Interpretation Of Chest Radiography Research Articles

Computer-aided detection thresholds for digital chest radiography interpretation in tuberculosis diagnostic algorithms.

Use of computer-aided detection (CAD) software is recommended to improve tuberculosis screening and triage, but threshold determination is challenging if reference testing has not been performed in all individuals. We aimed to determine such thresholds through secondary analysis of the 2019 Lesotho national tuberculosis prevalence survey. Symptom screening and chest radiographs were performed in participants aged ≥15 years; those symptomatic or with abnormal chest radiographs provided samples for Xpert MTB/RIF and culture testing. Chest radiographs were processed using CAD4TB version 7. We used six methodological approaches to deal with participants who did not have bacteriological test results to estimate pulmonary tuberculosis prevalence and assess diagnostic accuracy. Among 17 070 participants, 5214 (31%) had their tuberculosis status determined; 142 had tuberculosis. Prevalence estimates varied between methodological approaches (0.83-2.72%). Using multiple imputation to estimate tuberculosis status for those eligible but not tested, and assuming those not eligible for testing were negative, a CAD4TBv7 threshold of 13 had a sensitivity of 89.7% (95% CI 84.6-94.8) and a specificity of 74.2% (73.6-74.9), close to World Health Organization (WHO) target product profile criteria. Assuming all those not tested were negative produced similar results. This is the first study to evaluate CAD4TB in a community screening context employing a range of approaches to account for unknown tuberculosis status. The assumption that those not tested are negative - regardless of testing eligibility status - was robust. As threshold determination must be context specific, our analytically straightforward approach should be adopted to leverage prevalence surveys for CAD threshold determination in other settings with a comparable proportion of eligible but not tested participants.

Using AI to Improve Radiologist Performance in Detection of Abnormalities on Chest Radiographs.

Background Chest radiography remains the most common radiologic examination, and interpretation of its results can be difficult. Purpose To explore the potential benefit of artificial intelligence (AI) assistance in the detection of thoracic abnormalities on chest radiographs by evaluating the performance of radiologists with different levels of expertise, with and without AI assistance. Materials and Methods Patients who underwent both chest radiography and thoracic CT within 72 hours between January 2010 and December 2020 in a French public hospital were screened retrospectively. Radiographs were randomly included until reaching 500 radiographs, with about 50% of radiographs having abnormal findings. A senior thoracic radiologist annotated the radiographs for five abnormalities (pneumothorax, pleural effusion, consolidation, mediastinal and hilar mass, lung nodule) based on the corresponding CT results (ground truth). A total of 12 readers (four thoracic radiologists, four general radiologists, four radiology residents) read half the radiographs without AI and half the radiographs with AI (ChestView; Gleamer). Changes in sensitivity and specificity were measured using paired t tests. Results The study included 500 patients (mean age, 54 years ± 19 [SD]; 261 female, 239 male), with 522 abnormalities visible on 241 radiographs. On average, for all readers, AI use resulted in an absolute increase in sensitivity of 26% (95% CI: 20, 32), 14% (95% CI: 11, 17), 12% (95% CI: 10, 14), 8.5% (95% CI: 6, 11), and 5.9% (95% CI: 4, 8) for pneumothorax, consolidation, nodule, pleural effusion, and mediastinal and hilar mass, respectively (P < .001). Specificity increased with AI assistance (3.9% [95% CI: 3.2, 4.6], 3.7% [95% CI: 3, 4.4], 2.9% [95% CI: 2.3, 3.5], and 2.1% [95% CI: 1.6, 2.6] for pleural effusion, mediastinal and hilar mass, consolidation, and nodule, respectively), except in the diagnosis of pneumothorax (-0.2%; 95% CI: -0.36, -0.04; P = .01). The mean reading time was 81 seconds without AI versus 56 seconds with AI (31% decrease, P < .001). Conclusion AI-assisted chest radiography interpretation resulted in absolute increases in sensitivity for all radiologists of various levels of expertise and reduced the reading times; specificity increased with AI, except in the diagnosis of pneumothorax. © RSNA, 2023 Supplemental material is available for this article.

