Digital Chest Radiography Research Articles

Estimation of age from digital chest radiographs in district Mandi, Himachal Pradesh

In forensic investigations estimation of age is a crucial part. Age estimation assists in narrow downing the search possibilities in the case of unidentified bodies and living individuals, it can also help in the determination of preparators and deciding the penalty for any criminal liability. Different body parts are often investigated at the crime scene, so estimating the age using different areas of the body is important. Hence, we conducted this study for age estimation from costal cartilage calcification. Only posterior-anterior (PA) digital chest radiographs are taken, and the first rib of the left side is evaluated. The degree of calcification and age showed a positive correlation; similarly, the value of Spearman’s rho showed significant results (for the whole sample =0.914, p < 0.01 (for male subjects was =0.925, p < 0.01. For females was =0.905, p < 0.01). The degree of calcification is useful in estimating age groups and becomes more determined with advancing age. Prediction of age using the radiological method gives the advantages of ease, inexpensiveness, rapidity, and high accuracy. In developing countries, this type of study will be very useful because infrastructure and lack of sources are major issues. The article provides a non-destructive method of age estimation in a forensic context that may act as a preliminary method of age estimation in mass disasters and dismembered bodies when a thoracic cage is only present.

Usefulness of copper filters in digital chest radiography based on the relationship between effective detective quantum efficiency and deep learning-based segmentation accuracy of the tumor area.

This study aimed to determine the optimal radiographic conditions for detecting lesions on digital chest radiographs using an indirect conversion flat-panel detector with a copper (Cu) filter. First, we calculated the effective detective quantum efficiency (DQE) by considering clinical conditions to evaluate the image quality. We then measured the segmentation accuracy using a U-net convolutional network to verify the effectiveness of the Cu filter. We obtained images of simulated lung tumors using 10-mm acrylic spheres positioned at the right lung apex and left middle lung of an adult chest phantom. The Dice coefficient was calculated as the similarity between the output and learning images to evaluate the accuracy of tumor area segmentation using U-net. Our results showed that effective DQE was higher in the following order up to the spatial frequency of 2 cycles/mm: 120kV + no Cu, 120kV + Cu 0.1mm, and 120kV + Cu 0.2mm. The segmented region was similar to the true region for mass-area extraction in the left middle lobe. The lesion segmentation in the upper right lobe with 120kV + no Cu and 120kV + Cu 0.1mm was less successful. However, adding a Cu filter yielded reproducible images with high Dice coefficients, regardless of the tumor location. We confirmed that adding a Cu filter decreases the X-ray absorption efficiency while improving the signal-to-noise ratio (SNR). Furthermore, artificial intelligence accurately segments low-contrast lesions.

The performance of digital chest radiographs in the detection and diagnosis of pulmonary nodules and the consistency among readers

Objective: To investigate the detection and diagnostic efficacy of chest radiographs for ≤30 mm pulmonary nodules and the factors affecting them, and to compare the level of consistency among readers. Methods: A total of 43 patients with asymptomatic pulmonary nodules who consulted in Cancer Hospital, Chinese Academy of Medical Sciences from 2012 to 2014 and had chest CT and X-ray chest radiographs during the same period were retrospectively selected, and one nodule ≤30 mm was visible on chest CT images in the whole group (total 43 nodules in the whole group). One senior radiologist with more than 20 years of experience in imaging diagnosis reviewed CT images and recording the size, morphology, location, and density of nodules was selected retrospectively. Six radiologists with different levels of experience (2 residents, 2 attending physicians and 2 associate chief physicians independently reviewed the chest images and recorded the time of review, nodule detection, and diagnostic opinion. The CT imaging characteristics of detected and undetected nodules on X images were compared, and the factors affecting the detection of nodules on X-ray images were analyzed. Detection sensitivity and diagnosis accuracy rate of 6 radiologists were calculated, and the level of consistency among them was compared to analyze the influence of radiologists' seniority and reading time on the diagnosis results. Results: The number of nodules detected by all 6 radiologists was 17, with a sensitivity of detection of 39.5%(17/43). The number of nodules detected by ≥5, ≥4, ≥3, ≥2, and ≥1 physicians was 20, 21, 23, 25, and 28 nodules, respectively, with detection sensitivities of 46.5%, 48.8%, 53.5%, 58.1%, and 65.1%, respectively. Reasons for false-negative result of detection on X-ray images included the size, location, density, and morphology of the nodule. The sensitivity of detecting ≤30 mm, ≤20 mm, ≤15 mm, and ≤10 mm nodules was 46.5%-58.1%, 45.9%-54.1%, 36.0%-44.0%, and 36.4% for the 6 radiologists, respectively; the diagnosis accuracy rate was 19.0%-85.0%, 16.7%-6.5%, 18.2%-80.0%, and 0%-75.0%, respectively. The consistency of nodule detection among 6 doctors was good (Kappa value: 0.629-0.907) and the consistency of diagnostic results among them was moderate or poor (Kappa value: 0.350-0.653). The higher the radiologist's seniority, the shorter the time required to read the images. The reading time and the seniority of the radiologists had no significant influence on the detection and diagnosis results (P>0.05). Conclusions: The ability of radiographs to detect lung nodules ≤30 mm is limited, and the ability to determine the nature of the nodules is not sufficient, and the increase in reading time and seniority of the radiologists will not improve the diagnostic accuracy. X-ray film exam alone is not suitable for lung cancer diagnosis.

