Diagnostic accuracy of chest X-ray and CT using artificial intelligence for osteoporosis: systematic review and meta-analysis.

INTRODUCTION: Artificial intelligence (AI)-based systems using chest images are potentially reliable for diagnosing osteoporosis. METHODS: We performed a systematic review and meta-analysis to assess the diagnostic accuracy of chest X-ray and computed tomography (CT) scans using AI for osteoporosis in accordance with the diagnostic test accuracy guidelines. We included any type of study investigating the diagnostic accuracy of index test for osteoporosis. We searched MEDLINE, EMBASE, the Cochrane Central Register of Controlled Trials, and IEEE Xplore Digital Library on November 8, 2023. The main outcome measures were the sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) for osteoporosis and osteopenia. We described forest plots for sensitivity, specificity, and AUC. The summary points were estimated from the bivariate random-effects models. We summarized the overall quality of evidence using the Grades of Recommendation, Assessment, Development, and Evaluation approach. RESULTS: Nine studies with 11,369 participants were included in this review. The pooled sensitivity, specificity, and AUC of chest X-rays for the diagnosis of osteoporosis were 0.83 (95% confidence interval [CI] 0.75, 0.89), 0.76 (95% CI 0.71, 0.80), and 0.86 (95% CI 0.83, 0.89), respectively (certainty of the evidence, low). The pooled sensitivity and specificity of chest CT for the diagnosis of osteoporosis and osteopenia were 0.83 (95% CI 0.69, 0.92) and 0.70 (95% CI 0.61, 0.77), respectively (certainty of the evidence, low and very low). CONCLUSIONS: This review suggests that chest X-ray with AI has a high sensitivity for the diagnosis of osteoporosis, highlighting its potential for opportunistic screening. However, the risk of bias of patient selection in most studies were high. More research with adequate participants' selection criteria for screening tool will be needed in the future.

Artificial Intelligence for Assessment of Endotracheal Tube Position on Chest Radiographs: Validation in Patients From Two Institutions.

BACKGROUND. Timely and accurate interpretation of chest radiographs obtained to evaluate endotracheal tube (ETT) position is important for facilitating prompt adjustment if needed. OBJECTIVE. The purpose of our study was to evaluate the performance of a deep learning (DL)-based artificial intelligence (AI) system for detecting ETT presence and position on chest radiographs in three patient samples from two different institutions. METHODS. This retrospective study included 539 chest radiographs obtained immediately after ETT insertion from January 1 to March 31, 2020, in 505 patients (293 men, 212 women; mean age, 63 years) from institution A (sample A); 637 chest radiographs obtained from January 1 to January 3, 2020, in 302 patients (157 men, 145 women; mean age, 66 years) in the ICU (with or without an ETT) from institution A (sample B); and 546 chest radiographs obtained from January 1 to January 20, 2020, in 83 patients (54 men, 29 women; mean age, 70 years) in the ICU (with or without an ETT) from institution B (sample C). A commercial DL-based AI system was used to identify ETT presence and measure ETT tip-to-carina distance (TCD). The reference standard for proper ETT position was TCD between greater than 3 cm and less than 7 cm, determined by human readers. Critical ETT position was separately defined as ETT tip below the carina or TCD of 1 cm or less. ROC analysis was performed. RESULTS. AI had sensitivity and specificity for identification of ETT presence of 100.0% and 98.7% (sample B) and 99.2% and 94.5% (sample C). AI had sensitivity and specificity for identification of improper ETT position of 72.5% and 92.0% (sample A), 78.9% and 100.0% (sample B), and 83.7% and 99.1% (sample C). At a threshold y-axis TCD of 2 cm or less, AI had sensitivity and specificity for critical ETT position of 100.0% and 96.7% (sample A), 100.0% and 100.0% (sample B), and 100.0% and 99.2% (sample C). CONCLUSION. AI identified improperly positioned ETTs on chest radiographs obtained after ETT insertion as well as on chest radiographs obtained of patients in the ICU at two institutions. CLINICAL IMPACT. Automated AI identification of improper ETT position on chest radiographs may allow earlier repositioning and thereby reduce complications.

Prediction of bronchopulmonary dysplasia by the chest radiographic thoracic area on day one in infants with exomphalos.

