Posterior-anterior Chest Radiographs Research Articles

Optimal conversion coefficient from easily measurable dose to effective dose with consideration to radiation quality for posterior–anterior chest radiography

Effective dose is sometimes used to compare medical radiation exposure to patients and natural radiation for providing explanations about radiation exposure to patients, but its calculation is lengthy and requires dedicated measuring devices. The purpose of this study was to identify the most suitable conversion coefficient for conversion of easily measurable dose to effective dose in posterior–anterior chest radiography, and to evaluate its accuracy by direct measurement. We constructed an examination environment using Monte Carlo simulation, and evaluated the variation in conversion coefficients from incident air kerma (IAK), entrance-surface air kerma (ESAK), and air kerma-area product (KAP) to effective dose when the irradiation field size and radiation quality were changed. Effective doses were also measured directly using thermoluminescence dosimeters and compared with the effective dose obtained from conversion coefficients. The KAP conversion coefficient most effectively suppressed the effect of irradiation field size, and was then used to set conversion coefficients for various half-value layers. The optimal conversion coefficient was 0.00023 [mSv/(mGy·cm2)] at 120 kVp (half-value layer = 5.5 mmAl). Evaluation of the direct measurements obtained with various radiation qualities revealed that the accuracy of the conversion coefficient was maintained at ≤ 11%. The proposed conversion coefficient can be easily calculated even in facilities that do not have equipment for measuring effective dose, and might enable the use of effective dose for providing explanations about radiation exposure to patients.

Early diagnosis and meta-agnostic model visualization of tuberculosis based on radiography images

Despite being treatable and preventable, tuberculosis (TB) affected one-fourth of the world population in 2019, and it took the lives of 1.4 million people in 2019. It affected 1.2 million children around the world in the same year. As it is an infectious bacterial disease, the early diagnosis of TB prevents further transmission and increases the survival rate of the affected person. One of the standard diagnosis methods is the sputum culture test. Diagnosing and rapid sputum test results usually take one to eight weeks in 24 h. Using posterior-anterior chest radiographs (CXR) facilitates a rapid and more cost-effective early diagnosis of tuberculosis. Due to intraclass variations and interclass similarities in the images, TB prognosis from CXR is difficult. We proposed an early TB diagnosis system (tbXpert) based on deep learning methods. Deep Fused Linear Triangulation (FLT) is considered for CXR images to reconcile intraclass variation and interclass similarities. To improve the robustness of the prognosis approach, deep information must be obtained from the minimal radiation and uneven quality CXR images. The advanced FLT method accurately visualizes the infected region in the CXR without segmentation. Deep fused images are trained by the Deep learning network (DLN) with residual connections. The largest standard database, comprised of 3500 TB CXR images and 3500 normal CXR images, is utilized for training and validating the recommended model. Specificity, sensitivity, Accuracy, and AUC are estimated to determine the performance of the proposed systems. The proposed system demonstrates a maximum testing accuracy of 99.2%, a sensitivity of 98.9%, a specificity of 99.6%, a precision of 99.6%, and an AUC of 99.4%, all of which are pretty high when compared to current state-of-the-art deep learning approaches for the prognosis of tuberculosis. To lessen the radiologist’s time, effort, and reliance on the level of competence of the specialist, the suggested system named tbXpert can be deployed as a computer-aided diagnosis technique for tuberculosis.

Perbandingan Dosis Radiasi Pemeriksaan Radiografi Thoraks PA Menggunakan Faktor Eksposi: Standar dan High kVp Berdasarkan Database DRL Si-INTAN BAPETEN

