Murder of the Chest X-ray

The value of early chest computed tomography (CT) was evaluated among severe community-acquired pneumonia (SCAP) patients. The study population consisted of 65 of 457 SCAP patients with concomitant chest CT and radiograph performed within 48 h of ICU admission. Each image pair was re-evaluated by two radiologists. The type of pneumonia, the presence of pleural fluid and atelectasis were assessed. Therapeutic and diagnostic procedures induced by CT results were analysed together with clinical, microbiological and outcome data. Alveolar pneumonia was observed in 72.3% of patients by radiograph and in 75.4% of patients by CT. Pleural fluid was detected via chest radiograph and CT in 17 (26.2%) and 41 cases (63.1%), (P < 0.001) and atelectasis in 10 (15.4%) and 22 cases (33.8%), (P = 0.002), respectively. In 34 patients (52.3%), the CT revealed 38 new findings (58.5%) not shown in plain chest radiograph. Out of these 34 patients, therapeutic interventions or procedures were performed in 26 (76.5%). The number of infected lobes correlated negatively with the lowest PaO2 /FiO2 ratio (ρ = -0.326, P = 0.008) for chest CT scans. Compared with chest radiograph, chest CT generated new findings in nearly 60% of SCAP patients, leading to new procedures or changes in medical treatment in nearly 75% of those patients. Chest CT better describes the pulmonary involvement and severity of oxygenation disorder compared to a plain chest radiograph.

Background: Coronavirus Disease 2019 (COVID-19) is associated with an increased risk for acute ischemic stroke but screening for COVID-19 based on clinical criteria or laboratory testing may be difficult in acute stroke evaluation. Chest computed tomographic (CT) scan may be another time-sensitive option for identification. of COVID-19 in such patients. Objective: We report our experience of incorporating chest CT scan in the initial neuroimaging protocol for evaluation of acute stroke patients. Methods: All acute stroke patients underwent chest CT scan concurrent to CT head, CT angiogram of head and neck and CT perfusion for 4 months. We identified patients who had chest CT scan findings that were suggestive of COVID-19 including bilateral, multilobar ground glass opacification with a peripheral or posterior distribution, and/or consolidation (mainly in the lower lobes). All patients subsequently underwent polymerase chain reaction (PCR) testing of nasopharyngeal swab with contact isolation until COVID-19 could be excluded. Results: A total of 224 consecutive patients (mean age 62.12 years±SD; 15.3) underwent acute stroke evaluation with a concurrent chest CT scan. The chest CT identified findings suggestive of COVID-19 in 11 (4.9%) patients. Subsequent PCR testing did not confirm the diagnosis of COVID-19 in any of the patient. Another 99 patients (44%) without any findings suggestive of COVID-19 on chest CT scan underwent PCR testing. PCR testing did not confirm the diagnosis of COVID-19 in any of the patients. Four patients (4.3%) with chest CT scan findings suggestive of COVID-19 were found to have an ischemic stroke while 7 patients (5.9%) with chest CT scan findings suggestive of COVID-19 did not have any ischemic stroke (stroke mimic). Conclusions: We found a very low yield for identifying COVID-19 in acute stroke patients by performing chest CT scan concurrent to standard acute stroke neuroimaging protocol.

This study aimed to explore the diagnostic value of chest computed tomography (CT) based on adaptive statistical iterative reconstruction (ASiR) in the optimal weight range for children with community-acquired pneumonia (CAP) and find the differences between chest CT and ultrasound in the diagnosis of CAP. 106 children who were diagnosed as CAP were selected as the research objects. All objects underwent CT and ultrasound scans to measure the noise of CT images after iterative reconstruction and the contrast-to-noise ratio (CNR) of the thoracic aorta and the back muscles. The correlation between ASiR weight, noise, and CNR was analyzed and compared further. In addition, the differences in imaging manifestations of lung consolidation, abnormal pleural line, alveolar interstitial syndrome, and pleural effusion were observed under the ultrasound and CT scans of the patients. The sensitivity, specificity, positive predicted value (PV), and negative PV of the CT scan and ultrasound were analyzed further for the diagnosis of CAP in children. The results showed that ASiR weight and image noise were extremely and negatively correlated ( P &lt; 0.001 ) and that CNR and ASiR weight were extremely and positively correlated ( P &lt; 0.001 ). 40–60% of ASiR image noise was in the middle position, and the muscle space and subcutaneous fat in CT images were clearer when ASiR was at 40% and 60%. 60.38% of children were diagnosed as CAP. The proportions of children with lung consolidation, interstitial changes, pleural effusion, and atelectasis after ultrasound and CT examination were not greatly different ( P &gt; 0.05 ). After ASiR, the sensitivity of CT in detecting the abnormal pleural line + B-line/consolidation feature was the highest, 93.75%. The results of ultrasound diagnosis were consistent with those of chest CT diagnosis ( P &lt; 0.01 ). This indicated that the sensitivity, specificity, positive PV, and negative PV of ASiR-based chest CT in the diagnosis of CAP were close to those of ultrasound.