OBJECTIVES: To determine if infants with exomphalos had abnormal antenatal lung growth as indicated by lower chest radiographic thoracic areas (CRTA) on day one compared to controls and whether the CRTA could predict the development of bronchopulmonary dysplasia (BPD). METHODS: Infants with exomphalos cared for between January 2004 and January 2023 were included. The controls were term, newborn infants ventilated for absent respiratory drive at birth, without lung disease and had no supplemental oxygen requirement by 6 h of age. The radiographs were imported as digital image files by Sectra PACS software (Sectra AB, Linköping, Sweden). Free-hand tracing of the perimeter of the thoracic area was undertaken and the CRTA calculated by the software. RESULTS: Sixty-four infants with exomphalos and 130 controls were included. Infants with exomphalos had a lower median (IQR) CRTA (1,983 [1,657-2,471] mm2) compared to controls (2,547 [2,153-2,932] mm2, p<0.001). Following multivariable regression analysis, infants with exomphalos had lower CRTAs compared to controls (p=0.001) after adjusting for differences in gestational age and male sex. In the exomphalos group, the CRTAs were lower in those who developed BPD (n=14, 1,530 [1,307-1,941] mm2) compared to those who did not (2,168 [1,865-2,672], p<0.001). Following multivariable regression analysis, the CRTA was associated with BPD development (p=0.021) after adjusting for male sex and gestational age. CONCLUSIONS: Lower CRTAs on day one in the exomphalos infants compared to the controls predicted BPD development.

Diagnostic Efficiency of Point-Of-Care Lung Ultrasonography in the Diagnosis of Adult Community-Acquired Pneumonia - A Single-Centre Study in Southeastern Nigeria.

BACKGROUND: Lung ultrasonography is an emerging tool in diagnosing community-acquired pneumonia (CAP) - a major cause of mortality worldwide. The objective of the study was to determine the diagnostic performance of point-of-care ultrasound (POCUS) of the lung compared to the chest radiograph in the diagnosis of CAP in adults. METHODS: Adults ≥ 18 years presenting at the general and medical outpatient clinics, medical and emergency wards with symptoms of suspected CAP were evaluated using a portable ultrasound device and single posteroanterior chest radiograph. Sensitivity, specificity, positive and negative predictive values (PPV and NPV), positive and negative likelihood ratios (LR+ and LR-) with corresponding 95% confidence intervals were computed for the lung ultrasound (LUS) against the chest radiograph as the criterion standard. RESULTS: Out of the 65 patients eventually studied, 50 (76.9%) were diagnosed with pneumonia by chest radiograph. The sensitivity, specificity, PPV, NPV, LR+, LR- and DOR for the LUS against the chest radiograph, respectively, were 96% (95%CI, 86.3% - 99.5%), 93.3% (95%CI, 68.1% - 99.8%), 98.0% (95%CI, 87.8% - 99.7%), 87.5% (64.1% - 96.5%), 14.4 (95%CI, 2.2 - 95.7), 0.04 (95%CI, 0.01 - 0.17) and 336 (28.3 - 3985.0). The overall accuracy was 95.4% (95%CI, 87.1 - 99.0%). The median time to completion of the LUS was 13 minutes. CONCLUSION: Lung ultrasound at the point of care is a reasonably accurate tool for the diagnosis of CAP in adults presenting with typical features.

CONTEXTE: L'échographie pulmonaire est un outil émergent dans le diagnostic de la pneumonie communautaire (CAP) ­ une cause majeure de mortalité dans le monde entier. L'objectif de l'étude était de déterminer la performance diagnostique de l'échographie pulmonaire au point de soins (POCUS) par rapport à la radiographie thoracique dans le diagnostic de la CAP chez les adultes. MÉTHODES: Les adultes ≥ 18 ans se présentant aux cliniques générales et médicales, aux services médicaux et d'urgence avec des symptômes de CAP suspectée ont été évalués à l'aide d'un appareil d'échographie portable et d'une radiographie thoracique postéroantérieure unique. La sensibilité, la spécificité, les valeurs prédictives positive et négative (PPV et NPV), les rapports de vraisemblance positifs et négatifs (LR+ et LR-) avec les intervalles de confiance correspondants à 95 % ont été calculés pour l'échographie pulmonaire (LUS) par rapport à la radiographie thoracique comme norme de référence. RÉSULTATS: Sur les 65 patients étudiés, 50 (76,9 %) ont été diagnostiqués avec une pneumonie par adiographie thoracique. La sensibilité, la spécificité, la PPV, la NPV, les LR+, LR- et DOR pour la LUS par rapport à la radiographie thoracique étaient respectivement de 96 % (IC à 95%, 86,3% ­ 99,5%), 93,3% (IC à 95%, 68,1% ­ 99,8%), 98,0% (IC à 95%, 87,8% - 99,7%), 87,5% (64,1% - 96,5%), 14,4 (IC à 95%, 2,2 ­ 95,7), 0,04 (IC à 95 %, 0,01 ­ 0,17) et 336 (28,3 ­ 3985,0). La précision globale était de 95,4 % (IC à 95%, 87,1 ­ 99,0%). Le temps médian pour l'achèvement de la LUS était de 13 minutes. CONCLUSION: L'échographie pulmonaire au point de soins est un outil raisonnablement précis pour le diagnostic de la CAP chez les adultes présentant des caractéristiques typiques. MOTS-CLÉS: Échographie pulmonaire, Radiographie thoracique, Pneumonie communautaire, Précision diagnostique, Ressources limitées.

