Impact of an AI software on the diagnostic performance and reading time for the detection of cerebral aneurysms on time of flight MR-angiography

PURPOSE

To evaluate the impact of an AI-based software trained to detect cerebral aneurysms on TOF-MRA on the diagnostic performance and reading times across readers with varying experience levels.

METHODS

One hundred eighty-six MRI studies were reviewed by six readers to detect cerebral aneurysms. Initially, readings were assisted by the CNN-based software mdbrain. After 6 weeks, a second reading was conducted without software assistance. The results were compared to the consensus reading of two neuroradiological specialists and sensitivity (lesion and patient level), specificity (patient level), and false positives per case were calculated for the group of all readers, for the subgroup of physicians, and for each individual reader. Also, reading times for each reader were measured.

RESULTS

The dataset contained 54 aneurysms. The readers had no experience (three medical students), 2 years experience (resident in neuroradiology), 6 years experience (radiologist), and 12 years (neuroradiologist). Significant improvements of overall specificity and the overall number of false positives per case were observed in the reading with AI support. For the physicians, we found significant improvements of sensitivity on lesion and patient level and false positives per case. Four readers experienced reduced reading times with the software, while two encountered increased times.

CONCLUSION

In the reading with the AI-based software, we observed significant improvements in terms of specificity and false positives per case for the group of all readers and significant improvements of sensitivity and false positives per case for the physicians. Further studies are needed to investigate the effects of the AI-based software in a prospective setting.

Data Extraction from Free-Text Reports on Mechanical Thrombectomy in Acute Ischemic Stroke Using ChatGPT: A Retrospective Analysis

Background Procedural details of mechanical thrombectomy in patients with ischemic stroke are important predictors of clinical outcome and are collected for prospective studies or national stroke registries. To date, these data are collected manually by human readers, a labor-intensive task that is prone to errors. Purpose To evaluate the use of the large language models (LLMs) GPT-4 and GPT-3.5 to extract data from neuroradiology reports on mechanical thrombectomy in patients with ischemic stroke. Materials and Methods This retrospective study included consecutive reports from patients with ischemic stroke who underwent mechanical thrombectomy between November 2022 and September 2023 at institution 1 and between September 2016 and December 2019 at institution 2. A set of 20 reports was used to optimize the prompt, and the ability of the LLMs to extract procedural data from the reports was compared using the McNemar test. Data manually extracted by an interventional neuroradiologist served as the reference standard. Results A total of 100 internal reports from 100 patients (mean age, 74.7 years ± 13.2 [SD]; 53 female) and 30 external reports from 30 patients (mean age, 72.7 years ± 13.5; 18 male) were included. All reports were successfully processed by GPT-4 and GPT-3.5. Of 2800 data entries, 2631 (94.0% [95% CI: 93.0, 94.8]; range per category, 61%-100%) data points were correctly extracted by GPT-4 without the need for further postprocessing. With 1788 of 2800 correct data entries, GPT-3.5 produced fewer correct data entries than did GPT-4 (63.9% [95% CI: 62.0, 65.6]; range per category, 14%-99%; P < .001). For the external reports, GPT-4 extracted 760 of 840 (90.5% [95% CI: 88.3, 92.4]) correct data entries, while GPT-3.5 extracted 539 of 840 (64.2% [95% CI: 60.8, 67.4]; P < .001). Conclusion Compared with GPT-3.5, GPT-4 more frequently extracted correct procedural data from free-text reports on mechanical thrombectomy performed in patients with ischemic stroke. © RSNA, 2024 Supplemental material is available for this article.

First Experience with the Nimbus Stentretriever

Purpose

To share our first experience with the Nimbus stentretriever, a multizone device designed to assist neurointerventionalists in handling fibrin-rich clots in endovascular stroke treatment.

Methods

We retrospectively analyzed the data of patients who were treated with the Nimbus stentretriever at our high-volume stroke center between May 2021 and May 2022. We evaluated the number of passes before Nimbus was used, the number of passes with nimbus, as well as the recanalization success before and after Nimbus according to the modified treatment in cerebral ischemia (mTICI) scale. Also, patient characteristics, procedural times and clinical outcomes were documented.