Effect of multimodal diagnostic approach using deep learning-based automated detection algorithm for active pulmonary tuberculosis

In this study, we developed a model to predict culture test results for pulmonary tuberculosis (PTB) with a customized multimodal approach and evaluated its performance in different clinical settings. Moreover, we investigated potential performance improvements by combining this approach with deep learning-based automated detection algorithms (DLADs). This retrospective observational study enrolled patients over 18 years of age who consecutively visited the level 1 emergency department and underwent chest radiograph and sputum testing. The primary endpoint was positive sputum culture for PTB. We compared the performance of the diagnostic models by replacing radiologists’ interpretations of chest radiographs with screening scores calculated through DLAD. The optimal diagnostic model had an area under the receiver operating characteristic curve of 0.924 (95% CI 0.871–0.976) and an area under precision recall curve of 0.403 (95% CI 0.195–0.580) while maintaining a specificity of 81.4% when sensitivity was fixed at 90%. Multicomponent models showed improved performance for detecting PTB when chest radiography interpretation was replaced by DLAD. Multicomponent diagnostic models with DLAD customized for different clinical settings are more practical than traditional methods for detecting patients with PTB. This novel diagnostic approach may help prevent the spread of PTB and optimize healthcare resource utilization in resource-limited clinical settings.

Interpretation of chest radiography in patients with known or suspected SARS-CoV-2 infection: what we learnt from comparison with computed tomography.

Differently from computed tomography (CT), well-defined terminology for chest radiography (CXR) findings and standardized reporting in the setting of known or suspected COVID-19 are still lacking. We propose a revision of CXR major imaging findings in SARS-CoV-2 pneumonia derived from the comparison of CXR and CT, suggesting a precise and standardized terminology for CXR reporting. This description will consider asymptomatic patients, symptomatic patients, and patients with SARS-CoV-2-related pulmonary complications. We suggest using terms such as ground-glass opacities, consolidation, and reticular pattern for the most common findings, and characteristic chest radiographic pattern in presence of one or more of the above-mentioned findings with peripheral and mid-to-lower lung zone distribution.

Deep Multi-Objective Learning from Low-Dose CT for Automatic Lung-RADS Report Generation.

Radiology report generation through chest radiography interpretation is a time-consuming task that involves the interpretation of images by expert radiologists. It is common for fatigue-induced diagnostic error to occur, and especially difficult in areas of the world where radiologists are not available or lack diagnostic expertise. In this research, we proposed a multi-objective deep learning model called CT2Rep (Computed Tomography to Report) for generating lung radiology reports by extracting semantic features from lung CT scans. A total of 458 CT scans were used in this research, from which 107 radiomics features and 6 slices of segmentation related nodule features were extracted for the input of our model. The CT2Rep can simultaneously predict position, margin, and texture, which are three important indicators of lung cancer, and achieves remarkable performance with an F1-score of 87.29%. We conducted a satisfaction survey for estimating the practicality of CT2Rep, and the results show that 95% of the reports received satisfactory ratings. The results demonstrate the great potential in this model for the production of robust and reliable quantitative lung diagnosis reports. Medical personnel can obtain important indicators simply by providing the lung CT scan to the system, which can bring about the widespread application of the proposed framework.

Computer-aided interpretation of chest radiography reveals the spectrum of tuberculosis in rural South Africa

Computer-aided digital chest radiograph interpretation (CAD) can facilitate high-throughput screening for tuberculosis (TB), but its use in population-based active case-finding programs has been limited. In an HIV-endemic area in rural South Africa, we used a CAD algorithm (CAD4TBv5) to interpret digital chest x-rays (CXR) as part of a mobile health screening effort. Participants with TB symptoms or CAD4TBv5 score above the triaging threshold were referred for microbiological sputum assessment. During an initial pilot phase, a low CAD4TBv5 triaging threshold of 25 was selected to maximize TB case finding. We report the performance of CAD4TBv5 in screening 9,914 participants, 99 (1.0%) of whom were found to have microbiologically proven TB. CAD4TBv5 was able to identify TB cases at the same sensitivity but lower specificity as a blinded radiologist, whereas the next generation of the algorithm (CAD4TBv6) achieved comparable sensitivity and specificity to the radiologist. The CXRs of people with microbiologically confirmed TB spanned a range of lung field abnormality, including 19 (19.2%) cases deemed normal by the radiologist. HIV serostatus did not impact CAD4TB’s performance. Notably, 78.8% of the TB cases identified during this population-based survey were asymptomatic and therefore triaged for sputum collection on the basis of CAD4TBv5 score alone. While CAD4TBv6 has the potential to replace radiologists for triaging CXRs in TB prevalence surveys, population-specific piloting is necessary to set the appropriate triaging thresholds. Further work on image analysis strategies is needed to identify radiologically subtle active TB.