Respiratory symptoms due to a twisted nasogastric tube: A case report.

Insertion of a nasogastric tube (NGT) is generally considered safe; however, it is not without risk, and in cases of misplacement, complications and even death may occur. In this article, we reported a case of NGT misplacement in a 75-year-old male, which resulted in aspiration pneumonia. We also reviewed published cases of NGT misplacement. Clinicians should pay enough attention to the confirmation of the proper placement of an NGT. A systematic approach for NGT insertion and confirmation is required to prevent misplacement.

Estimating Preterm Lung Volume: A Comparison of Lung Ultrasound, Chest Radiography, and Oxygenation.

OBJECTIVE: To determine the relationship between lung ultrasound (LUS) examination, chest radiograph (CXR), and radiographic and clinical evaluations in the assessment of lung volume in preterm infants. STUDY DESIGN: In this prospective cohort study LUS was performed before CXR on 70 preterm infants and graded using (1) a LUS score, (2) an atelectasis score, and (3) measurement of atelectasis depth. Radiographic diaphragm position and radio-opacification were used to determine global and regional radiographic atelectasis. The relationship between LUS, CXR, and oxygenation was assessed using receiver operator characteristic and correlation analysis. RESULTS: LUS scores, atelectasis scores, and atelectasis depth did not correspond with radiographic global atelectasis (area under receiver operator characteristics curves, 0.54 [95% CI, 0.36-0.71], 0.49 [95% CI, 0.34-0.64], and 0.47 [95% CI, 0.31-0.64], respectively). Radiographic atelectasis of the right upper, right lower, left upper, and left lower quadrants was predicted by LUS scores (0.75 [95% CI, 0.59-0.92], 0.75 [95% CI, 0.62-0.89], 0.69 [95% CI, 0.56-0.82], and 0.63 [95% CI, 0.508-0.751]) and atelectasis depth (0.66 [95% CI, 0.54-0.78], 0.65 [95% CI, 0.53-0.77], 0.63 [95% CI, 0.50-0.76], and 0.56 [95% CI, 0.44-0.70]). LUS findings were moderately correlated with oxygen saturation index (ρ = 0.52 [95% CI, 0.30-0.70]) and saturation to fraction of inspired oxygen ratio (ρ = -0.63 [95% CI, -0.76 to -0.46]). The correlation between radiographic diaphragm position, the oxygenation saturation index, and peripheral oxygen saturation to fraction of inspired oxygen ratio was very weak (ρ = 0.36 [95% CI, 0.11-0.59] and ρ = -0.32 [95% CI, -0.53 to -0.07], respectively). CONCLUSIONS: LUS assessment of lung volume does not correspond with radiographic diaphragm position preterm infants. However, LUS predicted radiographic regional atelectasis and correlated with oxygenation. The relationship between radiographic diaphragm position and oxygenation was very weak. Although LUS may not replace all radiographic measures of lung volume, LUS more accurately reflects respiratory status in preterm infants. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry: ACTRN12621001119886.

Image augmentation and automated measurement of endotracheal-tube-to-carina distance on chest radiographs in intensive care unit using a deep learning model with external validation.

