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Medscape
3 days ago
- Health
- Medscape
AI Tool Linked to Fewer Recurrent Events After Stroke
HELSINKI, Finland — An artificial intelligence (AI)–based clinical decision support system helped doctors make decisions in the treatment of acute ischemic stroke and was associated with a significant reduction in recurrent vascular events in a new, randomized trial. The AI system integrated clinician input with data from the hospital records and imaging of more than 20,000 patients in China to help guide patient management around stroke etiology and secondary prevention. The trial represents a strong case for the future role of AI in stroke care, said Study Chair, Li Zhang, MD, Beijing Tiantan Hospital, Capital Medical University, Beijing, China. 'By harnessing AI to deliver rapid, evidence-based guidance, we've shown that decision support can move beyond theory into practice — improving both how we treat acute strokes and the lives of our patients.' However, experts not involved in the study noted that the research is based on Chinese stroke guidelines — which differ from guidelines in the United States and United Kingdom — and would need to be replicated in the West before use. Zhang presented the results of the GOLDEN BRIDGE II trial on May 22 at the European Stroke Organization Conference (ESOC) 2025. A Test of AI in Stroke Management Rapid and accurate decision-making is critical in acute stroke management, and AI tools offering automated imaging analysis, stroke subtype classification, and guideline-based treatment recommendations, hold promise for standardizing care and improving outcomes, Zhang noted. The GOLDEN BRIDGE II trial was designed to test this hypothesis in a real-world, multicenter setting. The trial, which had a cluster randomized design, was conducted from January 2021 to June 2023 in 77 hospitals in China. The hospitals were randomized to use the AI support system or to continue usual care. The AI system provided automated MRI lesion detection and lesion characteristics analysis, algorithmic classification of stroke etiology and pathogenesis, with real-time, guideline-based recommendations for secondary prevention. The trial included 21,603 patients with acute ischemic stroke (median age, 67 years; 35% women), with 96% completing a 12-month follow-up. The primary endpoint was the occurrence of new vascular events (composite of ischemic stroke, hemorrhagic stroke, myocardial infarction, or vascular death) at 3 months after stroke onset. A 'Strong Case' Results showed that patients for whom the AI model was used had significantly fewer recurrent vascular events at all three time points evaluated. Rates of recurrent vascular events in the intervention group vs the control group were 2.9% vs 3.9% (adjusted hazard ratio [aHR], 0.71; P < .001) at 3 months; 3.4% vs 4.8% (aHR, 0.70; P < .001) at 6 months; and 4.0% vs 5.5% (aHR, 0.70; P < .001) at 12 months. The use of the AI tool was also associated with a lower all-cause mortality at 6 months (2.0% vs 2.3%; aHR, 0.78; P = .007) and at 12 months (3.0% vs 3.5%; aHR 0.77; P < .001). In addition, patients at the hospitals using the AI tool were more likely to achieve a higher composite acute ischemic stroke quality score (91.4 % vs 89.7 %; odds ratio, 1.26; P < .001). A potential limitation of the study was the cluster randomized design, which could allow variations in care between hospitals to influence the results. But Zhang concluded that the trial represents a strong case for the future role of AI in stroke care, saying that the findings 'support wider adoption of AI-driven decision support to optimize acute stroke care and patient outcomes globally.' Not Ready for Routine Clinical Use Commenting on the findings during a discussion on the GOLDEN BRIDGE II trial, Carlos Molina, MD, head of Neurology at Vall d'Hebron Hospital, Barcelona, Spain, noted several study strengths, including the large study cohort and the clinical and technological validation of the sophisticated AI system used in the trial. 'This is an important step,' he added, 'but there are always concerns when we think about trying to integrate AI into clinical practice.' Georgios Tsivgoulis, MD, professor of neurology at the National and Kapodistrian University of Athens, Athens, Greece, noted that although there was a substantial reduction in vascular events and recurrent strokes in the intervention group, the mechanisms that led to this reduction were not clear. 'How did the AI improve clinical practice? Was it through imaging, was it because of better adherence to the Chinese stroke guidelines or was it related to better stroke subtype classification and perhaps treatment individualization? I would like to see more information about the step between the AI implementation and the event reduction results,' he commented. Karin Klijn, professor and chair of neurology at Radboud University Medical Center, Nijmegen, the Netherlands, said the improvement in recurrent events appeared to be driven by the imaging. 'But we didn't hear any details, and we need to see more details so we can see where improvements can be made.' 'However, this was an impressive study in that it involved more than 70 hospitals and more than 20,000 patients. I think AI will help us to achieve the same level of confidence for all the images we have to examine and decisions we have made. I see great potential for this technology,' Klijn added. The trial would also need to be replicated in the Western world for the AI tool to be used in Europe or North America, Tsivgoulis said. 'The GOLDEN BRIDGE II trial is based on the Chinese stroke guidelines which are substantially different from European and US stroke guidelines,' he pointed out. 'It would need to be replicated in Europe and North America using standardized European Stroke Organization and American Stroke Association guidelines to see if this huge beneficial effect can be replicated in Western patients.' Molina agreed, adding that 'the data sources used to train this model are critical to avoid bias that AI can amplify.' In the future, more complex large language models will be available that involve real time interaction with the user and include reinforcing learning, Molina added. 'This prototype is evolving and interaction with the user is critical. There is a lot to learn about that. But this is the first step,' he said. Also commenting, Alastair Webb, MD, clinical reader in stroke medicine at Imperial College London, London, England, also had questions on how it could be implemented in practice. 'This study was designed a few years ago and has taken years to produce these results. How do we keep the algorithm updated and incorporate the continuous changes in clinical practice that occur? We can't do a trial for every update. So we are going to have to integrate this technology into our systems with some sort of real time method of assessing whether every new evolution doesn't cause harm, doesn't make the wrong decision in a large language model.'