Competency in Chest Radiography Interpretation by Junior Doctors and Final Year Medical Students at a Teaching Hospital.

Background Chest radiography (CXR) is a widely used imaging technique for assessing various chest conditions; however, little is known on the medical doctors' and medical students' level of skills to interpret the CXRs. This study assessed the residents, medical officers, house officers, and final year medical students' competency in CXRs interpretation and how the patient's clinical history influences the interpretation. Methods We conducted a cross-sectional study in the Cape Coast Teaching Hospital in the Central Region of Ghana among 99 nonradiologists, comprising 10 doctors in residency programmes, 18 medical officers, 33 house officers, and 38 final year medical students. The data collection was done with a semistructured questionnaire in two phases. In phase 1, ten CXRs were presented without patient's clinical history. Phase 2 involved the same ten CXRs presented in the same order alongside the patient's clinical history. Participants were given 3 minutes to interpret each image. Median and interquartile ranges were used to describe continuous variables, while frequencies and percentages were used to describe categorical variables. Test of significant difference and association was conducted using a Wilcoxon rank-sum test/Kruskal–Wallis test and chi-square (X2) test, respectively. Results The average score for interpreting CXRs was 7.0 (IQR = 5–8) and 4.0 (IQR = 3-4), when CXRs were, respectively, presented with and without clinical history. No significant difference was seen in average scores regarding the levels of formal training. Without clinical history, only 40.0% of residents, 22.2% of medical officers, 24.2% of house officers, and 13.2% of medical students correctly interpreted CXRs, while more than 75% each of all categories correctly interpreted CXRs when presented with clinical history. However, all participants had difficulties in identifying CXR with pneumothorax (27.3% vs. 30.3%), pneumomediastinum or left rib fracture (8.1% vs. 33.3%), and lung collapse (37.4% vs. 37.4%) in both situations, with and without patient clinical history. Conclusion The patient's clinical history was found to greatly influence doctors' competence in interpreting CXRs. We found a gap in doctors' and medical students' ability to interpret CXRs; hence, the development of this skill should be improved at all levels of medical training.

Sensitivity of Computer-Aided Detection (CAD) Device for Lung Nodule Detection on Chest Radiography: A Real-Life Application

Purpose: The radiographic diagnosis of lung nodules is associated with low sensitivity and specificity. Computer-aided detection (CAD) system has been shown to have higher accuracy in the detection of lung nodules. The purpose of this study is to assess the effect on sensitivity and specificity when a CAD system is used to review chest radiographs in real-time setting. Methods: Sixty-three patients, including 24 controls, who had chest radiographs and CT within three months were included in this study. Three radiologists were presented chest radiographs without CAD and were asked to mark all lung nodules. Then the radiologists were allowed to see the CAD region-of-interest (ROI) marks and were asked to agree or disagree with the marks. All marks were correlated with CT studies. Results: The mean sensitivity of the three radiologists without CAD was 16.1%, which showed a statistically significant improvement to 22.5% with CAD. The mean specificity of the three radiologists was 52.5% without CAD and decreased to 48.1% with CAD. There was no significant change in the positive predictive value or negative predictive value. Conclusion: The addition of a CAD system to chest radiography interpretation statistically improves the detection of lung nodules without affecting its specificity. Thus suggesting CAD would improve overall detection of lung nodules.