BACKGROUND: Chest radiographs are routinely performed in intensive care unit (ICU) to confirm the correct position of an endotracheal tube (ETT) relative to the carina. However, their interpretation is often challenging and requires substantial time and expertise. The aim of this study was to propose an externally validated deep learning model with uncertainty quantification and image segmentation for the automated assessment of ETT placement on ICU chest radiographs. METHODS: The CarinaNet model was constructed by applying transfer learning to the RetinaNet model using an internal dataset of ICU chest radiographs. The accuracy of the model in predicting the position of the ETT tip and carina was externally validated using a dataset of 200 images extracted from the MIMIC-CXR database. Uncertainty quantification was performed using the level of confidence in the ETT-carina distance prediction. Segmentation of the ETT was carried out using edge detection and pixel clustering. RESULTS: The interrater agreement was 0.18 cm for the ETT tip position, 0.58 cm for the carina position, and 0.60 cm for the ETT-carina distance. The mean absolute error of the model on the external test set was 0.51 cm for the ETT tip position prediction, 0.61 cm for the carina position prediction, and 0.89 cm for the ETT-carina distance prediction. The assessment of ETT placement was improved by complementing the human interpretation of chest radiographs with the CarinaNet model. CONCLUSIONS: The CarinaNet model is an efficient and generalizable deep learning algorithm for the automated assessment of ETT placement on ICU chest radiographs. Uncertainty quantification can bring the attention of intensivists to chest radiographs that require an experienced human interpretation. Image segmentation provides intensivists with chest radiographs that are quickly interpretable and allows them to immediately assess the validity of model predictions. The CarinaNet model is ready to be evaluated in clinical studies.

A unilateral whiteout lung in child with multisystem inflammatory syndrome associated with COVID-19 due to SARS-CoV-2: one case report of a boy.

BACKGROUND: Multisystem inflammatory syndrome in children (MIS-C) is a relatively new and rare complication of COVID-19. This complication seems to develop after the infection rather than during the acute phase of COVID-19. This report aims to describe a case of MIS-C in an 8-year-old Thai boy who presented with unilateral lung consolidation. Unilateral whiteout lung is not a common pediatric chest radiograph finding in MIS-C, but this is attributed to severe acute respiratory failure. CASE PRESENTATION: An 8-year-old boy presented with persistent fever for seven days, right cervical lymphadenopathy, and dyspnea for 12 h. The clinical and biochemical findings were compatible with MIS-C. Radiographic features included total opacity of the right lung and CT chest found consolidation and ground-glass opacities of the right lung. He was treated with intravenous immunoglobulin and methylprednisolone, and he dramatically responded to the treatment. He was discharged home in good condition after 8 days of treatment. CONCLUSION: Unilateral whiteout lung is not a common pediatric chest radiographic finding in MIS-C, but when it is encountered, a timely and accurate diagnosis is required to avoid delays and incorrect treatment. We describe a pediatric patient with unilateral lung consolidation from the inflammatory process.

External validation of deep learning-based automated detection algorithm for chest radiograph: practical issues in outpatient clinic.

BACKGROUND: There have been no reports on diagnostic performance of deep learning-based automated detection (DLAD) for thoracic diseases in real-world outpatient clinic. PURPOSE: To validate DLAD for use at an outpatient clinic and analyze the interpretation time for chest radiographs. MATERIAL AND METHODS: This is a retrospective single-center study. From 18 January 2021 to 18 February 2021, 205 chest radiographs with DLAD and paired chest CT from 205 individuals (107 men and 98 women; mean ± SD age: 63 ± 8 years) from an outpatient clinic were analyzed for external validation and observer performance. Two radiologists independently reviewed the chest radiographs by referring to the paired chest CT and made reference standards. Two pulmonologists and two thoracic radiologists participated in observer performance tests, and the total amount of time taken during the test was measured. RESULTS: The performance of DLAD (area under the receiver operating characteristic curve [AUC] = 0.920) was significantly higher than that of pulmonologists (AUC = 0.756) and radiologists (AUC = 0.782) without assistance of DLAD. With help of DLAD, the AUCs were significantly higher for both groups (pulmonologists AUC = 0.853; radiologists AUC = 0.854). A greater than 50% decrease in mean interpretation time was observed in the pulmonologist group with assistance of DLAD compared to mean reading time without aid of DLAD (from 67 s per case to 30 s per case). No significant difference was observed in the radiologist group (from 61 s per case to 61 s per case). CONCLUSION: DLAD demonstrated good performance in interpreting chest radiographs of patients at an outpatient clinic, and was especially helpful for pulmonologists in improving performance.

Assessment of the Suitability of the Fleischner Society Imaging Guidelines in Evaluating Chest Radiographs of COVID-19 Patients.