Potentials of additional copper filtration on radiation dose and image quality for adults underwent digital chest X-ray imaging in Dubai Health Authority - UAE

ObjectiveTo explore the potentials of adding copper (Cu) filter on image quality and patient dose of adult patients underwent chest X-ray examination MethodsPatients were divided into four groups. Group 1, patients were exposed with no added Cu filter (standard or control), group 2 a 0.1 mm Cu filter was added, group 3 acquired with 0.2 mm Cu filter and group 4 performed with 0.3 mm Cu filter. Exposure index (EI), entrance surface dose (ESD) and dose area product (DAP) were recorded from the modality and retrospectively analyzed. The visual grading analysis score (VGAS) was used to evaluate image quality. Mann–Whitney T-Test and one-way ordinary ANOVA Test were used to evaluate statistical differences including gender-based findings. ResultsEI, ESD and DAP data for a total of 784 patients (422 male and 362 female) that underwent indirect digital chest radiography exam were collected. Image quality was maintained when adding 0.1 mm Cu filter achieved with ∼19% DAP reduction. Female showed a significant DAP reduction comparing to male registered in the same group. ConclusionsReducing dose when using indirect digital chest radiography is possible with no trade-off on image quality. No loss of image quality was reported, images were broadly comparable. Implications for practiceThis study highlights the importance of utilizing the additional copper filter in digital chest radiography.

Application of a light-weighted convolutional neural network for automatic recognition of coal workers' pneumoconiosis in the early stage

Objective: To construct and verify a light-weighted convolutional neural network (CNN), and explore its application value for screening the early stage (subcategory 0/1 and stage Ⅰ of pneumoconiosis) of coal workers' pneumoconiosis (CWP) from digital chest radiography (DR) . Methods: A total of 1225 DR images of coal workers who were examined at an Occupational Disease Prevention and Control Institute in Anhui Province from October 2018 to March 2021 were retrospectively collected. All DR images were collectively diagnosed by 3 radiologists with diagnostic qualifications and gave diagnostic results. There were 692 DR images with small opacity profusion 0/- or 0/0 and 533 DR images with small opacity profusion 0/1 to stage Ⅲ of pneumoconiosis. The original chest radiographs were preprocessed differently to generate four datasets, namely 16-bit grayscale original image set (Origin16), 8-bit grayscale original image set (Origin 8), 16-bit grayscale histogram equalized image set (HE16) and 8-bit grayscale histogram equalized image set (HE8). The light-weighted CNN, ShuffleNet, was applied to train the generated prediction model on the four datasets separately. The performance of the four models for pneumoconiosis prediction was evaluated on a test set containing 130 DR images using measures such as the receiver operating characteristic (ROC) curve, accuracy, sensitivity, specificity, and Youden index. The Kappa consistency test was used to compare the agreement between the model predictions and the physician diagnosed pneumoconiosis results. Results: Origin16 model achieved the highest ROC area under the curve (AUC=0.958), accuracy (92.3%), specificity (92.9%), and Youden index (0.8452) for predicting pneumoconiosis, with a sensitivity of 91.7%. And the highest consistency between identification and physician diagnosis was observed for Origin16 model (Kappa value was 0.845, 95%CI: 0.753-0.937, P<0.001). HE16 model had the highest sensitivity (98.3%) . Conclusion: The light-weighted CNN ShuffleNet model can efficiently identify the early stages of CWP, and its application in the early screening of CWP can effectively improve physicians' work efficiency.