The usage of high kV has yet to be debated, so it is not fully used in PA chest radiographic examinations. However, The World Health Organization (WHO) and the Commission of European Communities (CEC) recommend using high kV, which can reduce the radiation dose to a minimum while maintaining the quality of the radiographic image. This study aimed to analyze the radiation dose for posterior-anterior (PA) chest radiography examinations using exposure factors: standard and high kV. This research method uses a retrospective study of the results of PA chest radiography examinations database diagnostic reference level (DRL) BAPETEN between 2018-2022. The PA chest examination data are the procedure, patient position, age, body weight, exposure factors (kVp and mAs), and ESAK dose (mGy). Then, use a standard exposure factor between 60-75 kVp and tube current-time of 6-12 mAs, while the exposure factor is high kV between 90-109 kVp and tube current-time of 0.7-4 mAs. After that, statistical analysis was carried out using IBM SPSS version 26 and Microsoft Excel. Determination of the average value and percentage difference in radiation dose of standard exposure factors and high kV. Then, proceed with statistical tests mann-whitney to determine the difference in the average radiation dose value between standard and exposure factors high kVp. The study showed that the average radiation dose for the standard kV exposure factor for the PA chest radiography examination was 0.179 mGy, while the high kV was 0.146 mGy. The dose difference between standard kV and high kV is 18.43%, with exposure factor radiation dose high kV lower than the standard exposure factor. For statistical test results, mann-whitney results p-value < 0,000. High kV could be a procedure to optimize exposure factors for PA chest radiography examination. Keywords: Exposure factors, High kV, Optimization, DRL, Chest PA

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.

Validation of a Deep Learning–Based Model to Predict Lung Cancer Risk Using Chest Radiographs and Electronic Medical Record Data

Lung cancer screening with chest computed tomography (CT) prevents lung cancer death; however, fewer than 5% of eligible Americans are screened. CXR-LC, an open-source deep learning tool that estimates lung cancer risk from existing chest radiograph images and commonly available electronic medical record (EMR) data, may enable automated identification of high-risk patients as a step toward improving lung cancer screening participation. To validate CXR-LC using EMR data to identify individuals at high-risk for lung cancer to complement 2022 US Centers for Medicare & Medicaid Services (CMS) lung cancer screening eligibility guidelines. This prognostic study compared CXR-LC estimates with CMS screening guidelines using patient data from a large US hospital system. Included participants were persons who currently or formerly smoked cigarettes with an outpatient posterior-anterior chest radiograph between January 1, 2013, and December 31, 2014, with no history of lung cancer or screening CT. Data analysis was performed between May 2021 and June 2022. CXR-LC lung cancer screening eligibility (previously defined as having a 3.297% or greater 12-year risk) based on inputs (chest radiograph image, age, sex, and whether currently smoking) extracted from the EMR. 6-year incident lung cancer. A total of 14 737 persons were included in the study population (mean [SD] age, 62.6 [6.8] years; 7154 [48.5%] male; 204 [1.4%] Asian, 1051 [7.3%] Black, 432 [2.9%] Hispanic, 12 330 [85.2%] White) with a 2.4% rate of incident lung cancer over 6 years (361 patients with cancer). CMS eligibility could be determined in 6277 patients (42.6%) using smoking pack-year and quit-date from the EMR. Patients eligible by both CXR-LC and 2022 CMS criteria had a high rate of lung cancer (83 of 974 patients [8.5%]), higher than those eligible by 2022 CMS criteria alone (5 of 177 patients [2.8%]; P < .001). Patients eligible by CXR-LC but not 2022 CMS criteria also had a high 6-year incidence of lung cancer (121 of 3703 [3.3%]). In the 8460 cases (57.4%) where CMS eligibility was unknown, CXR-LC eligible patients had a 5-fold higher rate of lung cancer than ineligible (127 of 5177 [2.5%] vs 18 of 2283 [0.5%]; P < .001). Similar results were found in subgroups, including female patients and Black persons. Using routine chest radiographs and other data automatically extracted from the EMR, CXR-LC identified high-risk individuals who may benefit from lung cancer screening CT.