BACKGROUND: The Fleischner Society established consensus guidelines for imaging in patients with coronavirus disease 2019 (COVID-19). We investigated the prevalence of pneumonia and the adverse outcomes by dividing groups according to the symptoms and risk factors of patients and assessed the suitability of the Fleischner society imaging guidelines in evaluating chest radiographs of COVID-19 patients. METHODS: From February 2020 to May 2020, 685 patients (204 males, mean 58 ± 17.9 years) who were diagnosed with COVID-19 and hospitalized were included. We divided patients into four groups according to the severity of symptoms and presence of risk factors (age > 65 years and presence of comorbidities). The patient groups were defined as follows: group 1 (asymptomatic patients), group 2 (patients with mild symptoms without risk factors), group 3 (patients with mild symptoms and risk factors), and group 4 (patients with moderate to severe symptoms). According to the Fleischner society, chest imaging is not indicated for groups 1-2 but is indicated for groups 3-4. We compared the prevalence and score of pneumonia on chest radiographs and compare the adverse outcomes (progress to severe pneumonia, intensive care unit admission, and death) between groups. RESULTS: Among the 685 COVID-19 patients, 138 (20.1%), 396 (57.8%), 102 (14.9%), and 49 (7.1%) patients corresponded to groups 1 to 4, respectively. Patients in groups 3-4 were significantly older and showed significantly higher prevalence rates of pneumonia (group 1-4: 37.7%, 51.3%, 71.6%, and 98%, respectively, P < 0.001) than those in groups 1-2. Adverse outcomes were also higher in groups 3-4 than in groups 1-2 (group 1-4: 8.0%, 3.5%, 6.9%, and 51%, respectively, P < 0.001). Patients with adverse outcomes in group 1 were initially asymptomatic but symptoms developed during follow-up. They were older (mean age, 80 years) and most of them had comorbidities (81.8%). Consistently asymptomatic patients had no adverse events. CONCLUSION: The prevalence of pneumonia and adverse outcomes were different according to the symptoms and risk factors in COVID-19 patients. Therefore, as the Fleischner Society recommended, evaluation and monitoring of COVID-19 pneumonia using chest radiographs is necessary for old symptomatic patients with comorbidities.

Chest radiograph findings in children with COVID-19-A retrospective analysis from a tertiary care paediatric hospital in South India.

OBJECTIVE: The primary aim of this study is to document the chest X-ray findings in children with COVID-19 pneumonia. The secondary aim is to correlate chest X-ray findings to patient outcome. METHODS: We performed a retrospective analysis of children (0-18 years) with SARS-CoV-2 admitted to our hospital from June 2020 to December 2021. The chest radiographs were assessed for: peribronchial cuffing, ground-glass opacities (GGOs), consolidation, pulmonary nodules and pleural effusion. The severity of the pulmonary findings was graded using a modification of the Brixia score. RESULTS: There were a total of 90 patients with SARS-CoV-2 infection; the mean age was 5.8 years (age range 7 days to 17 years). Abnormalities were seen on the CXR in 74 (82%) of the 90 patients. Bilateral peribronchial cuffing was seen in 68% (61/90), consolidation in 11% (10/90), bilateral central GGOs in 2% (2/90) and unilateral pleural effusion in 1% (1/90). Overall the average CXR score in our cohort of patients was 6. The average CXR score in patients with oxygen requirement was 10. The duration of hospital stay was significantly longer in those patients with CXR score >9. CONCLUSION: The CXR score has the potential to serve as tool to identify children at high risk and may aid planning of clinical management in such patients.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) created a global pandemic in early March 2020. There are very few studies describing the lung changes in affected children. We performed a retrospective study in children, aged between 0 days and 18 years, who tested positive for this virus. This study was conducted in a paediatric tertiary care hospital in South India. Chest X-ray (CXR) was done in children with moderate and severe SARS-CoV-2 infection; these X-rays were reviewed and scoring was done to assess the degree of abnormality. It was seen that the duration of hospital stay was longer in children with a high CXR score. Amongst the children with score >9, 60% needed oxygen support during their treatment. Thus, CXR score can play a role in the prediction of disease outcome in SARS-CoV-2 infection.

Deep Learning-based Diagnosis and Localization of Pneumothorax on Portable Supine Chest X-ray in Intensive and Emergency Medicine: A Retrospective Study.

