Did I Miss That: Subtle and Commonly Missed Findings on Chest Radiographs

Ropp, Alan; Waite, Stephen; Reede, Deborah L.; Patel, Jay

doi:10.1067/j.cpradiol.2014.09.003

Cited by 15 publications

(10 citation statements)

References 29 publications

(37 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The addition of false or inaccurate information decreases the diagnostic accuracy of radiologists when interpreting chest radiographs in humans. 12,14,39 The same results were replicated in a CT study, where the addition of false historical information decreased the accuracy of the radiological diagnosis; conversely, when the clinical information was accurate, the radiological diagnosis improved. 40 Further research has also raised concerns about the significant increase in false positives when radiologists are instructed to only look for specific abnormalities (eg, pulmonary nodules).…”

Section: Discussionmentioning

confidence: 71%

Association between clinical history in the radiographic request and diagnostic accuracy of thorax radiographs in dogs: A retrospective case‐control study

Arruda Bergamaschi,

Huber,

Ludewig

et al. 2023

Veterinary Internal Medicne

View full text Add to dashboard Cite

BackgroundThe effect of clinical history on the interpretation of radiographs has been widely researched in human medicine. There is, however, no data on this topic in veterinary medicine.Hypothesis/ObjectivesDiagnostic accuracy would improve when history was supplied.AnimalsThirty client‐owned dogs with abnormal findings on thoracic radiographs and confirmation of the disease, and 30 healthy client‐owned controls were drawn retrospectively.MethodsRetrospective case‐control study. Sixty radiographic studies of the thorax were randomized and interpreted by 6 radiologists; first, with no access to the clinical information; and a second time with access to all pertinent clinical information and signalment.ResultsA significant increase in diagnostic accuracy was noted when clinical information was provided (64.4% without and 75.2% with clinical information; P = .002). There was no significant difference in agreement between radiologists when comparing no clinical information and with clinical information (Kappa 0.313 and 0.300, respectively).Conclusions and Clinical ImportanceThe addition of pertinent clinical information to the radiographic request significantly improves the diagnostic accuracy of thorax radiographs of dogs and is recommended as standard practice.

show abstract

Section: Discussionmentioning

confidence: 71%

Association between clinical history in the radiographic request and diagnostic accuracy of thorax radiographs in dogs: A retrospective case‐control study

Arruda Bergamaschi,

Huber,

Ludewig

et al. 2023

Veterinary Internal Medicne

View full text Add to dashboard Cite

show abstract

“…5,6 With an increase in the population and the number of radiological examinations performed each year, the relative number of qualified radiologists is declining, resulting in backlogs and delays in timely medical imaging even in large organisations like the UK-National Health Service and the US-Department of Veterans Affairs. [7][8][9] Consequent to the increase in the volume of scans, and a decrease in the number of radiologists, are the issues of increasing radiologist burn-out and high interpretation errors. Therefore, having in place a system which can automate the task of quantification on chest radiograph will imply one less thing for the radiologist to worry about while ensuring that this important parameter is not skipped over from radiology reports to conserve time.…”

Section: Introductionmentioning

confidence: 99%

Observer performance evaluation of the feasibility of a deep learning model to detect cardiomegaly on chest radiographs

Ajmera

Kharat

Gupte³

et al. 2022

Acta Radiologica Open

View full text Add to dashboard Cite

Background Cardiothoracic ratio (CTR) is the ratio of the diameter of the heart to the diameter of the thorax. An abnormal CTR (>0.55) is often an indicator of an underlying pathological condition. The accurate prediction of an abnormal CTR chest X-rays (CXRs) aids in the early diagnosis of clinical conditions. Purpose We propose a deep learning (DL)-based model for automatic CTR calculation to assist radiologists with rapid diagnosis of cardiomegaly and thus optimise the radiology flow. Material and Methods The study population included 1012 posteroanterior CXRs from a single institution. The Attention U-Net DL architecture was used for the automatic calculation of CTR. An observer performance test was conducted to assess the radiologist’s performance in diagnosing cardiomegaly with and without artificial intelligence assistance. Results U-Net model exhibited a sensitivity of 0.80 [95% CI: 0.75, 0.85], specificity >99%, precision of 0.99 [95% CI: 0.98, 1], and a F1 score of 0.88 [95% CI: 0.85, 0.91]. Furthermore, the sensitivity of the reviewing radiologist in identifying cardiomegaly increased from 40.50% to 88.4% when aided by the AI-generated CTR. Conclusion Our segmentation-based AI model demonstrated high specificity (>99%) and sensitivity (80%) for CTR calculation. The performance of the radiologist on the observer performance test improved significantly with provision of AI assistance. A DL-based segmentation model for rapid quantification of CTR can therefore have significant potential to be used in clinical workflows by reducing radiologists’ burden and alerting to an abnormal enlarged heart early on.