Introduction: Chest computed tomography (CT) is used for the screening and diagnosis of COVID-19 pneumonia. The aim of the study is to study the prevalence of residual lung parenchymal CT changes in COVID-19 patients and its association with common predictors. Methods: Longitudinal study conducted at Shree Birendra Hospital in which diagnosed COVID-19 patients with initial CT scan showing COVID-19 related changes were retrospectively enrolled after ethics approval. The study period was from 15 February 2020 to 15 November 2021. Chest CT changes were evaluated and CT scan severity score (CT SS) assigned. Data was entered in Microsoft Excel and analysed in SPSS (Statistical Package for Social Sciences) version 16. Results: Males were more common (72.6%) among the 62 enrolled participants with the mean age 53.63 ± 15.05 years. Median interval between two scans was 137 days. Residual lung parenchymal CT changes were seen in 43 (69.4%) participants. Hypertension (25, 40.3%) and GGO (55, 88.7%) were the most common comorbidity and CT features respectively. The mean initial and final CT SS was 10.84 ± 5.87 and 3.55 ± 4.58 respectively (Total score 25, p = 0.001). Age was significantly associated with severity of CT score (p = 0.04). Conclusions: A large percentage of COVID-19 patients have chest CT changes after three months of initial chest CT scan. CT SS reduced significantly in the second scan. Similar studies with long term serial CT follow up of patients is recommended to be carried out in future.

There are limited data on chest computed tomography (CT) findings in the assessment of lung nodules among adult Aboriginal Australians. In this retrospective study, we assessed lung nodules among a group of adult Aboriginal Australians in the Northern Territory of Australia. Patients who underwent at least two chest CT scans between 2012 and 2020 among those referred to undergo lung function testing (spirometry) were included. Chest CT scans were assessed for the number, location, size and morphological characteristics of lung nodules. Of the 402 chest CTs assessed, 75 patients (18.7%) had lung nodules, and 57 patients were included in the final analysis with at least two CT scans available for assessment over a median follow-up of 87 weeks. Most patients (68%) were women, with a median age of 58 years and smoking history in 83%. The majority recorded only a single nodule 43 (74%). Six patients (10%) were diagnosed with malignancy, five with primary lung cancer and one with metastatic thyroid cancer. Of the 51 (90%) patients assessed to be benign, 64 nodules were identified, of which 25 (39%) resolved, 38 (59%) remained stable and one (1.8%) enlarged on follow-up. Nodules among patients with malignancy were typically initially larger and enlarged over time, had spiculated margins and were solid, showing no specific lobar predilection. Most lung nodules in Aboriginal Australians are likely to be benign. However, a proportion could be malignant. Further prospective studies are required for prognostication and monitoring of lung nodules in this population.

<sec> <title>BACKGROUND</title> Dyspnea is a common cause of hospitalization, posing diagnostic challenges among older adult patients with multimorbid conditions. Chest computed tomography (CT) scans are increasingly used in patients with dyspnea and offer superior diagnostic accuracy over chest radiographs but face limited use due to a shortage of radiologists. </sec> <sec> <title>OBJECTIVE</title> This study aims to develop and validate artificial intelligence (AI) algorithms to enable automatic analysis of acute CT scans and provide immediate feedback on the likelihood of pneumonia, pulmonary embolism, and cardiac decompensation. This protocol will focus on cardiac decompensation. </sec> <sec> <title>METHODS</title> We designed a retrospective method development and validation study. This study has been approved by the Danish National Committee on Health Research Ethics (1575037). We extracted 4672 acute chest CT scans with corresponding radiological reports from the Copenhagen University Hospital–Bispebjerg and Frederiksberg, Denmark, from 2016 to 2021. The scans will be randomly split into training (2/3) and internal validation (1/3) sets. Development of the AI algorithm involves parameter tuning and feature selection using cross validation. Internal validation uses radiological reports as the ground truth, with algorithm-specific thresholds based on true positive and negative rates of 90% or greater for heart and lung diseases. The AI models will be validated in low-dose chest CT scans from consecutive patients admitted with acute dyspnea and in coronary CT angiography scans from patients with acute coronary syndrome. </sec> <sec> <title>RESULTS</title> As of August 2025, CT data extraction has been completed. Algorithm development, including image segmentation and natural language processing, is ongoing. However, for pulmonary congestion, the algorithm development has been completed. Internal and external validation are planned, with overall validation expected to conclude in 2025 and the final results to be available in 2026. </sec> <sec> <title>CONCLUSIONS</title> The results are expected to enhance clinical decision-making by providing immediate, AI-driven insights from CT scans, which will be beneficial for both clinicians and patients. </sec> <sec> <title>INTERNATIONAL REGISTERED REPORT</title> DERR1-10.2196/77030 </sec>

