PENet—a scalable deep-learning model for automated diagnosis of pulmonary embolism using volumetric CT imaging

Huang, Shih‐Yu; Kothari, Tanay; Banerjee, Imon; Chute, Chris; Ball, Robyn L.; Borus, Norah; Huang, Aihua; Patel, Bhavik N.; Rajpurkar, Pranav; Irvin, Jeremy; Dunnmon, Jared; Bledsoe, Joseph; Shpanskaya, Katie; Dhaliwal, Abhay; Zamanian, Roham T.; Ng, Andrew Y.; Lungren, Matthew P.

doi:10.1038/s41746-020-0266-y

Cited by 96 publications

(58 citation statements)

References 48 publications

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“…When tested, the deep learning model achieved an AUROC score of 0.84 with automatic detection of PE signs on the test set. Thus, the possibility of using deep learning to evaluate complex radiographic data of CTPA angiograms to detect PE was confirmed [55].…”

Section: Artificial Intelligence In Venous Thromboembolismmentioning

confidence: 90%

Artificial Intelligence Evidence-Based Current Status and Potential for Lower Limb Vascular Management

Butova

Shayakhmetov

Федин

et al. 2021

JPM

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Consultation prioritization is fundamental in optimal healthcare management and its performance can be helped by artificial intelligence (AI)-dedicated software and by digital medicine in general. The need for remote consultation has been demonstrated not only in the pandemic-induced lock-down but also in rurality conditions for which access to health centers is constantly limited. The term “AI” indicates the use of a computer to simulate human intellectual behavior with minimal human intervention. AI is based on a “machine learning” process or on an artificial neural network. AI provides accurate diagnostic algorithms and personalized treatments in many fields, including oncology, ophthalmology, traumatology, and dermatology. AI can help vascular specialists in diagnostics of peripheral artery disease, cerebrovascular disease, and deep vein thrombosis by analyzing contrast-enhanced magnetic resonance imaging or ultrasound data and in diagnostics of pulmonary embolism on multi-slice computed angiograms. Automatic methods based on AI may be applied to detect the presence and determine the clinical class of chronic venous disease. Nevertheless, data on using AI in this field are still scarce. In this narrative review, the authors discuss available data on AI implementation in arterial and venous disease diagnostics and care.

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Section: Artificial Intelligence In Venous Thromboembolismmentioning

confidence: 90%

Artificial Intelligence Evidence-Based Current Status and Potential for Lower Limb Vascular Management

Butova

Shayakhmetov

Федин

et al. 2021

JPM

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“…CNNs have been previously applied in pulmonary embolism detection based on labelling the occluding clots seen in CTPA as filling defects of intravascular contrast material in pulmonary arteries [39][40][41][42]. These studies have focused mainly on acute pulmonary embolism.…”

Section: Discussionmentioning

confidence: 99%

Performance of a 3D convolutional neural network in the detection of hypoperfusion at CT pulmonary angiography in patients with chronic pulmonary embolism: a feasibility study

et al. 2021

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Background Chronic pulmonary embolism (CPE) is a life-threatening disease easily misdiagnosed on computed tomography. We investigated a three-dimensional convolutional neural network (CNN) algorithm for detecting hypoperfusion in CPE from computed tomography pulmonary angiography (CTPA). Methods Preoperative CTPA of 25 patients with CPE and 25 without pulmonary embolism were selected. We applied a 48%–12%–40% training-validation-testing split (12 positive and 12 negative CTPA volumes for training, 3 positives and 3 negatives for validation, 10 positives and 10 negatives for testing). The median number of axial images per CTPA was 335 (min–max, 111–570). Expert manual segmentations were used as training and testing targets. The CNN output was compared to a method in which a Hounsfield unit (HU) threshold was used to detect hypoperfusion. Receiver operating characteristic area under the curve (AUC) and Matthew correlation coefficient (MCC) were calculated with their 95% confidence interval (CI). Results The predicted segmentations of CNN showed AUC 0.87 (95% CI 0.82–0.91), those of HU-threshold method 0.79 (95% CI 0.74–0.84). The optimal global threshold values were CNN output probability ≥ 0.37 and ≤ -850 HU. Using these values, MCC was 0.46 (95% CI 0.29–0.59) for CNN and 0.35 (95% CI 0.18–0.48) for HU-threshold method (average difference in MCC in the bootstrap samples 0.11 (95% CI 0.05–0.16). A high CNN prediction probability was a strong predictor of CPE. Conclusions We proposed a deep learning method for detecting hypoperfusion in CPE from CTPA. This model may help evaluating disease extent and supporting treatment planning.

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“…The long delay and high misdiagnosis rate is in part due to the rapid increase in utilization CTPA (27-fold in emergency settings). Many studies have attempted to automate PE diagnosis and patient triaging to alleviate the burden for radiologists [52,47,22]. However, few studies have directly included patient clinical history and demographic information as inputs to their model, even though patient EHR is crucial for accurate interpretation of medical images [16].…”

Section: Related Workmentioning

confidence: 99%

RadFusion: Benchmarking Performance and Fairness for Multimodal Pulmonary Embolism Detection from CT and EHR

Zhou¹,

Huang²,

Fries³

et al. 2021

Preprint

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Despite the routine use of electronic health record (EHR) data by radiologists to contextualize clinical history and inform image interpretation, the majority of deep learning architectures for medical imaging are unimodal, i.e., they only learn features from pixel-level information. Recent research revealing how race can be recovered from pixel data alone highlights the potential for serious biases in models which fail to account for demographics and other key patient attributes. Yet the lack of imaging datasets which capture clinical context, inclusive of demographics and longitudinal medical history, has left multimodal medical imaging underexplored. To better assess these challenges, we present RadFusion, a multimodal, benchmark dataset of 1794 patients with corresponding EHR data and high-resolution computed tomography (CT) scans labeled for pulmonary embolisms. We evaluate several representative multimodal fusion models and benchmark their fairness properties across protected subgroups, e.g., gender, race/ethnicity, age. Our results suggest that integrating imaging and EHR data can improve classification performance and robustness without introducing large disparities in the true positive rate between population groups.

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PENet—a scalable deep-learning model for automated diagnosis of pulmonary embolism using volumetric CT imaging

Cited by 96 publications

References 48 publications

Artificial Intelligence Evidence-Based Current Status and Potential for Lower Limb Vascular Management

Artificial Intelligence Evidence-Based Current Status and Potential for Lower Limb Vascular Management

Performance of a 3D convolutional neural network in the detection of hypoperfusion at CT pulmonary angiography in patients with chronic pulmonary embolism: a feasibility study

RadFusion: Benchmarking Performance and Fairness for Multimodal Pulmonary Embolism Detection from CT and EHR

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