P380 THE REEL‘S SYNDROME : A RARE COMPLICATION AFTER PACEMAKER IMPLANTATION

Abstract We report the case of a 83–year–old male who underwent single chamber pacemaker implantation in another center for bradyarrhythmia (atrial fibrillation) symptomatic for syncope. A ventricular lead was implanted in the apical septum, the post–procedural posterior–anterior chest radiograph showed correct placement of the leads and generator. Approximately 4 months after the implant, the patient came to our observation for repeated electric pulsations in his left shoulder. The patient was affected by mild dementia and was asymptomatic except for the presenting symptomatology, in particular he denied a history of syncope. The ECG showed arrhythmia due to atrial fibrillation, ventricular undersensing, and lack of ventricular capture (Figure 1). The chest X–ray showed the complete dislodgement of the ventricular lead tip apparently into the vein secondary to the rotation of the pulse generator on its transverse axis with consecutive coiling of the lead (Fig. 2). These findings were consistent with Reel syndrome The patient was admitted to undergo a lead repositioning attempt in consideration of the recent first implant. During the surgery, however, it was shown that the lead was completely displaced from the venous system, near the pectoral muscle. This extreme dislocation of the lead has been related to the electric pulsations felt by the patient. We proceeded with a new re–implantation of the lead and removal of the previous one. The lead was firmly tied to the pectoral muscle with 2 silk points with slight traction control of optimal fixation. Reel‘s syndrome is a variant of the better known Twiddler syndrome, characterized by the coiling of catheters along the transverse axis of the pacemaker. It is often a complication due to the manipulation of the generator by the patient, unlike Twiddler syndrome it occurs early and the leads remain intact. This syndrome is one of the most serious complications of pacemaker or defibrillator implants, it can be potentially fatal due to lack of therapy in the case of defibrillators, or asystole in pacemaker–dependent patients.

Automated quality assessment of chest radiographs based on deep learning and linear regression cascade algorithms.

Develop and evaluate the performance of deep learning and linear regression cascade algorithms for automated assessment of the image layout and position of chest radiographs. This retrospective study used 10 quantitative indices to capture subjective perceptions of radiologists regarding image layout and position of chest radiographs, including the chest edges, field of view (FOV), clavicles, rotation, scapulae, and symmetry. An automated assessment system was developed using a training dataset consisting of 1025 adult posterior-anterior chest radiographs. The evaluation steps included: (i) use of a CNN framework based on ResNet - 34 to obtain measurement parameters for quantitative indices and (ii) analysis of quantitative indices using a multiple linear regression model to obtain predicted scores for the layout and position of chest radiograph. In the testing dataset (n = 100), the performance of the automated system was evaluated using the intraclass correlation coefficient (ICC), Pearson correlation coefficient (r), mean absolute difference (MAD), and mean absolute percentage error (MAPE). The stepwise regression showed a statistically significant relationship between the 10 quantitative indices and subjective scores (p < 0.05). The deep learning model showed high accuracy in predicting the quantitative indices (ICC = 0.82 to 0.99, r = 0.69 to 0.99, MAD = 0.01 to 2.75). The automatic system provided assessments similar to the mean opinion scores of radiologists regarding image layout (MAPE = 3.05%) and position (MAPE = 5.72%). Ten quantitative indices correlated well with the subjective perceptions of radiologists regarding the image layout and position of chest radiographs. The automated system provided high performance in measuring quantitative indices and assessing image quality. • Objective and reliable assessment for image quality of chest radiographs is important for improving image quality and diagnostic accuracy. • Deep learning can be used for automated measurements of quantitative indices from chest radiographs. • Linear regression can be used for interpretation-based quality assessment of chest radiographs.

Implant screwdriver aspiration during dental procedure at a geriatric patient: a case report

The aim of this case report is to present a geriatric patient who had aspirated an implant screwdriver. An 82-year-old male patient involuntarily closed his mouth during the healing cap placement phase of implant treatment, and the implant screwdriver disappeared through the pharynx. As the patient exhibited no signs of airway obstruction, it was considered that the implant screwdriver might have passed into the gastrointestinal tract. However, no foreign object was detected in the esophageal mucosa. Therefore, the procedure was repeated using C arm fluoroscopy for guidance. The examination revealed the presence of the implant screwdriver in the bifurcation of the left lower lobe bronchus, which was confirmed by posterior-anterior chest radiography. The following day, the patient underwent a flexible bronchoscopy, however the screwdriver could not be retrieved during the procedure. Further treatment was refused by the patient. After one month, the implant screwdriver was expelled through the gastrointestinal tract. At the three-year follow-up, there was no evidence of a late complication. Aspiration of screwdrivers and treatment failures of these complications are reported a small number of cases in the geriatric patients. Therefore, documentation of the rare cases is necessary to provide valuable information.

