Deep Learning–Assisted Diagnosis of Cerebral Aneurysms Using the HeadXNet Model
Authors: Allison Park, Chris Chute, Pranav Rajpurkar, Joe Lou, Robyn L. Ball, Katie Shpanskaya, Rashad Jabarkheel, Lily H. Kim, Emily McKenna, Joe Tseng, Jason Ni, Fidaa Wishah, Fred Wittber, David S. Hong, Thomas J. Wilson, Safwan Halabi, Sanjay Basu, Bhavik N. Patel, Matthew P. Lungren, Andrew Y. Ng, Kristen W. Yeom
Journal: JAMA Network Open (2019)

<h3>Importance</h3> Deep learning has the potential to augment clinician performance in medical imaging interpretation and reduce time to diagnosis through automated segmentation. Few studies to date have explored this topic. <h3>Objective</h3> To develop and apply a neural network segmentation model (the HeadXNet model) capable of generating precise voxel-by-voxel predictions of intracranial aneurysms on head computed tomographic angiography (CTA) imaging to augment clinicians' intracranial aneurysm diagnostic performance. <h3>Design, Setting, and Participants</h3> In this diagnostic study, a 3-dimensional convolutional neural network architecture was developed using a training set of 611 head CTA examinations to generate aneurysm segmentations. Segmentation outputs from this support model on a test set of 115 examinations were provided to clinicians. Between August 13, 2018, and October 4, 2018, 8 clinicians diagnosed the presence of aneurysm on the test set, both with and without model augmentation, in a crossover design using randomized order and a 14-day washout period. Head and neck examinations performed between January 3, 2003, and May 31, 2017, at a single academic medical center were used to train, validate, and test the model. Examinations positive for aneurysm had at least 1 clinically significant, nonruptured intracranial aneurysm. Examinations with hemorrhage, ruptured aneurysm, posttraumatic or infectious pseudoaneurysm, arteriovenous malformation, surgical clips, coils, catheters, or other surgical hardware were excluded. All other CTA examinations were considered controls. <h3>Main Outcomes and Measures</h3> Sensitivity, specificity, accuracy, time, and interrater agreement were measured. Metrics for clinician performance with and without model augmentation were compared. <h3>Results</h3> The data set contained 818 examinations from 662 unique patients with 328 CTA examinations (40.1%) containing at least 1 intracranial aneurysm and 490 examinations (59.9%) without intracranial aneurysms. The 8 clinicians reading the test set ranged in experience from 2 to 12 years. Augmenting clinicians with artificial intelligence–produced segmentation predictions resulted in clinicians achieving statistically significant improvements in sensitivity, accuracy, and interrater agreement when compared with no augmentation. The clinicians' mean sensitivity increased by 0.059 (95% CI, 0.028-0.091; adjusted<i>P</i> = .01), mean accuracy increased by 0.038 (95% CI, 0.014-0.062; adjusted<i>P</i> = .02), and mean interrater agreement (Fleiss κ) increased by 0.060, from 0.799 to 0.859 (adjusted<i>P</i> = .05). There was no statistically significant change in mean specificity (0.016; 95% CI, −0.010 to 0.041; adjusted<i>P</i> = .16) and time to diagnosis (5.71 seconds; 95% CI, 7.22-18.63 seconds; adjusted<i>P</i> = .19). <h3>Conclusions and Relevance</h3> The deep learning model developed successfully detected clinically significant intracranial aneurysms on CTA. This suggests that integration of an artificial intelligence–assisted diagnostic model may augment clinician performance with dependable and accurate predictions and thereby optimize patient care.