ACCURACY OF PATIENTS AGE ASSESSMENT FROM FRONTAL CHEST RADIOGRAPHS

Background: Chest radiograph interpretation can be aided by knowing the patient'sage. Patients’ age is often assessed by radiologists from costal cartilage calcification assessment ofchest radiographs with anteroposterior views. Objectives: Therefore, we performed this research todetermine the precision of these radiologists in age estimation from CXRs. Methods: Ten radiologyexperts were selected to evaluate 3500 chest digital radiography with posterior-anterior images, inNational Institute of Child Health Karachi/JSMU from January 2022 to 2023. The most importantinclusion criteria were selecting normal or nearly normal radiographs in the study. Radiologists wereblind to patients actual age and were requested to determine patient’s age to closest decade from CXR.The respondents entered their responses in separate Excel spreadsheets. Results: A sum of 3,500CXRs was interpreted by radiologists, out of which 32.14% CXRs were correctly interpreteddetermining age at SD + 3 years, whereas 2375 CXRs were either overrated or underestimated(p&lt;0.05) and misinterpreted the age of patients (2375/3500; 67.85%). Conclusion: Overall ageassessment from a frontal CXR was only 32.14% in our study; considerable disparities were identifiedin age estimation using CXRs. Yet, it would be fascinating to expand this academic endeavor throughartificial intelligence tools and possibly improving the precision of patient age prediction from CXR.This approach for determining the age will be useful for screening tests in the prospect.

Automated identification of the preclinical stage of coal workers' pneumoconiosis from digital chest radiography using three-stage cascaded deep learning model

ObjectiveCoal workers’ pneumoconiosis (CWP) is a broad and serious occupational disease caused by inhaling coal dust, which can cause permanent physical injury. There is no effective treatment currently. Screening of the preclinical stage of CWP can earlier detect the risk of CWP. However, it is laborious for radiologists to screen for CWP from a large number of chest radiographs. Missed diagnoses and misdiagnoses often occur. To address this problem, a novel three-stage cascaded learning model for screening the preclinical stage of CWP on digital chest radiography (DR) was proposed in this paper. Methods1447 DRs of coal workers from two hospitals were used in the study. At the first stage, a YOLOv2 network was trained for detecting lung boxes on DR images. At the second stage, six convolutional neural network (CNN) models were trained to identify the preclinical stage of CWP. At the third stage, an ensemble learning (EL) model based on the soft voting mechanism was implemented to integrate the outputs of the six CNN models. ResultsTest results on the test set generated the area under the receiver characteristic operating curve (AUC), accuracy, sensitivity and specificity of 0.931, 84.7%, 75.0% and 95.7%, respectively. ConclusionThe proposed three-stage cascaded learning model could effectively screen coal workers at the preclinical stage. SignificanceThis is the first study for identifying the preclinical stage of pneumoconiosis on DR images, which could facilitate the secondary prevention of pneumoconiosis.

Validation of a Deep Learning Model for Detecting Chest Pathologies from Digital Chest Radiographs.

Purpose: Manual interpretation of chest radiographs is a challenging task and is prone to errors. An automated system capable of categorizing chest radiographs based on the pathologies identified could aid in the timely and efficient diagnosis of chest pathologies. Method: For this retrospective study, 4476 chest radiographs were collected between January and April 2021 from two tertiary care hospitals. Three expert radiologists established the ground truth, and all radiographs were analyzed using a deep-learning AI model to detect suspicious ROIs in the lungs, pleura, and cardiac regions. Three test readers (different from the radiologists who established the ground truth) independently reviewed all radiographs in two sessions (unaided and AI-aided mode) with a washout period of one month. Results: The model demonstrated an aggregate AUROC of 91.2% and a sensitivity of 88.4% in detecting suspicious ROIs in the lungs, pleura, and cardiac regions. These results outperform unaided human readers, who achieved an aggregate AUROC of 84.2% and sensitivity of 74.5% for the same task. When using AI, the aided readers obtained an aggregate AUROC of 87.9% and a sensitivity of 85.1%. The average time taken by the test readers to read a chest radiograph decreased by 21% (p < 0.01) when using AI. Conclusion: The model outperformed all three human readers and demonstrated high AUROC and sensitivity across two independent datasets. When compared to unaided interpretations, AI-aided interpretations were associated with significant improvements in reader performance and chest radiograph interpretation time.