A deep learning-based automatic analysis of cardiovascular borders on chest radiographs of valvular heart disease: development/external validation.

Cardiovascular border (CB) analysis is the primary method for detecting and quantifying the severity of cardiovascular disease using posterior-anterior chest radiographs (CXRs). This study aimed to develop and validate a deep learning-based automatic CXR CB analysis algorithm (CB_auto) for diagnosing and quantitatively evaluating valvular heart disease (VHD). We developed CB_auto using 816 normal and 798 VHD CXRs. For validation, 640 normal and 542 VHD CXRs from three different hospitals and 132 CXRs from a public dataset were assigned. The reliability of the CB parameters determined by CB_auto was evaluated. To evaluate the differences between parameters determined by CB_auto and manual CB drawing (CB_hand), the absolute percentage measurement error (APE) was calculated. Pearson correlation coefficients were calculated between CB_hand and echocardiographic measurements. CB parameters determined by CB_auto yielded excellent reliability (intraclass correlation coefficient > 0.98). The 95% limits of agreement for the cardiothoracic ratio were 0.00 ± 0.04% without systemic bias. The differences between parameters determined by CB_auto and CB_hand as defined by the APE were < 10% for all parameters except for carinal angle and left atrial appendage. In the public dataset, all CB parameters were successfully drawn in 124 of 132 CXRs (93.9%). All CB parameters were significantly greater in VHD than in normal controls (all p < 0.05). All CB parameters showed significant correlations (p < 0.05) with echocardiographic measurements. The CB_auto system empowered by deep learning algorithm provided highly reliable CB measurements that could be useful not only in daily clinical practice but also for research purposes. • A deep learning-based automatic CB analysis algorithm for diagnosing and quantitatively evaluating VHD using posterior-anterior chest radiographs was developed and validated. • Our algorithm (CB_auto) yielded comparable reliability to manual CB drawing (CB_hand) in terms of various CB measurement variables, as confirmed by external validation with datasets from three different hospitals and a public dataset. • All CB parameters were significantly different between VHD and normal control measurements, and echocardiographic measurements were significantly correlated with CB parameters measured from normal control and VHD CXRs.

Evaluation of Entrance Surface Air Kerma in Patients During PA Chest Radiography Using CALDose Program in Al Najaf Governorate Hospitals

Due to its value in providing diagnostic information, large Figure of chest X-rays conducted everywhere. This therefore necessitate assessing patients’ dose to avoid any potential harm. This work is aimed at evaluating the Entrance Surface Air Kerma (ESAK) for adult patients undergoing chest radiographic examination (posterior - anterior (PA)) in Al Najaf (Iraq). The ESAK were evaluated for 186 patients utilizing CALDose program. The tube output measurements were carried out using Rad-Check dosimeter. Patient demographic data was also recorded (height and weight). Nine X-ray units were considered in this research (i.e. Al-Furat hospital (AFH), Al Sajad General Hospital (ASGH), Al-Hakeem hospital (AHH), Al-Zahra hospital (AZH), Al-Sadder hospital (ASH), Al Hydria General Hospital (AHGH), Middle Euphrates cancer center (MECC), Al Manzrah General Hospitals (AMGH) and Al Mishkhab Hospital (AMH)). The resulted data revealed that the ESAK for the considered hospitals AFH, ASH, ASGH, AHH, AZH, AHGH, MECC, AMGH and AMH are 0.67, 0.069, 0.59, 0.05, 0.79, 0.76, 0.48, 0.21 and 0.18, respectively with an overall average of 0.425 ± (0.299) mGy. In AZH, the ESAK was generally higher than those of other X-ray units of this study. Finally, performing a regular quality control (QC) checking with dose audit is largely advised

Analysis of Potential for User Errors in Mobile Deployment of Radiology Deep Learning for Cardiac Rhythm Device Detection.

