001).

CONCLUSION

Whole-body computed tomography utilization is increasing relative to selective CT. This increasing utilization is highest at Level II TCs in patients with low ISSs, and in patients without associated head or abdominal injury. The findings have implications for quality improvement and cost reduction.

LEVEL OF EVIDENCE

Care management, Level IV.

Diagnostic yield, safety, and advantages of ultra-low dose chest CT compared to chest radiography in early stage suspected SARS-CoV-2 pneumonia: A retrospective observational study

ABSTRACT

To determine the role of ultra-low dose chest computed tomography (uld CT) compared to chest radiographs in patients with laboratory-confirmed early stage SARS-CoV-2 pneumonia.Chest radiographs and uld CT of 12 consecutive suspected SARS-CoV-2 patients performed up to 48 hours from hospital admission were reviewed by 2 radiologists. Dosimetry and descriptive statistics of both modalities were analyzed.On uld CT, parenchymal abnormalities compatible with SARS-CoV-2 pneumonia were detected in 10/12 (83%) patients whereas on chest X-ray in, respectively, 8/12 (66%) and 5/12 (41%) patients for reader 1 and 2. The average increment of diagnostic performance of uld CT compared to chest X-ray was 29%. The average effective dose was, respectively, of 0.219 and 0.073 mSv.Uld CT detects substantially more lung injuries in symptomatic patients with suspected early stage SARS-CoV-2 pneumonia compared to chest radiographs, with a significantly better inter-reader agreement, at the cost of a slightly higher equivalent radiation dose.

Deep learning predicts cardiovascular disease risks from lung cancer screening low dose computed tomography

Cancer patients have a higher risk of cardiovascular disease (CVD) mortality than the general population. Low dose computed tomography (LDCT) for lung cancer screening offers an opportunity for simultaneous CVD risk estimation in at-risk patients. Our deep learning CVD risk prediction model, trained with 30,286 LDCTs from the National Lung Cancer Screening Trial, achieves an area under the curve (AUC) of 0.871 on a separate test set of 2,085 subjects and identifies patients with high CVD mortality risks (AUC of 0.768). We validate our model against ECG-gated cardiac CT based markers, including coronary artery calcification (CAC) score, CAD-RADS score, and MESA 10-year risk score from an independent dataset of 335 subjects. Our work shows that, in high-risk patients, deep learning can convert LDCT for lung cancer screening into a dual-screening quantitative tool for CVD risk estimation.

CT visual quantitative evaluation of hypertensive patients with coronavirus disease (COVID-19): Potential influence of angiotensin converting enzyme inhibitors / angiotensin receptor blockers on severity of lung involvement

OBJECTIVE

There is not enough data on the effect of angiotensin-converting enzyme inhibitors (ACEIs)/angiotensin receptor blockers (ARBs) on lung involvement in patients with COVID-19 pneumonia and hypertension (HT). Our aim was to compare the lung involvement of the HT patients hospitalized for COVID-19 using ACEIs/ARBs with the patients taking other anti-HT medications.

METHODS

: Patients who have a diagnosis of HT among the patients treated for laboratory-confirmed COVID-19 between 31 March 2020 and 28 May 2020 were included in the study. One hundred and twenty-four patients were divided into two as ACEIs/ARBs group (n = 75) and non-ACEIs/ARBs group (n = 49) according to the anti-HT drug used. The chest CT involvement areas of these two groups were evaluated quantitatively by two observers including all lobes, and total severity score (TSS) was calculated. These TSS values were compared between drug groups and clinical groups.

RESULTS

In clinical classification; there were 4 (%3.2) asymptomatic, 5 (4.0%) mild type, 92 (74.1%) common type, 14 (11.3%) severe type, 9 (7.3%) critical type patients. ACEI/ARB group's TSS (mean±SD, 7.74 ± 3.54) was statistically higher than other anti-HT medication group (mean±SD, 4.40 ± 1.89) (p < .001). Likewise, severe-critical clinical type's TSS (mean±SD, 9.17 ± 3.44) was statistically higher than common type (mean±SD, 5.76 ± 3.07) (p < .001). Excellent agreement was established between the two blinded observers in the TSS measurements.

CONCLUSIONS

Quantitative evaluation of CT and TSS score can give an idea about the clinical classification of the patient. TSS is higher in ACEI/ARB group than non-ACEIs/ARBs group.

Radiologists versus Deep Convolutional Neural Networks: A Comparative Study for Diagnosing COVID-19

The reverse transcriptase polymerase chain reaction (RT-PCR) is still the routinely used test for the diagnosis of SARS-CoV-2 (COVID-19). However, according to several reports, RT-PCR showed a low sensitivity and multiple tests may be required to rule out false negative results. Recently, chest computed tomography (CT) has been an efficient tool to diagnose COVID-19 as it is directly affecting the lungs. In this paper, we investigate the application of pre-trained models in diagnosing patients who are positive for COVID-19 and differentiating it from normal patients, who tested negative for coronavirus. The study aims to compare the generalization capabilities of deep learning models with two thoracic radiologists in diagnosing COVID-19 chest CT images. A dataset of 3000 images was obtained from the Near East Hospital, Cyprus, and used to train and to test the three employed pre-trained models. In a test set of 250 images used to evaluate the deep neural networks and the radiologists, it was found that deep networks (ResNet-18, ResNet-50, and DenseNet-201) can outperform the radiologists in terms of higher accuracy (97.8%), sensitivity (98.1%), specificity (97.3%), precision (98.4%), and F1-score (198.25%), in classifying COVID-19 images.