PURPOSE: To develop two deep learning-based systems for diagnosing and localizing pneumothorax on portable supine chest X-rays (SCXRs). METHODS: For this retrospective study, images meeting the following inclusion criteria were included: (1) patient age ≥ 20 years; (2) portable SCXR; (3) imaging obtained in the emergency department or intensive care unit. Included images were temporally split into training (1571 images, between January 2015 and December 2019) and testing (1071 images, between January 2020 to December 2020) datasets. All images were annotated using pixel-level labels. Object detection and image segmentation were adopted to develop separate systems. For the detection-based system, EfficientNet-B2, DneseNet-121, and Inception-v3 were the architecture for the classification model; Deformable DETR, TOOD, and VFNet were the architecture for the localization model. Both classification and localization models of the segmentation-based system shared the UNet architecture. RESULTS: In diagnosing pneumothorax, performance was excellent for both detection-based (Area under receiver operating characteristics curve [AUC]: 0.940, 95% confidence interval [CI]: 0.907-0.967) and segmentation-based (AUC: 0.979, 95% CI: 0.963-0.991) systems. For images with both predicted and ground-truth pneumothorax, lesion localization was highly accurate (detection-based Dice coefficient: 0.758, 95% CI: 0.707-0.806; segmentation-based Dice coefficient: 0.681, 95% CI: 0.642-0.721). The performance of the two deep learning-based systems declined as pneumothorax size diminished. Nonetheless, both systems were similar or better than human readers in diagnosis or localization performance across all sizes of pneumothorax. CONCLUSIONS: Both deep learning-based systems excelled when tested in a temporally different dataset with differing patient or image characteristics, showing favourable potential for external generalizability.

Predicting the risk of chest radiograph abnormality 12-weeks post hospitalisation with SARS CoV-2 PCR confirmed COVID-19.

BACKGROUND: Routine follow-up of patients hospitalised with COVID-19 is recommended, however due to the ongoing high number of infections this is not without significant health resource and economic burden. In a previous study we investigated the prevalence of, and risk factors for, persistent chest radiograph (CXR) abnormalities post-hospitalisation with COVID-19 and identified a 5-point composite score that strongly predicted risk of persistent CXR abnormality at 12-weeks. Here we sought to validate and refine our findings in an independent cohort of patients. METHODOLOGY: A single-centre prospective study of consecutive patients attending a virtual post-hospitalisation COVID-19 clinic and CXR as part of their standard clinical care between 2nd March - 22nd June 2021. Inpatient and follow-up CXRs were scored by the assessing clinician for extent of pulmonary infiltrates (0-4 in each lung) with complete resolution defined as a follow-up score of zero. RESULTS: 182 consecutive patients were identified of which 31% had persistent CXR abnormality at 12-weeks. Patients with persistent CXR abnormality were significantly older (p < 0.001), had a longer hospital length of stay (p = 0.005), and had a higher incidence of both level 2 or 3 facility admission (level 2/3 care) (p = 0.003) and ever-smoking history (p = 0.038). Testing our composite score in the present cohort we found it predicted persistent CXR abnormality with reasonable accuracy (area under the receiver operator curve [AUROC 0.64]). Refining this score replacing obesity with Age ≥ 50 years, we identify the SHADE-750 score (1-point each for; Smoking history, Higher-level care (level 2/3 admission), Age ≥ 50 years, Duration of admission ≥ 15 days and Enzyme-lactate dehydrogenase (LDH ≥ 750U/L), that accurately predicted risk of persistent CXR abnormality, both in the present cohort (AUROC 0.73) and when retrospectively applied to our 1st cohort (AUROC 0.79). Applied to both cohorts combined (n = 213) it again performed strongly (AUROC 0.75) with all patients with a score of zero (n = 18) having complete CXR resolution at 12-weeks. CONCLUSIONS: In two independent cohorts of patients hospitalised with COVID-19, we identify a 5-point score which accurately predicts patients at risk of persistent CXR abnormality at 12-weeks. This tool could be used by clinicians to identify patients in which radiological follow-up may not be required.

Current Trends in the Use of Ultrasound Over Chest X-Ray to Identify Pneumothoraces in ICU, Trauma, and ARDS Patients.

Pneumothoraces are a common and potentially fatal complication for critically ill patients in the trauma and intensive care units. Since its use for pneumothorax detection was first reported in 1987, ultrasound has been increasingly used for the detection of thoracic injuries. As ultrasound imaging has improved and operators have potentially become more proficient, it is important to analyze more recent trends in the sensitivities and specificities of ultrasound for the detection of pneumothorax. This literature review and meta-analysis identifies 17 studies that directly compare the sensitivity and specificity of ultrasound and anterior-posterior chest x-ray in the identification of pneumothorax among 2955 patients who developed 793 pneumothoraces as detected by gold standard CT scanning. For the 17 articles analyzed, the pooled sensitivity of trans-thoracic ultrasound was 75.07% (64.92%-85.22%), and the pooled specificity was 98.36% (97.45%-99.26%). The pooled sensitivity of CXR was 45.65% (36.04%-55.26%), and pooled specificity was 99.62% (99.00%-100%). While this review demonstrates an improved sensitivity in the detection of pneumothorax with ultrasound over AP chest x-rays, it did not find a significant trend or improvement in the sensitivity or specificity of ultrasound for detecting pneumothorax over time.