We examine how convolutional neural networks (CNNs) for cardiac rhythm device detection can exhibit failures in performance under suboptimal deployment scenarios and examine how medically adversarial image presentation can further impair neural network performance. We validated the publicly available Pacemaker-ID web server and mobile app on 43 local hospital emergency department (ED) cases of patients presenting with a cardiac rhythm device on anterior-posterior (AP) chest radiograph and assessed performance using Cohen's kappa coefficient for inter-rater reliability. To illustrate adversarial performance concerns, we then produced example CNN models using the 65,379 patient MIMIC-CXR chest radiograph retrospective database and evaluated performance with area under the receiver operating characteristic (AUROC). In retrospective review of 43 patients with cardiac rhythm devices on AP chest radiographs during our study period (January 1, 2020 to March 1, 2020), 74.4% (32/43) had device manufacturer information readily available within the electronic medical record. A total of 25.6% of patients (11/43) did not have this information documented in the patient chart and could ostensibly benefit from CNN-based identification of device manufacturer. For patients with known device manufacturer, the Pacemaker-ID prediction was accurate in 87.5% of cases (28/32). Mobile app accuracy varied from 62.5 to 93.75% depending on image capture settings and presentation. Cohen's kappa coefficient varied from 0.448 to 0.897 depending on mobile image capture conditions. For our additional analysis of medically adversarial performance failures with a DenseNet121 trained on MIMIC-CXR images, we showed that an AUROC of 0.9807 ± 0.0051 could be achieved on an example testing dataset while masking a 30% false positive rate in identification of cardiac rhythm devices versus clinically distinct entities such as vagal nerve stimulators. Despite the promise of CNN approaches for cardiac rhythm device analysis on chest radiographs, further study is warranted to assess potential for errors driven by user misuse when deploying these models to mobile devices as well as for cases when performance can be impaired by the presence of other support apparatuses.

A semantic contour based segmentation of lungs from chest x‐rays for the classification of tuberculosis using Naïve Bayes classifier

AbstractTuberculosis (TB) is an infectious disease that primarily affects the lungs. If the TB is left undiagnosed in its early stage, it may affect other body parts leading to sudden death. A posterior anterior chest radiographs are used to diagnose pulmonary TB. This work presents a novel automatic system for the early detection of pulmonary TB. Initially, the Chan‐Vese active contour model is generated to segment the lung region from the preprocessed chest x‐rays. Different textural, statistical, and morphological features are extracted from the segmented lungs. Finally, the pertinent feature vector is considered for the classification of chest x‐rays using the Naïve Bayes classifier (NBC). The NBC is trained using 10‐fold cross validation technique. The proposed method achieves an average accuracy, the area under the curve, specificity, and sensitivity as 95.5%, 98%, 93.3%, and 94.6% respectively, when tested on different available datasets. Comparison results reveals that the Naïve Bayes classifier outperforms than the multinomial Naïve Bayes classifier and Bernoulli's Naïve Bayes classifier in classifying the chest x‐rays into healthy and TB.

Proposing a novel multi-instance learning model for tuberculosis recognition from chest X-ray images based on CNNs, complex networks and stacked ensemble.