Tuberculosis preventive treatment among individuals with inactive tuberculosis suggested by untreated radiographic abnormalities: a community-based randomized controlled trial

ABSTRACT Epidemiological and interventional studies have been rarely conducted among those with positive interferon-γ release assay (IGRA) results and radiologically inactive tuberculosis (TB) lesions on chest radiograph. This study aimed to estimate the effectiveness and safety of a six-week twice-weekly regimen (rifapentine plus isoniazid) among this key population in rural China. First, chest digital radiography was conducted to screen individuals with inactive TB lesions. Then, the identified participants were further evaluated and eligible participants with IGRA-positive results were included in subsequent randomized controlled trial (RCT). Of 44,500 recruited residents, 2,988 presented with radiographically inactive TB among 43,670 with complete results of chest radiography and questionnaire, and 28.61% (855/2,988) tested IGRA positive. Subsequently, 677 eligible participants were included in this RCT (345 in the preventive treatment group and 332 in the untreated control group). The treatment completion rate was 80.00% (276/345), and 11.88% (41/345) participants reported side-effects including two cases of hepatotoxicity (0.58%, 2/345). In the intention-to-treat analysis, the cumulative incidence rate of microbiologically confirmed active TB during a two-year follow-up was 1.16 (95% confidence interval [CI]: 0.03–2.29) in the preventive treatment group and 1.51 (95% CI: 0.20–2.82) in the control group (p = .485). Subgroup analyses showed that the protective rates were 55.42% (95% CI: 10.33–93.07%) and 80.17% (95% CI: 25.36–97.96%) for participants with fibrosis and for those aged ≥60 years, respectively. The expected treatment effect was not observed for the six-week regimen in this study. Future studies with sufficient sample size are needed to verify our findings.

Evaluation of the clinical utility of temporal subtraction using bone suppression processing in digital chest radiography

Rationale and objectivesTo evaluate the usefulness of temporal subtraction using the bone suppression method in digital chest radiography for the detection of pulmonary lesions. Materials and methodsThe images of 31 patients with pulmonary lesions and 19 normal cases were included in the study. Conventional and bone suppression temporal subtraction were performed in the 50 cases selected and used for an observer performance study. Five radiologists participated in the study, and the differences between using conventional and bone suppression temporal subtraction were assessed using jackknife free-response receiver operating characteristic analysis. ResultsThe average figure-of-merit values for all radiologists increased significantly using the bone suppression method, from 0.619 (conventional) to 0.696 (p = 0.032). The average sensitivity for detecting pulmonary lesions improved from 67.9% to 75.4%, and the average number of false-positive per case decreased from 0.336 to 0.252 using bone suppression temporal subtraction. ConclusionBone suppression temporal subtraction processing can assist with the detection of subtle pulmonary lesions in digital chest radiographs.

ПРИМЕНЕНИЕ НИЗКОДОЗОВОЙ ЦИФРОВОЙ РЕНТГЕНОГРАФИИ ДЛЯ ВЫЯВЛЕНИЯ НОВОЙ КОРОНАВИРУСНОЙ ИНФЕКЦИИ COVID-19 В УСЛОВИЯХ АМБУЛАТОРНО-ПОЛИКЛИНИЧЕСКОГО ЗВЕНА

A comparative analysis of the results of chest X-ray examinations was carried out in 346 patients with a clinically diagnosed novel coronavirus infection COVID-19, confirmed by laboratory tests. All patients underwent low-dose digital radiography and multi-slice computed tomography (MSCT) of the chest organs. Patients with radiological signs of pneumonia were identified, from which cases with interstitial changes were selected. As a result of low-dose digital chest radiography, radiological signs of acute interstitial pneumonia were revealed in 119 (52%) patients, and with MSCT, pneumonia was revealed in 308 (89%) patients. During dynamic monitoring on digital radiographs, post-Covid changes were detected in 224 (64.7%) patients, and with CT – in 308 patients.