Clinical Practices in Central Venous Catheter Mechanical Adverse Events.

Background: Over 5 million central venous catheters (CVCs) are placed annually. Pneumothorax and catheter malpositioning are common adverse events (AE) that requires attention. This study aims to evaluate local practices of mechanical complication frequency, type, and subsequent intervention(s) related to mechanical AE with an emphasis on catheter malpositioning. Methods: This is a retrospective review of CVC placements in a tertiary hospital setting from 1/2013 to 12/2013. Pneumothorax and CVC positioning were evaluated on post-insertion chest x-ray (CXR). Malposition was defined as unintended placement of the catheter in a vessel other than the intended superior vena cava on CXR. Catheter reposition was defined as radiographic evidence of a new catheter with removal of the old catheter less than 24hrs after initial placement. Data points analyzed included pneumothorax and thoracostomy rate, CVC malposition frequency, catheter reposition rate, catheter duration, and incidence of complications such as catheter associated venous thrombosis. Result: Among 2045 eligible CVC insertions, pneumothoraces occurred in 14 (0.7%; 95%CI 0.38, 1.17) and malpositions were identified in 275 (13.4%; 95% CI 12.3, 15.3). The proportion of pneumothoraces that required tube thoracostomy was 57%. The proportion of CVCs with malposition that were removed or replaced within 24h was 32.7%. "Malpositioned" catheters that were left in place by the clinical team (n = 185) had an average catheter duration of 8.2 days (95% CI 7.2, 9.3) versus 7.2 days (95% CI 6.17, 8.23) for catheters that were replaced after initial malposition (p = 0.14, t test). The incidence of venous thrombosis in repositioned "malpositioned" catheters was 7.8% versus 4.9% for "malpositioned" catheters that were left in place. Conclusions: Clinically significant catheter malposition and pneumothorax after CVC insertion are low. In this study, replaced and non-replaced "malpositioned" catheters had similar catheter duration and rates of complications, challenging the current dogma of CVC malposition practice.

Strategies for recognizing pneumonia look-alikes.

Community-acquired pneumonia is a common diagnosis in children. Among the many children whose symptoms and/or chest X-ray is consistent with community-acquired pneumonia, it can be difficult to distinguish the rare cases of differential diagnoses that require specific management. The aim of this educational article is to provide clinicians with a series of questions to ask themselves in order to detect a possible differential diagnosis of pneumonia in children. The value of this approach is illustrated by 13 real clinical cases in which a child was misdiagnosed as having lobar pneumonia. What is Known: • When a lobar pneumonia is diagnosed, an appropriate antibiotic treatment leads to the resolution of the clinical signs in most cases. • However, several diseases can be look-alikes for pneumonia and mislead the practitioner. What is New: • This article provides a new approach to identify differential diagnoses of pneumonia in children. • It is illustrated by 13 real-life situations of children misdiagnosed as having pneumonia.

Clinical significance of intracavitary electrocardiographic localization in the prevention of PICC heterotopia in children with tumor.

OBJECTIVE: To explore the clinical significance of intracavitary electrocardiogram positioning technology in preventing catheter ectopic position during peripherally inserted central catheter (PICC) catheterization in children with tumors. METHODS: A retrospective analysis of the clinical data of 62 children who required PICC catheterization was performed. The intracavitary electrocardiogram (ECG) positioning technology was used during the tube placement of the child patients. After the tube was successfully placed, the chest radiograph was taken. The ECG positioning result was compared with the chest radiograph positioning result after the tube was inserted, and the sensitivity and specificity of the ECG positioning were calculated. RESULTS: The intracavitary electrocardiogram results of 62 children with PICC catheters showed that 56 cases (90.32%) had characteristic P waves, and six cases (9.68%) had no characteristic P waves. The chest radiographs of 56 children with characteristic P wave showed that 33 cases (58.93%) of the catheter tip position was appropriate, 22 cases (39.29%) of the catheter tip was too deep, and 1 case was in a non-superior vena cava; six cases of chest radiographs of children with no characteristic P wave showed: one case was too deep at T8 level, one case was too shallow at T4 level, four cases were at non-superior vena cava, one case was contralateral internal jugular vein, two cases in the contralateral brachiocephalic vein, and one case was the contralateral subclavian vein. CONCLUSION: Intracavitary ECG positioning assisted catheter placement in infants can effectively improve the accuracy of catheter tip position.

Development and Validation of a Multimodal-Based Prognosis and Intervention Prediction Model for COVID-19 Patients in a Multicenter Cohort.

