Deep learning-enabled system for rapid pneumothorax screening on chest CT

Li, Xiang; Thrall, James H.; Digumarthy, Subba R.; Kalra, Mannudeep K.; Pandharipande, Pari V.; Zhang, Bowen; Nitiwarangkul, Chayanin; Singh, Ramandeep; Khera, Ruhani Doda; Li, Quanzheng

doi:10.1016/j.ejrad.2019.108692

Cited by 39 publications

(32 citation statements)

References 23 publications

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“…The second study [11] reported a specificity of 91%. Similar results were reported by a recent study [13], with an overall accuracy of 97%, a sensitivity of 100%, and a specificity of 83%. A high sensitivity is important for detecting all cases of a relevant condition to avoid detrimental consequences.…”

Section: Discussionsupporting

confidence: 91%

“…However, such algorithms may be prone to bias through concurrent pathologies [12]; consequently, scans with these pathologies were excluded in one of these studies [9]. In these studies, pneumothorax quantification on chest CT was performed using different methods, obtaining a sensitivity of 100%; however, specificity ranged from 10 to 100%, with low values in the cases of small pneumothorax or concurrent pathologies such as emphysema and bullae [10,13].…”

Section: Introductionmentioning

confidence: 99%

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Deep learning detection and quantification of pneumothorax in heterogeneous routine chest computed tomography

Röhrich

Schlegl²,

Bardach

et al. 2020

Eur Radiol Exp

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Background: Automatically detecting and quantifying pneumothorax on chest computed tomography (CT) may impact clinical decision-making. Machine learning methods published so far struggle with the heterogeneity of technical parameters and the presence of additional pathologies, highlighting the importance of stable algorithms. Methods: A deep residual UNet was developed and evaluated for automated, volume-level pneumothorax grading (i.e., labelling a volume whether a pneumothorax was present or not), and pixel-level classification (i.e., segmentation and quantification of pneumothorax), on a retrospective series of routine chest CT data. Ground truth annotations were provided by radiologists. The fully automated pixel-level pneumothorax segmentation method was trained using 43 chest CT scans and evaluated on 9 chest CT scans with pixel-level annotation basis and 567 chest CT scans on a volume-level basis. Results: This method achieved a receiver operating characteristic area under the curve (AUC) of 0.98, an average precision of 0.97, and a Dice similarity coefficient (DSC) of 0.94. This segmentation performance resulted to be similar to the inter-rater segmentation accuracy of two radiologists, who achieved a DSC of 0.92. The comparison of manual and automated pneumothorax quantification yielded a Pearson correlation coefficient of 0.996. The volume-level pneumothorax grading accuracy was evaluated on 567 chest CT scans and yielded an AUC of 0.98 and an average precision of 0.95. Conclusions: We proposed a deep learning method for the detection and quantification of pneumothorax in heterogeneous routine clinical data that may facilitate the automated triage of urgent examinations and enable treatment decision support.

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Section: Discussionsupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Deep learning detection and quantification of pneumothorax in heterogeneous routine chest computed tomography

Röhrich

Schlegl²,

Bardach

et al. 2020

Eur Radiol Exp

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“…RT-PCR to diagnose COVID-19 has its own limitations: the test is not universally available, turnaround times can be lengthy, and reported sensitivities vary [ 20 ]. The combination of chest CT and ML has the advantage of obtaining an immediate result – practically after image reconstruction and while the patient is still on the examination bed – and independent of the presence of the radiologist [ [21] , [22] , [23] , [24] ]. This scenario could potentially play a role in either minor peripheral hospitals or places with radiological personnel shortage due to various reasons including a high load of COVID-19 cases and could also act as a support for inexperienced radiologists during night-duty.…”

Section: Discussionmentioning

confidence: 99%

Deep learning analysis provides accurate COVID-19 diagnosis on chest computed tomography

Javor

Kaplan

et al. 2020

European Journal of Radiology

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Introduction Computed Tomography is an essential diagnostic tool in the management of COVID-19. Considering the large amount of examinations in high case-load scenarios, an automated tool could facilitate and save critical time in the diagnosis and risk stratification of the disease. Methods A novel deep learning derived machine learning (ML) classifier was developed using a simplified programming approach and an open source dataset consisting of 6868 chest CT images from 418 patients which was split into training and validation subsets. The diagnostic performance was then evaluated and compared to experienced radiologists on an independent testing dataset. Diagnostic performance metrics were calculated using Receiver Operating Characteristics (ROC) analysis. Operating points with high positive (>10) and low negative (<0.01) likelihood ratios to stratify the risk of COVID-19 being present were identified and validated. Results The model achieved an overall accuracy of 0.956 (AUC) on an independent testing dataset of 90 patients. Both rule-in and rule out thresholds were identified and tested. At the rule-in operating point, sensitivity and specificity were 84.4% and 93.3% and did not differ from both radiologists (p > 0.05). At the rule-out threshold, sensitivity (100%) and specificity (60%) differed significantly from the radiologists (p < 0.05). Likelihood ratios and a Fagan nomogram provide prevalence independent test performance estimates. Conclusion Accurate diagnosis of COVID-19 using a basic deep learning approach is feasible using open-source CT image data. In addition, the machine learning classifier provided validated rule-in and rule-out criteria could be used to stratify the risk of COVID-19 being present.

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“…Unlike traditional ML, the potential to learn data representations obviates the need to use handcrafted features for DL. Therefore, DL applications are increasing in radiology for object detection, image segmentation, and classification [69,70]. While most applications of DL focus on image analysis, which is invaluable to radiology, DL has found a new turf in CT image reconstruction [71,72].…”

Section: Deep Learning-based Reconstructionmentioning

confidence: 99%

Artificial intelligence in image reconstruction: The change is here

Singh

Wang

et al. 2020

Physica Medica

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Innovations in CT have been impressive among imaging and medical technologies in both the hardware and software domain. The range and speed of CT scanning improved from the introduction of multidetector-row CT scanners with wide-array detectors and faster gantry rotation speeds. To tackle concerns over rising radiation doses from its increasing use and to improve image quality, CT reconstruction techniques evolved from filtered back projection to commercial release of iterative reconstruction techniques, and recently, of deep learning (DL)based image reconstruction. These newer reconstruction techniques enable improved or retained image quality versus filtered back projection at lower radiation doses. DL can aid in image reconstruction with training data without total reliance on the physical model of the imaging process, unique artifacts of PCD-CT due to charge sharing, K-escape, fluorescence x-ray emission, and pulse pileups can be handled in the data-driven fashion. With sufficiently reconstructed images, a well-designed network can be trained to upgrade image quality over a practical/clinical threshold or define new/killer applications. Besides, the much smaller detector pixel for PCD-CT can lead to huge computational costs with traditional model-based iterative reconstruction methods whereas deep networks can be much faster with training and validation. In this review, we present techniques, applications, uses, and limitations of deep learning-based image reconstruction methods in CT.

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Deep learning-enabled system for rapid pneumothorax screening on chest CT

Cited by 39 publications

References 23 publications

Deep learning detection and quantification of pneumothorax in heterogeneous routine chest computed tomography

Deep learning detection and quantification of pneumothorax in heterogeneous routine chest computed tomography

Deep learning analysis provides accurate COVID-19 diagnosis on chest computed tomography

Artificial intelligence in image reconstruction: The change is here

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