Background and Aims Non-contrast chest computed tomography (CT) scans can accurately evaluate the type and extent of lung lesions. The aim of this study was to investigate the chest CT features associated with critical and non-critical patients with coronavirus disease 2019 (COVID-19). Methods A total of 1078 patients with COVID-19 pneumonia who underwent chest CT scans, including 169 critical cases and 909 non-critical cases, were enrolled in this retrospective study. The scans of all participants were reviewed and compared in two groups of study. In addition, the risk factors associated with disease in critical and non-critical patients were analyzed. Results Chest CT scans showed bilateral and multifocal involvement in most (86.4%) of the participants, with 97.6 and 84.3% reported in critical and non-critical patients, respectively. The incidences of pure consolidation (p=0.019), mixed ground-glass opacities (GGOs) and consolidation (p<0.001), pleural effusion (p<0.001), and intralesional traction bronchiectasis (p=0.007) were significantly higher in critical compared to non-critical patients. However, non-critical patients showed higher incidence of pure GGOs than the critical patients (p<0.001). Finally, the total opacity scores of the critical patients were significantly higher than those of non-critical patients (13.71±6.26 versus 4.86±3.52, p<0.001), with an area under the curve of 0.91 (0.88-0.94) for COVID-19 detection. Conclusions Our results revealed that the chest CT examination was an effective means of detecting pulmonary parenchymal abnormalities in the natural course of COVID-19. It can distinguish the critical patients from the non-critical patients (AUC=0.91), which is helpful for the judgment of clinical condition and has important clinical value for the diagnosis and follow-up of COVID-19 pneumonia.

Background: Diaphragmatic eventration is a rare disorder that is typically found by accident in asymptomatic patients with a raised hemidiaphragm on chest X-rays. Both acquired and developmental defects can cause diaphragmatic eventration. The imaging methods for diaphragmatic eventration are numerous. Chest radiography should be done when there is clinical suspicion; this can be further confirmed with a chest computed tomography (CT). Case presentation: We present a case of a 37 years old woman with a left sided diaphragm eventration presenting as dyspnea and epigastrical discomfort. The diagnosis was made with chest x-ray and then confirmed with a chest CT scans. Discussion: There are several modalities to choose in diaphragmatic imaging. Chest x-ray is the main imaging method for diagnosing diaphragm eventration, in rare cases, a eventration needs to be differentiated using CT or MRI. Other imaging methods such as fluoroscopy and ultrasonography may be used in some instances to assess diaphragm function. Conclusions: There are various methods available in the field of diaphragmatic imaging. Certain methods, like CT and plain chest radiographs, concentrate on the anatomic anomalies of the diaphragm that may indicate dysfunction. Some instruments, including fluoroscopy and ultrasonography, are more appropriate for functional imaging.

Context: Due to their availability and rapid turnaround time, the supplemental role of chest computed tomography (CT) scan and real-time polymerase chain reaction (RT-PCR) is growing for early diagnosis of patients with COVID-19. However, due to the low efficiency of viral nucleic acid detection as well as low specificity of chest CT scan for detecting COVID-19 pneumonia, both methods show incomplete clinical performance for proper COVID-19 disease diagnosis. The purpose of this review was to compare the clinical performance of two methods and to evaluate the diagnostic values of chest CT scan and RT-PCR for suspected COVID-19 patients. Evidence acquisition: We systemically searched PubMed, Cochrane, from December 2019 to the end of April 2020. Clinical research papers in goal fields that reviewed COVID-19 patients, whom chest CT scan, and PCR testing were performed together were included. Results: In total, we found 536 studies; and finally168 studies were shortlisted. Following title and abstract screening, we reached 83 studies based on the inclusion and exclusion criteria. Conducted screen by the full text covered 28 studies, which led to data extraction. By the full-text assessment of 28 included studies, we found 4486 assessed patients. Totally, 3164 patients had positive chest CT scans, and 3014 patients had positive PCR results. The finding showed that recent studies on the diagnostic performance of RT-PCR and chest CT scan have commonly been reported from China. Conclusion: The results from this review indicate that the chest CT scan should be used for symptomatic and hospitalized patients. Moreover, chest CT scan should not be used as a primary screening tool for diagnosing COVID-19. Application of RT-PCR as the first line diagnosis is still recommended.