show abstract

“…In a busy practice, radiologists may be asked to interpret hundreds of CXRs every day. The high volume of examinations causes delays in result reporting, and fatigue that increases the risk of missing subtle findings 4–6 . Automated tools that can help in triaging examinations and in highlighting regions of abnormality within these examinations can help improve the timeliness and accuracy of the CXR reports 7–9 …”

Section: Introductionmentioning

confidence: 99%

“…The high volume of examinations causes delays in result reporting, and fatigue that increases the risk of missing subtle findings. [4][5][6] Automated tools that can help in triaging examinations and in highlighting regions of abnormality within these examinations can help improve the timeliness and accuracy of the CXR reports. [7][8][9] Pneumonia usually manifests as one or more areas of increased opacity on a CXR.…”

Section: Introductionmentioning

confidence: 99%

Weakly supervised pneumonia localization in chest X‐rays using generative adversarial networks

2021

View full text Add to dashboard Cite

Purpose: Automatic localization of pneumonia on chest X‐rays (CXRs) is highly desirable both as an interpretive aid to the radiologist and for timely diagnosis of the disease. However, pneumonia's amorphous appearance on CXRs and complexity of normal anatomy in the chest present key challenges that hinder accurate localization. Existing studies in this area are either not optimized to preserve spatial information of abnormality or depend on expensive expert‐annotated bounding boxes. We present a novel generative adversarial network (GAN)‐based machine learning approach for this problem, which is weakly supervised (does not require any location annotations), was trained to retain spatial information, and can produce pixel‐wise abnormality maps highlighting regions of abnormality (as opposed to bounding boxes around abnormality). Methods: Our method is based on the Wasserstein GAN framework and, to the best of our knowledge, the first application of GANs to this problem. Specifically, from an abnormal CXR as input, we generated the corresponding pseudo normal CXR image as output. The pseudo normal CXR is the “hypothetical” normal, if the same abnormal CXR were not to have any abnormalities. We surmise that the difference between the pseudo normal and the abnormal CXR highlights the pixels suspected to have pneumonia and hence is our output abnormality map. We trained our algorithm on an “unpaired” data set of abnormal and normal CXRs and did not require any location annotations such as bounding boxes/segmentations of abnormal regions. Furthermore, we incorporated additional prior knowledge/constraints into the model and showed that they help improve localization performance. We validated the model on a data set consisting of 14 184 CXRs from the Radiological Society of North America pneumonia detection challenge. Results: We evaluated our methods by comparing the generated abnormality maps with radiologist annotated bounding boxes using receiver operating characteristic (ROC) analysis, image similarity metrics such as normalized cross‐correlation/mutual information, and abnormality detection rate.We also present visual examples of the abnormality maps, covering various scenarios of abnormality occurrence. Results demonstrate the ability to highlight regions of abnormality with the best method achieving an ROC area under the curve (AUC) of 0.77 and a detection rate of 85%.The GAN tended to perform better as prior knowledge/constraints were incorporated into the model. Conclusions: We presented a novel GAN based approach for localizing pneumonia on CXRs that (1) does not require expensive hand annotated location ground truth; and (2) was trained to produce abnormality maps at the pixel level as opposed to bounding boxes. We demonstrated the efficacy of our methods via quantitative and qualitative results.

show abstract

Did I Miss That: Subtle and Commonly Missed Findings on Chest Radiographs

Cited by 15 publications

References 29 publications

Association between clinical history in the radiographic request and diagnostic accuracy of thorax radiographs in dogs: A retrospective case‐control study

Association between clinical history in the radiographic request and diagnostic accuracy of thorax radiographs in dogs: A retrospective case‐control study

Observer performance evaluation of the feasibility of a deep learning model to detect cardiomegaly on chest radiographs

Weakly supervised pneumonia localization in chest X‐rays using generative adversarial networks

Contact Info

Product

Resources

About