Knowledge and Skills of Radiographers concerning “Digital Chest Radiography”

Aims: Chest X ray is the first choice for the investigation of cardiovascular and respiratory disease. Radiographer technologists are responsible for managing the radiation exposure to patients and providing adequate image quality according to the ALARA (As Low as Radiation Achievable) concept. The knowledge and skills of radiographers in performing chest radiography helps to improve the diagnosis of diseases. The aim of this study was to assess the knowledge and skills of Radiographers on digital chest radiography in medical imaging departments.

Dose optimization for digital chest radiography in phantom

Dose optimization for digital chest radiography in phantom

Algorithms for Screening for Active Tuberculosis among Individuals with Latent Tuberculosis Infection in a Rural Community in China.

Screening for active tuberculosis (TB) among individuals with latent tuberculosis infection (LTBI) is important for the initiation and evaluation of TB preventive treatment. The performances of different tools and their combinations had rarely been studied in community-level screening among individuals with LTBI in China. This study aimed to explore appropriate algorithms for screening for active TB among individuals with LTBI in rural China. Three sputum samples were collected from each participant for smear microscopy, culture, and an Xpert MTB/RIF assay. Chest digital radiography and TB symptoms were investigated as well. The performances of different testing algorithms were compared with that of sputum culture as the gold standard. Overall, 1,564 study participants with LTBI were investigated, with a final diagnosis of 20 TB cases by sputum culture. Compared with other tests, the Xpert MTB/RIF assay detected 80.00% (95% confidence interval [CI], 58.40% to 91.93%) of culture-positive cases, with the highest sensitivity. When tests were combined using "or," "and," or "step" algorithms, the highest sensitivity reached 90.00% (95% CI, 69.90% to 97.21%) for the combination of the Xpert MTB/RIF assay and chest radiography, but the positive predictive value (PPV) decreased to 22.22% (95% CI, 14.54% to 32.41%). The Xpert MTB/RIF assay alone showed the best agreement with sputum culture, with a kappa value of 0.840. Pathogen molecular detection alone showed good performance compared to the other algorithms, for ruling out active TB in general LTBI, but the high cost might be a challenge for scaling it up. Identifying those with a high risk for progression to TB more precisely and establishing a cost-effective screening algorithm deserve further exploration. IMPORTANCE Enhancing community-wide active case screening in target LTBI populations is important for achieving the early treatment of active TB, and ruling active TB out is a prerequisite for initiating preventive treatment. The current study evaluated the performances of multiple tests and their combinations in screening for active TB among individuals with LTBI at the community level. Compared with the classical "TB symptoms and chest radiography" algorithm, the application of Xpert MTB/RIF improved the sensitivity from 45% to 80%. When the Xpert MTB/RIF assay was combined with chest radiography, the sensitivity was further improved to 90.00%, which achieved the World Health Organization (WHO) target product profiles. However, the algorithm requires caution as the PPV decreased from 88.89% for Xpert MTB/RIF alone to 22.22% for the combination. Xpert MTB/RIF alone offered remarkable sensitivity without compromising the PPV but would have major resource implications. Thus, identifying target populations for LTBI treatment more precisely and developing cost-effective and high-throughput screening tools and algorithms deserve further efforts.

Can advanced edge enhancement software improve image quality to visualise tubes, catheters and wires in digital chest radiographs?