Results

A total of 21 consecutive patients were included in the study. An mTICI 2b/3 could be achieved in 76.2% and mTICI 2c/3 could be achieved in 57.1%. The mean number of passes was 3.4 before the use of Nimbus, 2.2 with Nimbus, and 5.4 for all passes with and without Nimbus and 4 occlusions (19.0%) were successfully recanalized with direct aspiration after the use of Nimbus. We observed seven subarachnoid hemorrhages (33.3%) and two cases of vasospasm.

Conclusion

In our series, the use of Nimbus resulted in successful recanalization in half of the patients after otherwise unsuccessful thrombectomy maneuvers; therefore, it should be considered as a rescue option if the maneuver with conventional stent retrievers was unsuccessful.

Automated Detection of Cerebral Aneurysms on TOF-MRA Using a Deep Learning Approach: An External Validation Study

BACKGROUND AND PURPOSE

Cerebral aneurysms yield the risk of rupture, severe disability and death. Thus, early detection of cerebral aneurysms is crucial to ensure timely treatment, if necessary. AI-based software tools are expected to enhance radiologists' performance in detecting pathologies like cerebral aneurysms in the future. Our aim was to evaluate the diagnostic performance of an artificial intelligence-based software designed to detect intracranial aneurysms on TOF-MRA.

MATERIALS AND METHODS

One hundred ninety-one MR imaging data sets were analyzed using the software mdbrain for the presence of intracranial aneurysms on TOF-MRA obtained using two 3T MR imaging scanners or a 1.5T MR imaging scanner according to our clinical standard protocol. The results were compared with the reading of an experienced radiologist as a criterion standard to measure the sensitivity, specificity, positive and negative predictive values, and accuracy of the software. Additionally, detection rates depending on size, morphology, and location of the aneurysms were evaluated.

RESULTS

Fifty-four aneurysms were detected by the expert reader. The overall sensitivity of the software for the detection of cerebral aneurysms was 72.6%, the specificity was 87.2%, and the accuracy was 82.6%. The positive predictive value was 67.9%, and the negative predictive value was 88.5%. We observed a sensitivity of 100% for saccular aneurysms of >5 mm without signs of thrombosis and low detection rates for fusiform or thrombosed aneurysms of 33.3% and 16.7%, respectively. Of 8 aneurysms that were not included in the initial written reports but were detected by the expert reader, retrospectively, 4 were detected by the software.

CONCLUSIONS

Our data suggest that the software can assist radiologists in reporting TOF-MRA. The software was highly reliable in detecting saccular aneurysms, while for fusiform or thrombosed aneurysms, further improvements are needed. Further studies are necessary to investigate the impact of the software on detection rates, interrater reliability, and reading times.

Late-Onset Vaccine-Induced Immune Thombotic Thrombocytopenia (VITT) with Cerebral Venous Sinus Thrombosis

Objectives

Vaccine-induced thrombotic thrombocytopenia (VITT) is a rare complication after adenoviral vector vaccination against COVID-19 reported up to 24 days after ChAdOx1 nCOV-19 (AZD1222) vaccination. This report describes a case with a significantly later onset of VITT with cerebral venous sinus thrombosis.

Case description

We report a 42-year-old woman presenting to the emergency department 53 days after AZD1222 vaccination with sudden onset sensory aphasia and an 18-day history of headache. Cranial computed tomography (CT) showed acute intracranial hemorrhage and CT venogram demonstrated thrombosis of the left vein of Labbé and transverse and sigmoid sinus. D-dimers were elevated and despite a normal platelet count, platelet-activating anti-PF4 antibody testing was positive, confirming the diagnosis of VITT. The patient was treated with intravenous immunoglobulins and argatroban, and was discharged without any neurological deficit on day 12.

Conclusion

Our report of VITT with symptom onset on day 35 and diagnosis of cerebral sinuous thrombosis on day 53 after AZD1222 vaccination significantly enhances the time window during which VITT may occur.