To evaluate the diagnostic performance of a natural language processing (NLP) model in detecting incidental lung nodules (ILNs) in unstructured chest computed tomography (CT) reports. All unstructured consecutive reports of chest CT scans performed at a tertiary hospital between 2020 and 2021 were retrospectively reviewed (n = 21,542) to train the NLP tool. Internal validation was performed using reference readings by two radiologists of both CT scans and reports, using a different external cohort of 300 chest CT scans. Second, external validation was performed in a cohort of all random unstructured chest CT reports from 57 different hospitals conducted in May 2022. A review by the same thoracic radiologists was used as the gold standard. The sensitivity, specificity, and accuracy were calculated. Of 21,542 CT reports, 484 mentioned at least one ILN (mean age, 71 ± 17.6 [standard deviation] years; women, 52%) and were included in the training set. In the internal validation (n = 300), the NLP tool detected ILN with a sensitivity of 100.0% (95% CI, 97.6 to 100.0), a specificity of 95.9% (95% CI, 91.3 to 98.5), and an accuracy of 98.0% (95% CI, 95.7 to 99.3). In the external validation (n = 977), the NLP tool yielded a sensitivity of 98.4% (95% CI, 94.5 to 99.8), a specificity of 98.6% (95% CI, 97.5 to 99.3), and an accuracy of 98.6% (95% CI, 97.6 to 99.2). Twelve months after the initial reports, 8 (8.60%) patients had a final diagnosis of lung cancer, among which 2 (2.15%) would have been lost to follow-up without the NLP tool. NLP can be used to identify ILNs in unstructured reports with high accuracy, allowing a timely recall of patients and a potential diagnosis of early-stage lung cancer that might have been lost to follow-up.

There is an urgent need to find an effective and accurate method for triaging coronavirus disease 2019 (COVID-19) patients from millions or billions of people. Therefore, this study aimed to develop a novel deep-learning approach for COVID-19 triage based on chest computed tomography (CT) images, including normal, pneumonia, and COVID-19 cases. A total of 2,809 chest CT scans (1,105 COVID-19, 854 normal, and 850 non-3COVID-19 pneumonia cases) were acquired for this study and classified into the training set (n = 2,329) and test set (n = 480). A U-net-based convolutional neural network was used for lung segmentation, and a mask-weighted global average pooling (GAP) method was proposed for the deep neural network to improve the performance of COVID-19 classification between COVID-19 and normal or common pneumonia cases. The results for lung segmentation reached a dice value of 96.5% on 30 independent CT scans. The performance of the mask-weighted GAP method achieved the COVID-19 triage with a sensitivity of 96.5% and specificity of 87.8% using the testing dataset. The mask-weighted GAP method demonstrated 0.9% and 2% improvements in sensitivity and specificity, respectively, compared with the normal GAP. In addition, fusion images between the CT images and the highlighted area from the deep learning model using the Grad-CAM method, indicating the lesion region detected using the deep learning method, were drawn and could also be confirmed by radiologists. This study proposed a mask-weighted GAP-based deep learning method and obtained promising results for COVID-19 triage based on chest CT images. Furthermore, it can be considered a convenient tool to assist doctors in diagnosing COVID-19.