Mycobacterium Tuberculosis (TB) is an infectious bacterial disease. In 2018, about 10 million people has been diagnosed with tuberculosis (TB) worldwide. Early diagnosis of TB is necessary for effective treatment, higher survival rate, and preventing its further transmission. The gold standard for tuberculosis diagnosis is sputum culture. Nevertheless, posterior-anterior chest radiographs (CXR) is an effective central method with low cost and a relatively low radiation dose for screening TB with immediate results. TB diagnosis from CXR is a challenging task requiring high level of expertise due to the diverse presentation of the disease. Significant intra-class variation and inter-class similarity in CXR images makes TB diagnosis from CXR a more challenging task. The main aim of this study is tuberculosis recognition from CXR images for reducing the disease burden. For this purpose, a novel multi-instance classification model is proposed in this study which is based on CNNs, complex networks and stacked ensemble (CCNSE). A main advantage of CCNSE is not requiring an accurate lung segmentation to localize the suspicious regions. Several overlapping patches are extracted from each CXR image. Features describing each patch are obtained by CNNs and then the feature vectors are clustered. Local complex networks (LCN) and global ones (GCN) of the cluster representatives are formed and feature engineering on LCN (GCN) generates other features at image-level (patch-level and image-level). Global clustering on these feature sets is performed for all patches. Each patch is assigned the purity score of its corresponding cluster. Patch-level features and purity scores are aggregated for each image. Finally, the images are classified with a proposed stacked ensemble classifier to normal and TB classes. Two datasets are used in this study including Montgomery County CXR set (MC) and Shenzhen dataset (SZ). MC/SZ includes 138/662 chest X-rays (CXR) from which 80 and 58/326 and 336 images belong to normal/TB classes, respectively. The experimental results show that the proposed method with AUC of 99.00 ± 0.28/98.00 ± 0.16 for MC/SZ and accuracy of 99.26 ± 0.40/99.22 ± 0.32 for MC/SZ with fivefold cross validation strategy is superior than the compared ones for diagnosis of TB from CXR images. The proposed method can be used as a computer-aided diagnosis system to reduce the manual time, effort and dependency to specialist's expertise level.

Isolated Hyponatremic Dehydration in the Setting of COVID-19–Associated Gastroenteritis in a Toddler

Isolated Hyponatremic Dehydration in the Setting of COVID-19–Associated Gastroenteritis in a Toddler

Determination of Diagnostic Reference Levels for Chest Examination for Windhoek, Namibia

The widespread use of X-ray examination has improved lives worldwide and this evolution of imaging has also resulted in a significant increase in the population’s exposure to ionizing radiation. This exposure can be minimised by the setting up Diagnostic Reference Levels (DRLs). DRLs are exposure level that serve as guide for standard regions of interest and are recommended by the International Atomic Energy Agency for member states to apply within the context of the national legislation. Namibia Radiation Protection and Waste Disposal Regulations also mandate the National Radiation Protection Authority to determine guidance levels as a condition of a license. Management of patient dose and determination of DRLs are important part of quality control programsin X-ray diagnostic departments as it forms an efficient and powerful standard for minimising radiation dosage to patients. Since Namibia has not yet established DRLs, the aim of this study was to develop DRLs for posterior anterior (PA) chest examinationin Windhoek, Namibia. Quality control tests were done on the X-ray machines using a Xi-Unfors radiation meter. Entrance skin doses (ESDs) were obtained from 120 patients that were referred for chest examination at the six selected facilities in Windhoek. The following parameters were measured: kilovoltage peak (kVp), milliampere seconds (mAs), the focus to bucky distance (FBD) and focus to surface distance (FSD). The ESDs were then calculated. The average ESD was found to be 0.47 mGy, ranging from 0.14 to 1.3 mGy. The study provided baseline information on ESD’s for PA chest radiography. The ESD’s in this study was above the ESD’s recommended by the IAEA which is 0.2mGy. This was attributed to the use of high kVp and mAs and a short FBD at some facilities. The results of this study, with more data expected to be collected by the National Radiation Protection Authority (NRPA) will provide a useful baseline to establish Namibia’s DRLs.