Effectiveness of an e-Learning Platform for Image Interpretation Education of Medical Staff and Students.

Medical staff must be able to perform accurate initial interpretations of radiography to prevent diagnostic errors. Education in medical image interpretation is an ongoing need that is addressed by text-based and e-learning platforms. The effectiveness of these methods has been previously reported. Here, we describe the effectiveness of an e-learning platform used for medical image interpretation education. Ten third-year medical students without previous experience in chest radiography interpretation were provided with e-learning instructions. Accuracy of diagnosis using chest radiography was provided before and after e-learning education. We measured detection accuracy for two image groups: nodular shadow and ground-glass shadow. We also distributed the e-learning system to the two groups and analyzed the effectiveness of education for both types of image shadow. The mean correct answer rate after the 2-week e-learning period increased from 34.5 to 72.7%. Diagnosis of the ground glass shadow improved significantly more than that of the mass shadow. Education using the e-leaning platform is effective for interpretation of chest radiography results. E-learning is particularly effective for the interpretation of chest radiography images containing ground glass shadow.

Computer-Aided Reporting of Chest Radiographs: Efficient and Effective Screening in the Value-Based Imaging Era.

In the post-PACS era, mammography is unique in adopting specialized ergonomic interfaces to improve efficiency in a high volume setting. Chest radiography is also a high volume area of radiology. The authors hypothesize that applying a novel interface for chest radiography interpretation and reporting could create high productivity while maintaining quality. A custom version of the ClearCanvas open source software, EzRad, was created with a workflow re-designed specifically for tuberculosis screening chest radiographs, which utilized standardized computer generated reports. The preliminary reports from 881,792 studies evaluated by radiology residents over a nine-year period were analyzed for productivity as RVU/FTE and compared to the finalized reports from a subspecialty attending chest radiologist for accuracy. Radiology residents were able to produce 7480 RVU/FTE per year in screening chest radiography productivity when using a custom interface at a large academic medical center with a miss rate of 0.1%. Sensitivity was 77% and specificity was 99.9%. An ergonomic user interface allowed high productivity in interpretation of chest radiography for tuberculosis screening while maintaining quality.

Development of a Simple Reliable Radiographic Scoring System to Aid the Diagnosis of Pulmonary Tuberculosis

RationaleChest radiography is sometimes the only method available for investigating patients with possible pulmonary tuberculosis (PTB) with negative sputum smears. However, interpretation of chest radiographs in this context lacks specificity for PTB, is subjective and is neither standardized nor reproducible. Efforts to improve the interpretation of chest radiography are warranted.ObjectivesTo develop a scoring system to aid the diagnosis of PTB, using features recorded with the Chest Radiograph Reading and Recording System (CRRS).MethodsChest radiographs of outpatients with possible PTB, recruited over 3 years at clinics in South Africa were read by two independent readers using the CRRS method. Multivariate analysis was used to identify features significantly associated with culture-positive PTB. These were weighted and used to generate a score.Results473 patients were included in the analysis. Large upper lobe opacities, cavities, unilateral pleural effusion and adenopathy were significantly associated with PTB, had high inter-reader reliability, and received 2, 2, 1 and 2 points, respectively in the final score. Using a cut-off of 2, scores below this threshold had a high negative predictive value (91.5%, 95%CI 87.1,94.7), but low positive predictive value (49.4%, 95%CI 42.9,55.9). Among the 382 TB suspects with negative sputum smears, 229 patients had scores <2; the score correctly ruled out active PTB in 214 of these patients (NPV 93.4%; 95%CI 89.4,96.3). The score had a suboptimal negative predictive value in HIV-infected patients (NPV 86.4, 95% CI 75,94).ConclusionsThe proposed scoring system is simple, and reliably ruled out active PTB in smear-negative HIV-uninfected patients, thus potentially reducing the need for further tests in high burden settings. Validation studies are now required.

Up-front staging of suspected lung cancer with PET-CT – New horizon or false dawn?

Up-front staging of suspected lung cancer with PET-CT – New horizon or false dawn?

Emergency department interpretation of chest radiography

Emergency department interpretation of chest radiography