Deep Learning–Assisted Diagnosis of Cerebral Aneurysms Using the HeadXNet Model

Park, Allison; Chute, Chris; Rajpurkar, Pranav; Lou, Joe; Ball, Robyn L.; Shpanskaya, Katie; Jabarkheel, Rashad; Kim, Lily H.; McKenna, Emily; Tseng, Joe; Ni, Jason C.; Wishah, Fidaa; Wittber, Fred; Hong, David S.; Wilson, Thomas J.; Halabi, Safwan; Basu, Sanjay; Patel, Bhavik N.; Lungren, Matthew P.; Ng, Andrew Y.; Yeom, Kristen W.

doi:10.1001/jamanetworkopen.2019.5600

Cited by 191 publications

(143 citation statements)

References 33 publications

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“…8,9 However, providing rapid and accurate diagnostic imaging is increasingly difficult to sustain for many medical systems and radiology providers as utilization has expanded; for example, CTPA usage alone in the emergency setting has increased 27-fold over the past 2 decades. 10,11 Applications of deep learning have already shown significant promise in medical imaging including chest and extremity Xrays, [12][13][14][15] head CT, 16 and musculoskeletal magnetic resonance imaging (MRI). 17 But despite the potential clinical and engineering advantages for utilization of deep learning automated PE classification on CTPA studies, significant development challenges remain when compared to other applications.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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Pulmonary embolism (PE) is a life-threatening clinical problem and computed tomography pulmonary angiography (CTPA) is the gold standard for diagnosis. Prompt diagnosis and immediate treatment are critical to avoid high morbidity and mortality rates, yet PE remains among the diagnoses most frequently missed or delayed. In this study, we developed a deep learning model-PENet, to automatically detect PE on volumetric CTPA scans as an end-to-end solution for this purpose. The PENet is a 77-layer 3D convolutional neural network (CNN) pretrained on the Kinetics-600 dataset and fine-tuned on a retrospective CTPA dataset collected from a single academic institution. The PENet model performance was evaluated in detecting PE on data from two different institutions: one as a hold-out dataset from the same institution as the training data and a second collected from an external institution to evaluate model generalizability to an unrelated population dataset. PENet achieved an AUROC of 0.84 [0.82-0.87] on detecting PE on the hold out internal test set and 0.85 [0.81-0.88] on external dataset. PENet also outperformed current state-of-the-art 3D CNN models. The results represent successful application of an end-to-end 3D CNN model for the complex task of PE diagnosis without requiring computationally intensive and time consuming preprocessing and demonstrates sustained performance on data from an external institution. Our model could be applied as a triage tool to automatically identify clinically important PEs allowing for prioritization for diagnostic radiology interpretation and improved care pathways via more efficient diagnosis.npj Digital Medicine (2020) 3:61 ; https://doi.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…CTA images have been studied by combining a neural network segmentation model (the HeadXNet model) to augment diagnostic performance in the detection of aneurysms. 26 In this study, the clinicians showed significant increases in sensitivity, accuracy, and interrater agreement when they were augmented with the model. However, the lack of a reference standard and external data verification, as well as the focus only on nonruptured aneurysms of .3 mm, limited the generalization and further application of the model.…”

Section: Brief Overview Of Aimentioning

confidence: 72%

Artificial Intelligence in the Management of Intracranial Aneurysms: Current Status and Future Perspectives

Shi

Schoepf

et al. 2020

AJNR Am J Neuroradiol

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Intracranial aneurysms with subarachnoid hemorrhage lead to high morbidity and mortality. It is of critical importance to detect aneurysms, identify risk factors of rupture, and predict treatment response of aneurysms to guide clinical interventions. Artificial intelligence has received worldwide attention for its impressive performance in image-based tasks. Artificial intelligence serves as an adjunct to physicians in a series of clinical settings, which substantially improves diagnostic accuracy while reducing physicians' workload. Computerassisted diagnosis systems of aneurysms based on MRA and CTA using deep learning have been evaluated, and excellent performances have been reported. Artificial intelligence has also been used in automated morphologic calculation, rupture risk stratification, and outcomes prediction with the implementation of machine learning methods, which have exhibited incremental value. This review summarizes current advances of artificial intelligence in the management of aneurysms, including detection and prediction. The challenges and future directions of clinical implementations of artificial intelligence are briefly discussed. ABBREVIATIONS: AI¼ artificial intelligence; AUC ¼ area under the curve; CAD ¼ computer-assisted diagnostics; DL ¼ deep learning; FP ¼ false-positive; ML ¼ machine learning; SVM ¼ support vector machines

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“…Explorations on DL combined with MRA have reported decent results for IA detection 23,24 . While CTA based CAD system has been rarely reported, and only two recently published studies can be found, to the best of our knowledge 25,26 . Notably, these models were trained in small sample size, without the gold standard, and were not tested in different scenarios, thus they were not adequate to apply in real-world clinical settings, which may fall into the 'AI chasm' 27 .…”

mentioning

confidence: 94%

Clinically Applicable Deep Learning for Intracranial Aneurysm Detection in Computed Tomography Angiography Images: A Comprehensive Multicohort Study

Chen

Chi

et al. 2020

Preprint

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Intracranial aneurysm is a common life-threatening disease. CTA is recommended as a standard diagnosis tool, while the interpretation is time-consuming and challenging. We presented a novel deep-learning-based framework trained on 1,177 DSA verified bone-removal CTA cases. The framework had excellent tolerance to the influence of occult cases of CTA-negative but DSA-positive aneurysms, image quality, and manufacturers. Simulated real-world studies were conducted in consecutive internal and external cohorts, achieving improved sensitivity and negative predictive value than radiologists. A specific cohort of suspected acute ischemic stroke was employed and found 96.8% predicted-negative cases can be trusted with high confidence, leading to reducing in human burden. A prospective study is warranted to determine whether the algorithm could improve patients' care in comparison to radiologists' assessment.

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Deep Learning–Assisted Diagnosis of Cerebral Aneurysms Using the HeadXNet Model

Cited by 191 publications

References 33 publications

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

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

Artificial Intelligence in the Management of Intracranial Aneurysms: Current Status and Future Perspectives

Clinically Applicable Deep Learning for Intracranial Aneurysm Detection in Computed Tomography Angiography Images: A Comprehensive Multicohort Study

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