IntroductionThis study aimed to test whether Advanced Edge Enhancement (AEE) software could improve the localisation of tubes, catheters or wires, while also affecting the overall image quality in chest x-rays (CXR). MethodsIn total, 50 retrospective CXRs were included. All images were obtained utilising the Canon X-ray system (CANON/Arcoma Precision T3 DR System, Canon Europe, Amsterdam, NL) with a CXDI-810C wireless detector. A clinical image, plus three additional AEE algorithms were applied using post processing (two intensity variations 1 and 4) on all CXRs totalling 350 different images. Three radiologists evaluated the images using a subjective Absolute Visual Grading Analysis (VGA). The clinical images used in post processing were not applied as reference in the analysis. Each radiologist graded the images separately in a randomized order, with a score of three indicating suitability for diagnostic assessment. ResultsThe three AEE algorithms contributed to an overall improvement (average 16–49%) in visualisation of tube, catheter or wire on CXR images. The Mann–Whitney U tests showed a statistically significant (p < 0.05) improvement in contrast resolution and sharpness, indicating an increased ability to differentiate tubes, wires or catheters tips from surrounding tissues. For the noise criterion, not applying any AEE algorithm showed a significantly higher homogeneity in soft tissue (p < 0.001), reducing the ability to visualise soft tissue. The high-intensity catheter algorithm was the only algorithm to achieve a statistically significant (p = 0.017) increase in the ability to differentiate pulmonary tissues of similar density. ConclusionAn overall improvement in the visualisation of tube, catheter and wire placement was obtained using the three AEE-algorithms. The bone and catheter algorithms showed the highest consistency, with the small structure algorithm underperforming in resolution and low contrast resolution. In general, image noise increased regardless of algorithm type or applied intensity. The AEE-algorithms should therefore be seen as a supplementary tool to the clinical image protocol, while having the potential to improve image quality to specific clinical situations. Implications for practiceAEE filtered images appear to be a supplement to the current practice of using CXRs in the diagnosis in placement of catheters, tubes and wires in the chest region. The use of AEE-algorithms has the potential to improve the daily work in clinical practice, which serves the basis for further investigation of its effect on radiographic practices.

Postexposure progression of pneumoconiosis among former Appalachian coal miners.

The prevalence of pneumoconiosis among working United States underground coal miners has been increasing for the past two decades, with the highest rates of disease observed among miners in the central Appalachian states of Kentucky, Virginia, and West Virginia. Surveillance for this disease in the United States focuses on working coal miners, who continue to be occupationally exposed to dust. This study examines the radiographic evidence for postexposure progression of pneumoconiosis in a population of former coal miners no longer occupationally exposed to coal mine dust who were seen at a community radiology clinic in eastern Kentucky. Data were obtained and analyzed from clinical records of former coal miners who had a clinic encounter during January 1, 2017-August 1, 2019, a recorded final year of employment, and ≥2 postemployment digital chest radiographs. Radiographs were classified according to the International Labour Office guidelines by at least two B Readers. A final summary pneumoconiosis severity score (range, 0-13), accounting for both small and large opacities, was assigned to each chest radiograph. Progression was defined as an increase in severity score between a miner's radiographs over time. Data for 130 former coal miners were analyzed. All miners were male and most (n = 114, 88%) had worked primarily in Kentucky. Information on race/ethnicity was not available. The most common job types were roof bolters (n = 51, 39%) and continuous miner operators (n = 46, 35%). Forty-one (31.5%) miners had evidence of radiographic disease progression after leaving the workforce, with a median of 3.6 years between first and latest postretirement radiograph. A total of 80 (62%) miners had evidence of pneumoconiosis on their latest radiograph, and two-thirds (n = 53) of these were classified as progressive massive fibrosis (PMF), the most severe form of the disease. Postexposure progression can occur in former coal miners, emphasizing the potential benefits of continued radiographic follow-up postemployment. In addition to participating in disease screening throughout their careers to detect pneumoconiosis early and facilitate intervention, radiographic follow-up of former coal miners can identify new or progressive radiographic findings even after workplace exposure to respirable coal mine dust ends. Identification of progressive pneumoconiosis in former miners has potential implications for clinical management and eligibility for disability compensation.

Retracted: Chest digital dynamic radiography to detect changes in human pulmonary perfusion in response to alveolar hypoxia.

Hypoxic pulmonary vasoconstriction optimises oxygenation in the lung by matching the local-blood perfusion to local-ventilation ratio upon exposure to alveolar hypoxia. It plays an important role in various pulmonary diseases, but few imaging evaluations of this phenomenon in humans. This study aimed to determine whether chest digital dynamic radiography could detect hypoxic pulmonary vasoconstriction as changes in pulmonary blood flow in healthy individuals. Five Asian men underwent chest digital dynamic radiography before and after 60 sec breath-holding at the maximal inspiratory level in upright and supine positions. Alveolar partial pressure of oxygen and atmospheric pressure were calculated using the blood gas test and digital dynamic radiography imaging, respectively. To evaluate the blood flow, the correlation rate of temporal change in each pixel value between the lung fields and left cardiac ventricles was analysed. Sixty seconds of breath-holding caused a mean reduction of 26.7 ± 6.4 mmHg in alveolar partial pressure of oxygen. The mean correlation rate of blood flow in the whole lung was significantly lower after than before breath-holding (before, upright 51.5%, supine 52.2%; after, upright 45.5%, supine 46.1%; both P < 0.05). The correlation rate significantly differed before and after breath-holding in the lower lung fields (upright, 11.8% difference; supine, 10.7% difference; both P < 0.05). The mean radiation exposure of each scan was 0.98 ± 0.09 mGy. No complications occurred. Chest digital dynamic radiography could detect the rapid decrease in pulmonary perfusion in response to alveolar hypoxia. It may suggest hypoxic pulmonary vasoconstriction in healthy individuals.