Mirrored Ribs: A Sign for Pneumothorax in Neonates*

To evaluate if "mirrored ribs"-a mirroring of chest structures-is a sign for pneumothorax in lung ultrasound in neonates. Retrospective study. Medical University Vienna/General Hospital, Vienna, Austria. All neonates admitted with clinical signs of pneumothorax between July 2018 and February 2019. Chest radiograph and lung ultrasound in succession. Anterior-posterior chest radiograph and lung ultrasound were compared. "Mirrored ribs" were defined as mirroring of at least one rib and intercostal muscles in B-mode in the absence of B-lines. Twenty-nine cases in 26 patients were considered. Pneumothorax was diagnosed in 55% of cases using reported signs for pneumothorax in lung ultrasound and in 31% using chest radiograph. Lung ultrasound identified all pneumothoraces visible in chest radiograph. "Mirrored ribs" were visible in all patients with signs for pneumothorax and in none without signs for pneumothorax in lung ultrasound, representing a specificity and sensitivity of 100% compared with reported signs for pneumothorax in lung ultrasound. The occurrence of "mirrored ribs" in lung ultrasound in combination with absence of lung sliding, absence of B-lines and lung point might facilitate the rapid recognition of pneumothorax in neonates.

Estimation of age in unidentified patients via chest radiography using convolutional neural network regression.

Patient age has important clinical utility for refining a differential diagnosis in radiology. Here, we evaluate the potential for convolutional neural network models to predict patient age based on anterior-posterior chest radiographs for instances where patients may present for emergency services without the ability to provide this identifying information. We used the CheXpert dataset of 224,316 chest radiographs from 65,240 patients to train CNN regression models with ResNet50 and DenseNet121 architectures for prediction of patient age based on anterior-posterior (AP) view chest radiographs. We evaluate these models on both the CheXpert validation dataset and a local hospital case in which a patient initially presented for emergency services intubated and without identification. Mean absolute error (MAE) for our ResNet50 model on the CheXpert dataset is 4.94years for predicting patient age based on AP chest radiographs. MAE for our DenseNet121 model is 4.69years. Both models have a correlation coefficient between true patient ages and predicted ages of 0.944. Wilcoxon rank-sum comparison between the two model architectures shows no significant difference (p = 0.33), but both show improvement over a baseline demographic-driven estimation (p < 0.001). For circumstances in which patients present for healthcare services without readily accessible identification such as in the setting trauma or altered mental status, CNN regression models for age prediction have potential clinical utility for refining estimates related to this missing patient information.

Gambaran Radiografi Tuberkulosis Paru Multidrug-Resistant: Studi Retrospektif di Rumah Sakit Umum Dr. Soetomo Surabaya

Background: Multidrug-resistant pulmonary tuberculosis (MDR-TB) is a major health problem worldwide. Prompt diagnosis is necessary for insuring appropriate therapy to lower morbidity and mortality, as well as to prevent disease transmission. Determination of drug-resistance patterns through microbiological examination is sometimes challenging, especially when the sputum acid-fast bacilli smear is negative. Therefore, it is worthwhile to investigate whether there may be radiographic patterns suggesting MDR-TB infection. The objective of this study was to evaluate the radiographic features of MDR-TB. Methods: From September 2015 to March 2018, 167 patients with microbiologically proven MDR-TB were enrolled in the study. All radiographs were obtained before the patients received MDR-TB chemotherapy regimen. Posterior-anterior chest radiography was performed using digital radiography system (Hitachi Radnext50 500mAs) and retrospectively reviewed as digital raw data. Results: Based on severity category, 60% had severe lesion, 31% had moderate lesion, and only 9% had mild lesion. The most frequently observed findings were ground glass opacity or consolidation, noted in 96% patients, with bilateral lung involvement in 81% patients; fibrosis (95%), cavity (78%; 87% of which were multiple), interstitial opacities (53%), pleural thickening and mediastinal shift (59%). Other radiological findings were calcification (16%), emphysema (13%), lung destruction (12%), atelectasis (10%), nodule (8%), bullae (8%), bronchiectasis (5%), miliary pattern (1%), pleural effusion (25%), pneumothorax (1%), and hilar lymphadenopathy (14%) which predominantly unilateral. Conclusion: The presence of severe pulmonary lesion, ground glass opacity or consolidation with bilateral lung involvement, fibrosis, multiple cavities, interstitial opacities, pleural thickening, and mediastinal shift are the main features of MDR-TB.