BACKGROUND: Speech recognition is becoming increasingly common in the national healthcare system. One of the first specialties to implement this technology on a large scale was radiology. However, the efficiency of voice input and its effect on the length of time required to complete medical records remain unresolved.&#x0D; AIM: To assess the efficiency of speech recognition in generating radiological protocols of different modalities and types.&#x0D; METHODS: The retrospective study was conducted at the Moscow Reference Center of the Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Department of Health. A total of 12,912 radiological reports on fluorography, mammography, chest computed tomography (CT), contrast-enhanced magnetic resonance imaging (MRI) of the brain, and contrast-enhanced CT of the abdomen and pelvis were included in the study by simple random sampling. The size of all samples exceeded 766 reports, calculated with regard to the size of the general population of over 100,000 reports. The Voice2Med software was used to fill in the radiological protocols. Intergroup comparison was performed using the MannWhitney U-test with a statistical significance level of 0.05.&#x0D; RESULTS: The average duration of generating fluorographic protocols in the keyboard and voice input groups was 189.9 s (0:03:09) and 236.2 s (0:03:56), respectively (p 0.0001). For mammographic reports, the duration was 387.1 s (0:06:27) and 444.8 s (0:07:24), respectively (p 0.0001). For radiographic reports, it amounted to 247.8 s (0:04:07) and 189.0 s (0: 03:09), respectively (p 0.0001), and for chest CT, it was 379.7 s (0:06:19) and 382.7 s (0:06:22), respectively (p=0.12). For MRI of the brain, the protocols were generated for 709.9 s (0:11:49) and 559.9 s (0: 09:19), respectively (p 0.0001), and for contrast-enhanced chest, abdominal, and pelvic CT scans, it took 2714.6 s (0:45:15) and 1778.4 s (0:29:38), respectively. Voice input slowed down the preparation time of mammographic and fluorographic protocols. This is due to the use of a structured electronic medical document in medical facilities to describe the results of the examinations. Speech recognition showed the greatest efficiency in generating MRI and CT protocols. Such reports contain a large number of pathological changes, both target and incidental findings, which requires a detailed description by the radiologist in the examination protocol.&#x0D; CONCLUSIONS: Speech recognition in generating radiological protocols showed different efficiency depending on the modality and type of the radiological protocol filled in using the voice input system. This approach is optimal for describing CT and MRI scans.

There are few original studies on the true role of normal chest radiograph (CXR) in exclusion of pulmonary conditions that may be associated with chronic cough. Thus, the aim of the study was to assess whether a plain CXR is a sufficient tool to exclude relevant pulmonary causes of chronic cough. A retrospective analysis of chest computed tomography (CT) scans in non-smoking patients with chronic cough and normal CXR was performed. The percentage of individuals in whom chest CT revealed relevant abnormalities was compared with the percentage of patients with irrelevant findings or normal chest CT scans. The negative predictive value (NPV) of the CXR in diagnosing the causes of chronic cough was calculated as a proportion of true negative CXRs (normal CXR AND irrelevant CT findings OR normal CT scan) to all negative CXRs (all patients, who had both a CXR and CT scan). The study group consisted of 59 adult patients with chronic cough, normal CXR and CT scan performed to diagnose the cause of chronic cough. In 21 patients (21/59, 36%), chest CT revealed abnormalities that were classified as relevant to chronic cough. The most frequent were: bronchiectasis (7/59, 11.9%), bronchial wall thickening (6/59, 10.2%) and mediastinal lymphadenopathy (5/59, 8.5%). The NPV of a CXR in diagnosing the causes of chronic cough was 64%. In conclusion, the NPV of CXR in diagnosing pulmonary causes of chronic cough is relatively low. Thus, plain CXR seems to be insufficient to exclude pulmonary diseases potentially associated with chronic cough.

Pregnant women and children sometimes had to undergo chest computed tomography (CT) scans during the Corona Virus Disease 2019 (COVID-19) pandemic. This study estimated the fetal and pediatric doses from chest CT scans. Organ doses and effective doses were calculated using the VirtualDose-CT software. Two groups of computational human phantoms, pregnant females and pediatric patients were used in this study. The results of doses normalized to volumetric CT Dose Index (CTDIvol) can be used universally for other dosimetry studies. Based on our calculations and international survey data of CTDIvol, fetal absorbed doses from COVID-19-related chest CT were found to be 0.04-0.36, 0.05-0.44 and 0.07-0.61mGy for 3, 6 and 9months of pregnancy, respectively. When the scan range is extended to the abdominal region, fetal doses increase by almost 4-fold. Effective doses for COVID-19-related chest CT were 1.62-13.77, 1.58-13.46, 1.57-13.33 and 1.29-10.98mSv for the newborn, 1-, 5- and 10-y-old children, respectively. In addition, the effects of specific axial scan ranges exceeding the thorax region were evaluated. Although doses from chest CT scans are small, such data allow radiologists and patients to be informed of the dose levels and ways to avoid unnecessary radiation.

POS-580 BURDEN OF COVID-19 IN A HEMODIALYSIS CENTER IN SUB-SAHARAN AFRICA