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nlm-article

Cover Neurosurgical Focus: Video

Flow diversion of a dissecting PICA aneurysm

Tyler Lazaro, Viren Vasandani, Ariadna Robledo, Nisha Gadgil, and Peter Kan

A 47-year-old female with a history of a ruptured left posterior inferior cerebellar artery (PICA) aneurysm, status post coil embolization and retreatment for recurrence, presented with evidence of a recurrent dissecting PICA aneurysm. Given that these aneurysms are considered high risk and have a greater propensity for rupture than anterior circulation aneurysms, retreatment was recommended. With the patient’s strong preference for endovascular therapy, flow diversion with a Silk Vista Baby was performed. Given the low-profile design of the device, a radial artery approach and coaxial technique were used to deploy the flow diverter. The device was successfully placed, with complete obliteration of the aneurysm after 1 year.

The video can be found here: https://stream.cadmore.media/r10.3171/2022.7.FOCVID2247

Free access

nlm-article

Cover Journal of Neurosurgery

Automated detection and analysis of subdural hematomas using a machine learning algorithm

Marco Colasurdo, Nir Leibushor, Ariadna Robledo, Viren Vasandani, Zean Aaron Luna, Abhijit S. Rao, Roberto Garcia, Visish M. Srinivasan, Sunil A. Sheth, Naama Avni, Moleen Madziva, Mor Berejick, Goni Sirota, Aielet Efrati, Avraham Meisel, Hashem Shaltoni, and Peter Kan

OBJECTIVE

Machine learning algorithms have shown groundbreaking results in neuroimaging. Herein, the authors evaluate the performance of a newly developed convolutional neural network (CNN) to detect and quantify the thickness, volume, and midline shift (MLS) of subdural hematoma (SDH) from noncontrast head CT (NCHCT).

METHODS

NCHCT studies performed for the evaluation of head trauma in consecutive patients between July 2018 and April 2021 at a single institution were retrospectively identified. Ground truth determination of SDH, thickness, and MLS was established by the neuroradiology report. The primary outcome was performance of the CNN in detecting SDH in an external validation set, as measured using area under the receiver operating characteristic curve analysis. Secondary outcomes included accuracy for thickness, volume, and MLS.

RESULTS

Among 263 cases with valid NCHCT according to the study criteria, 135 patients (51%) were male, the mean (± standard deviation) age was 61 ± 23 years, and 70 patients were diagnosed with SDH on neuroradiologist evaluation. The median SDH thickness was 11 mm (IQR 6 mm), and 16 patients had a median MLS of 5 mm (IQR 2.25 mm). In the independent data set, the CNN performed well, with sensitivity of 91.4% (95% CI 82.3%–96.8%), specificity of 96.4% (95% CI 92.7%–98.5%), and accuracy of 95.1% (95% CI 91.7%–97.3%); sensitivity for the subgroup with an SDH thickness above 10 mm was 100%. The maximum thickness mean absolute error was 2.75 mm (95% CI 2.14–3.37 mm), whereas the MLS mean absolute error was 0.93 mm (95% CI 0.55–1.31 mm). The Pearson correlation coefficient computed to determine agreement between automated and manual segmentation measurements was 0.97 (95% CI 0.96–0.98).

CONCLUSIONS

The described Viz.ai SDH CNN performed exceptionally well at identifying and quantifying key features of SDHs in an independent validation imaging data set.

Restricted access

nlm-article

Cover Journal of Neurosurgery

Validation of an automated machine learning algorithm for the detection and analysis of cerebral aneurysms

Marco Colasurdo, Daphna Shalev, Ariadna Robledo, Viren Vasandani, Zean Aaron Luna, Abhijit S. Rao, Roberto Garcia, Gautam Edhayan, Visish M. Srinivasan, Sunil A. Sheth, Yoni Donner, Orin Bibas, Nicole Limzider, Hashem Shaltoni, and Peter Kan

OBJECTIVE

Machine learning algorithms have shown groundbreaking results in neuroimaging. The authors herein evaluated the performance of a newly developed convolutional neural network (CNN) to detect and analyze intracranial aneurysms (IAs) on CTA.

METHODS

Consecutive patients with CTA studies between January 2015 and July 2021 at a single center were identified. The ground truth determination of cerebral aneurysm presence or absence was made from the neuroradiology report. The primary outcome was the performance of the CNN in detecting IAs in an external validation set, measured using area under the receiver operating characteristic curve statistics. Secondary outcomes included accuracy for location and size measurement.

RESULTS

The independent validation imaging data set consisted of 400 patients with CTA studies, median age 40 years (IQR 34 years) and 141 (35.3%) of whom were male; 193 patients (48.3%) had a diagnosis of IA on neuroradiologist evaluation. The median maximum IA diameter was 3.7 mm (IQR 2.5 mm). In the independent validation imaging data set, the CNN performed well with 93.8% sensitivity (95% CI 0.87–0.98), 94.2% specificity (95% CI 0.90–0.97), and a positive predictive value of 88.2% (95% CI 0.80–0.94) in the subgroup with an IA diameter ≥ 4 mm.

CONCLUSIONS

The described Viz.ai Aneurysm CNN performed well in identifying the presence or absence of IAs in an independent validation imaging set. Further studies are necessary to investigate the impact of the software on detection rates in a real-world setting.