Citations

Introduction: AI-driven software has been recommended as a means of triaging suspected large vessel occlusions (LVOs) on CT angiography (CTA), and different solutions are now commercially available. This study compared the performance of two software packages, Brainomix 360 Triage LVO and Rapid LVO, in prospective, real-world use. Methods: Consecutive patients scanned for suspected stroke at 3 comprehensive stroke centres from May to July 2024 (3 months) were included in the study. CTA, where available, was processed in real time by both Rapid and Brainomix software. Ground truth on vessel occlusion was ascertained using clinical review of electronic medical records. Occlusion location was coded as intracranial internal carotid artery terminus (ICA-T), middle cerebral artery (MCA) M1 or MCA M2. If more than one occlusion was observed, the most proximal of these was taken as the primary vessel affected. Results: There were 561 suspected stroke patients scanned during the study period across the three sites. Of these, CTA was sent for processing by both Rapid LVO and Brainomix 360 Triage LVO for 359 cases. Cases were excluded if no contrast was detected (n=17), leaving 342 cases (Table 1). Of these, proximal LVO (ICA-T or MCA M1) was confirmed in 14 cases and MCA M2 occlusions were present in 6 cases. Table 2 shows the LVO detection results for all cases from the two software packages. For both devices, sensitivity was higher when considering only proximal occlusions (ICA-T or M1). Performance was similar overall, but there was a trend towards differing sensitivity for more distal occlusions. As a composite measure, area under the curve (AUC) was higher for Brainomix than Rapid (Figure 1) but this difference was not significant. The positive predictive value (PPV) was low for both software packages due to the low incidence of LVO in the patient population. Conclusions: This study demonstrated consistent performance of two AI software devices, Brainomix 360 Triage LVO and Rapid LVO, for detection of LVO on CTA imaging, in prospective, real-world use. The low number of positive cases in the dataset (N=21) is a limitation of the study. There was a trend towards different performance for more distal vessel occlusion locations. Overall, the sensitivity observed in this dataset was in line with reports from previous retrospective studies for both software packages and underlines the importance of prospective evaluation to understand real-world performance.

Introduction Software packages for quantification of CT perfusion parameters allow quick visualization of perfusion deficits to support clinical decision making in acute ischemic stroke (AIS). Several packages are now commercially available for automated quantification of ischemic core, penumbra volumes, and mismatch ratios to indicate eligibility for reperfusion therapies. This study explored the agreement between two software packages, Brainomix 360 e‐CTP and Rapid CTP, in prospective, real‐world use. Methods Consecutive patients scanned for suspected stroke at three comprehensive stroke centers over 90 days from May to July 2024 were included in the study. Eligibility for the current analysis was limited to discharge diagnosis of AIS and CTP imaging processed by both Brainomix and Rapid software. Software processing was conducted in real time after image acquisition. The presence of large vessel occlusion (LVO) was determined from review of electronic medical records. CTP core (CBF<30%) and penumbra (Tmax>6s) volumes from Brainomix and Rapid were recorded for each case. Mismatch ratios were calculated (penumbra÷core volume); positive mismatch was defined according to DEFUSE‐3 criteria (core<70ml, penumbra>15ml, mismatch ratio > 1.8). The correlations and linear regressions between Brainomix and Rapid were computed for core, penumbra and mismatch ratios, and their concordance for mismatch positivity was assessed. Results 137 patients were scanned and received a final diagnosis of AIS. Of these, only 38 had imaging processed by both Brainomix and Rapid. Median age was 69 years (interquartile range: 57‐77.5y); 16 (42%) were female; 13 (34%) had LVO. Linear models between Brainomix and Rapid's outputs for CBF and Tmax volumes are shown in Figure 1a and 1b. The correlation between software outputs for CBF volume was r=0.70 (p<.001), and for Tmax volume was r=0.72 (p<.001). Mismatch ratios were calculated for the 13 cases with LVO (Figure 1c). The linear model between mismatch ratios was not significant, with four Rapid outputs showing infinite mismatch ratios (due to having CBF volumes of 0ml). Concordance between software for mismatch positivity was good; 8/13 cases showed mismatch on both softwares; 1 case showed mismatch on Brainomix only; 1 for Rapid only; and 3 cases showed mismatch on neither software. Conclusion This prospective study, despite limited sample size (N=38), showed good correlations between Brainomix 360 e‐CTP and Rapid CTP for calculation of both core and penumbra volumes. Although mismatch ratios showed less agreement, the categorization of mismatch positivity showed good concordance between software platforms. image

Background: Artificial intelligence (AI) stroke imaging software is becoming mainstay in many hub-and-spoke hospitals. Brainomix 360 Stroke software is the market leader in Europe focused on leveraging simple imaging (non-contrast computerized topography (NCCT) and CT angiography (CTA)) and has recently been FDA-cleared for use in the USA. We evaluated the implementation of Brainomix 360 AI Stroke software in the 17 spoke, multi-state Mayo Clinic Health System (MCHS) telestroke network. Methods: This prospective study compared decision and treatment times before and after Brainomix AI implementation, as well as clinician feedback and simulated decision making to better understand any changes seen. Patients were included if they underwent telestroke evaluation at an MCHS emergency department within 90 days of implementation (2/9/2024-8/8/2024), and excluded if they were already admitted to the hospital or if video evaluation was not performed. Data collection included demographics, clinical decisions and treatment times. Qualitative surveys were conducted at baseline and evaluation end, as well as simulated decision making in 20 de-identified cases randomized to with/without AI through an online portal. Results: A total of 907 patients were included (444 pre- and 463 post-implementation, 287 (32%) with ischemic stroke final diagnosis). Median NIHSS was 2. IVT was recommended in 20.3% (27/148 ischemic stroke patients) pre- and 25.9% (36/139) post-implementation. EVT was recommended in 16.2% (24/148) pre and 14.4% (20/139) post. AI use was associated with trends of faster telestroke activation to IVT decision (36 vs 32 mins, p=0.6), IVT administration (47 vs 40 minutes, p=0.6), and EVT decision (36 vs 33 minutes, p=0.5). In the simulation, imaging interpretation was significantly faster when randomized to AI use (3.4 vs 2.1 mins, p<0.001) and associated with improved physician confidence (62 vs 88%, p=0.004). Qualitative feedback from 14/24 neurologists showed the greatest perceived value was improved confidence of treatment decisions (43%) and faster triaging (36%). Most value was gained from e-ASPECTS (NCCT) and LVO modules. Conclusion: This service evaluation highlighted the opportunity to improve efficiency of decision making even in the setting of a well-developed telestroke system. The impact was underpinned by the improved speed and confidence of imaging interpretation reflected in an offline simulation and qualitative feedback.