The ability to accurately predict the prognosis and intervention requirements for treating highly infectious diseases, such as COVID-19, can greatly support the effective management of patients, especially in resource-limited settings. The aim of the study is to develop and validate a multimodal artificial intelligence (AI) system using clinical findings, laboratory data and AI-interpreted features of chest X-rays (CXRs), and to predict the prognosis and the required interventions for patients diagnosed with COVID-19, using multi-center data. In total, 2282 real-time reverse transcriptase polymerase chain reaction-confirmed COVID-19 patients' initial clinical findings, laboratory data and CXRs were retrospectively collected from 13 medical centers in South Korea, between January 2020 and June 2021. The prognostic outcomes collected included intensive care unit (ICU) admission and in-hospital mortality. Intervention outcomes included the use of oxygen (O2) supplementation, mechanical ventilation and extracorporeal membrane oxygenation (ECMO). A deep learning algorithm detecting 10 common CXR abnormalities (DLAD-10) was used to infer the initial CXR taken. A random forest model with a quantile classifier was used to predict the prognostic and intervention outcomes, using multimodal data. The area under the receiver operating curve (AUROC) values for the single-modal model, using clinical findings, laboratory data and the outputs from DLAD-10, were 0.742 (95% confidence interval [CI], 0.696−0.788), 0.794 (0.745−0.843) and 0.770 (0.724−0.815), respectively. The AUROC of the combined model, using clinical findings, laboratory data and DLAD-10 outputs, was significantly higher at 0.854 (0.820−0.889) than that of all other models (p < 0.001, using DeLong's test). In the order of importance, age, dyspnea, consolidation and fever were significant clinical variables for prediction. The most predictive DLAD-10 output was consolidation. We have shown that a multimodal AI model can improve the performance of predicting both the prognosis and intervention in COVID-19 patients, and this could assist in effective treatment and subsequent resource management. Further, image feature extraction using an established AI engine with well-defined clinical outputs, and combining them with different modes of clinical data, could be a useful way of creating an understandable multimodal prediction model.

Limited generalizability of deep learning algorithm for pediatric pneumonia classification on external data.

PURPOSE: (1) Develop a deep learning system (DLS) to identify pneumonia in pediatric chest radiographs, and (2) evaluate its generalizability by comparing its performance on internal versus external test datasets. METHODS: Radiographs of patients between 1 and 5 years old from the Guangzhou Women and Children's Medical Center (Guangzhou dataset) and NIH ChestXray14 dataset were included. We utilized 5232 radiographs from the Guangzhou dataset to train a ResNet-50 deep convolutional neural network (DCNN) to identify pediatric pneumonia. DCNN testing was performed on a holdout set of 624 radiographs from the Guangzhou dataset (internal test set) and 383 radiographs from the NIH ChestXray14 dataset (external test set). Receiver operating characteristic curves were generated, and area under the curve (AUC) was compared via DeLong parametric method. Colored heatmaps were generated using class activation mapping (CAM) to identify important image pixels for DCNN decision-making. RESULTS: The DCNN achieved AUC of 0.95 and 0.54 for identifying pneumonia on internal and external test sets, respectively (p < 0.0001). Heatmaps generated by the DCNN showed the algorithm focused on clinically relevant features for images from the internal test set, but not for images from the external test set. CONCLUSION: Our model had high performance when tested on an internal dataset but significantly lower accuracy when tested on an external dataset. Likewise, marked differences existed in the clinical relevance of features highlighted by heatmaps generated from internal versus external datasets. This study underscores potential limitations in the generalizability of such DLS models.

Ingestion poisoning related lung injury- a pictorial review.

Poison ingestion is a medical emergency requiring immediate care in the emergency department. Respiratory symptoms with ingested poisons can occur due to aspiration, cardiopulmonary effects, or direct lung toxicity due to injury of the alveolar epithelium. Chest imaging (chest radiographs/CT) is usually performed in the emergency setting to evaluate such symptoms. It is often impossible to elicit the nature of the poison ingested by the patients due to their unconscious state. Identification of the culprit poison can expedite the patient's management towards a specific antidote or help understand the underlying mechanism causing the pulmonary symptoms. The imaging manifestations depend on the underlying mechanisms, varying for each ingested poison, forming an imaging signature which has not been adequately discussed in existing literature. Poisons like paraquat and organophosphate are important to differentiate as indiscriminate use of oxygen therapy in the former can exacerbate the lung injury caused by redox cycling. In this pictorial assay, we present the chest imaging spectrum of commonly ingested poisons, and further suggest algorithmic approach towards identification of common poisons based on their chest imaging.