The Australian
21-05-2025
- Health
- The Australian
EMVision advances its AI-driven stroke diagnostics
EMVision Medical Devices advances its AI-driven stroke diagnostics Promising new data shows enhanced performance of 'ischemia or not' algorithm Data being presented at 11th European Stroke Organisation Conference Special Report: EMVision Medical Devices is advancing its AI-driven stroke diagnostics with promising new data showing enhanced performance of its 'ischemia or not' algorithm. EMVision Medical Devices (ASX:EMV) said the promising updated data is being presented at the 11th European Stroke Organisation Conference (ESOC 2025) in Helsinki, Finland, from 21-23 May. The neurodiagnostics medical devices company previously reported that the AI-based 'ischemia or not' diagnostic algorithm achieved a sensitivity of 85% and specificity of 78% in the EMView pre-validation trial. Sensitivity is the ability of the device to diagnose a disease, while specificity is the ability to rule out false positives. The study was conducted at Liverpool Hospital in Sydney, The Royal Melbourne Hospital, and Princess Alexandra Hospital in Brisbane. The algorithm was initially trained on more than 240 patient cases enrolled in the study. Following training, it was tested on an unseen dataset that had been isolated and not used during the training phase. EMVision said as part of ongoing innovation, the AI-powered 'ischemia or not' diagnostic algorithm has since been re-trained using cleaned training data and re-evaluated. 'Ischemia or not' refers to distinguishing between blockages – the more common ischemic strokes – and bleeds (haemorrhagic strokes). In the updated dataset, the algorithm's performance has improved as follows: Source: EMVision EMVision model shows promising detection In a limited sensitivity analysis of 20 ischemic test cases, EMVision said the emu RF-based model missed only one case, compared to nine missed using first-line Non-Contrast Computed Tomography (NCCT). EMVision said notably, three of the cases were also not identified using Computed Tomography Perfusion (CTP) or Computed Tomography Angiography (CTA). They were only confirmed on follow-up MRI diffusion-weighted imaging (MR DWI) conducted 48 hours later. Source: EMVision Pivotal trial to validate algorithm performance EMVision said whilst encouraging, due to the design of the study and limited sample size, the data does not yet allow statistically significant or generalisability conclusions to be drawn on the performance of the updated 'ischemia or not' AI-powered model. Generalisability is the degree to which the results of a study can be applied to other situations. The company said the recently started pivotal trial of its emu bedside brain scanner to diagnose stroke to support US Food and Drug Administration (FDA) de novo (new device) clearance was also designed to validate algorithm performance. EMVision said it was implementing a cost-effective strategy for continued device innovation and enhancement during the pivotal trial. Additional patients will be scanned at multiple sites in Australia outside of the pivotal trial, including the Princess Alexandra Hospital and John Hunter Hospital in Newcastle. The company said the study data would be used to progress the development of additional device features, scale the training library for EMVision's diagnostic AI algorithms – including ongoing 'ischemia or not' detection and classification development. The data – which is separate to, and isolated from, the pivotal trial dataset – would also be used to potentially extend indications by the enrolment of patients with traumatic brain injury. EMVision observed meaningful performance increases in the sensitivity/specificity of its diagnostic AI algorithms during the previous EMView pre-validation study when additional training data was used. Source: EMVision Importance of knowing 'ischemia or not' EMVision's 'ischemia or not' AI-driven algorithm could play an important role in the future of prompt stroke detection and treatment. Prompt and accurate stroke detection pre-hospital or in remote areas can accelerate treatment by ensuring that patients are directed to the most appropriate centre (e.g., stroke centre vs standard hospital) and the ED teams are prepared for their arrival, thus avoiding delays from unnecessary transfers or care coordination. In a comprehensive stroke centre, confidence in an ischaemic stroke diagnosis could enable direct transfer to an angiogram suite for intervention initiation, thus cutting down door-to-surgery times. About half of suspected stroke cases aren't stroke at all, and are instead 'stroke mimics' which often don't require the same urgent care. Identification of true stroke cases ensures that hospital resources are put to best use by avoiding unnecessary imaging expenses, or by allowing regional hospitals to treat less critical patients rather than undertaking expensive transfers where they may not be necessary. Furthermore, when it comes to treating stroke speed is crucial, treatment in the first 60 minutes after a stroke – known as 'the golden hour' – can heavily influence the likelihood of positive outcomes. This article does not constitute financial product advice. You should consider obtaining independent advice before making any financial decisions. Sponsored LTR Pharma appoints Associate Professor Darren Katz to its scientific advisory board to consult for Spontan. Sponsored The appointment of industry trailblazer Ashok Parekh underscores the company's commitment to leveraging deep expertise as it advances its gold projects.