Deep Learning Detection of Active Pulmonary Tuberculosis at Chest Radiography Matched the Clinical Performance of Radiologists.

Background The World Health Organization (WHO) recommends chest radiography to facilitate tuberculosis (TB) screening. However, chest radiograph interpretation expertise remains limited in many regions. Purpose To develop a deep learning system (DLS) to detect active pulmonary TB on chest radiographs and compare its performance to that of radiologists. Materials and Methods A DLS was trained and tested using retrospective chest radiographs (acquired between 1996 and 2020) from 10 countries. To improve generalization, large-scale chest radiograph pretraining, attention pooling, and semisupervised learning ("noisy-student") were incorporated. The DLS was evaluated in a four-country test set (China, India, the United States, and Zambia) and in a mining population in South Africa, with positive TB confirmed with microbiological tests or nucleic acid amplification testing (NAAT). The performance of the DLS was compared with that of 14 radiologists. The authors studied the efficacy of the DLS compared with that of nine radiologists using the Obuchowski-Rockette-Hillis procedure. Given WHO targets of 90% sensitivity and 70% specificity, the operating point of the DLS (0.45) was prespecified to favor sensitivity. Results A total of 165 754 images in 22 284 subjects (mean age, 45 years; 21% female) were used for model development and testing. In the four-country test set (1236 subjects, 17% with active TB), the receiver operating characteristic (ROC) curve of the DLS was higher than those for all nine India-based radiologists, with an area under the ROC curve of 0.89 (95% CI: 0.87, 0.91). Compared with these radiologists, at the prespecified operating point, the DLS sensitivity was higher (88% vs 75%, P < .001) and specificity was noninferior (79% vs 84%, P = .004). Trends were similar within other patient subgroups, in the South Africa data set, and across various TB-specific chest radiograph findings. In simulations, the use of the DLS to identify likely TB-positive chest radiographs for NAAT confirmation reduced the cost by 40%-80% per TB-positive patient detected. Conclusion A deep learning method was found to be noninferior to radiologists for the determination of active tuberculosis on digital chest radiographs. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by van Ginneken in this issue.

Early risk assessment in paediatric and adult household contacts of confirmed tuberculosis cases by novel diagnostic tests (ERASE-TB): protocol for a prospective, non-interventional, longitudinal, multicountry cohort study

IntroductionThe WHO End-TB Strategy calls for the development of novel diagnostics to detect tuberculosis (TB) earlier and more accurately. Better diagnostics, together with tools to predict disease progression, are critical...

Evaluation of the Diagnostic Quality of Chest Radiographs

Introduction: With the advancement of conventional radiography to digital, digital radiography of the chest is commonly performed these days. The role and importance of diagnostic quality of radiographs are to help the radiologists and the clinicians in the diagnosis of diseases and their management, as well as to prevent the misdiagnosis of any pathology. This study was an attempt to quantify the diagnostic quality of chest radiographs by evaluating the quality of depiction of the anatomical details as well as other technical factors. Method: A total of 450 chest radiographs, produced at Tribhuvan University Teaching Hospital, were selected for study over two months period. Five image quality criteria i.e. anatomical coverage, rotation, adequate penetration, adequate inspiration, and scapula out of lung fields were evaluated and tabulated. Results: Out of the 450 radiographs taken for study, only 22.2% of the radiographs fulfilled all the image quality criteria, the rest 77.8% either lacked one or more of the quality criteria. Conclusion: Many technical factors affect the image quality of digital chest radiographs. However, the radiographer/technologist should always try to maintain adequate image quality of the radiographs.