Sternal-Sparing Approach for LVAD Implantation Allows for a More Consistent Inflow Cannula Angle

Prior study has suggested an association between angulation of the inflow cannula and outcomes following left ventricular assist device (LVAD) implantation. A potential benefit of the sternal-sparing LVAD implantation technique is superior visualization of the apex in its natural anatomic position. Our hypothesis was the sternal-sparing technique would result in more consistent angle of LVAD implantation. This single-center, retrospective review included consecutive patients implanted from September 2015 through September 2019. Patients were stratified via surgical technique: sternal-sparing or median sternotomy. The coronal angle of the inflow cannula relative to the horizontal line was measured on the first available postoperative anterior-posterior chest radiograph (Figure 1). All measurements were calculated by trained radiologists. A randomly selected subgroup of patients was repeated by multiple readers to assess interrater reliability. Of the 161 patients included in the study, 102 (63%) were implanted via the sternal-sparing approach. Preoperative patient characteristics were similar between cohorts. The inflow cannula angle was significantly different between cohorts (Figure 1, p=0.020). The sternal-sparing cohort had a more consistent angle (median [IQR], 18 [6-29] degrees) compared to the sternotomy patients (25 [10-42] degrees, p=0.020). Significantly more sternal-sparing patients had an angle ≤65 degrees (100% vs 93%, p=0.017), which has previously been associated with improved patient outcomes with a different model pump. Our data suggests the angle of the inflow cannula after LVAD implantation is more consistently reproduced when utilizing the sternal-sparing approach compared to median sternotomy. Further research is needed to examine the relationship between inflow cannula angle and patient outcomes to determine the ideal inflow cannula angle for the fully magnetically levitated pump.

4-month moxifloxacin containing regimens in the treatment of patients with sputum-positive pulmonary tuberculosis in South India - a randomised clinical trial.

Shortening tuberculosis (TB) treatment duration is a research priority. We tested the efficacy and safety of 3- and 4-month regimens containing moxifloxacin in a randomised clinical trial in pulmonary TB (PTB) patients in South India. New, sputum-positive, adult, HIV-negative, non-diabetic PTB patients were randomised to 3- or 4-month moxifloxacin regimens [moxifloxacin (M), isoniazid (H), rifampicin (R), pyrazinamide (Z) and ethambutol (E)] or to a control regimen (2H3 R3 Z3 E3 /4R3 H3 ) [C]. The 4 test regimens were 3R7 H7 Z7 E7 M7 [M3], 2R7 H7 Z7 E7 M7 /2R7 H7 M7 [M4], 2R7 H7 Z7 E7 M7 /2R3 H3 M3 [M4-I] or 2R7 H7 Z7 E7 M7 /2R3 H3 E3 M3 [M4-IE]. Treatment was directly observed. Clinical and bacteriological assessments were done monthly during treatment and for 24months post-treatment. The primary end point was TB recurrence post-treatment. Of 1371 patients, randomised, modified intention-to-treat (ITT) analysis was done in 1329 and per-protocol (PP) analysis in 1223 patients. Regimen M3 was terminated due to high TB recurrence rates. 'Favourable' response at end of treatment was 96-100% in the moxifloxacin regimensand 93% in the control regimen. Among these, the TB recurrence occurred in 4.1% in the M4 regimen and in 4.5% in the control regimen and demonstrated equivalence within a 5% margin (95% CI -3.68, 4.55). Similar findings were observed in modified ITT analysis. The TB recurrence rates in the M4-I and M4-IE regimens did not show equivalence with the control regimen. Sixteen (1.4%) of 1087 patients in the moxifloxacin regimens required treatment modification. The 4-month daily moxifloxacin regimen [M4] was found to be equivalent and as safe as the 6-month thrice-weekly control regimen.