Marasini Anurag, Shrestha Alisha, Phuyal Subash, Zaidat Osama O, Kalia Junaid Siddiq
AINeuroCare Academy, Dallas, TX, United States.
Travel and Mountain Medicine Center, Kathmandu, Nepal.
Front Neurol. 2022 Feb 23;13:784326. doi: 10.3389/fneur.2022.784326. eCollection 2022.
Intracranial aneurysms (IAs) are a significant public health concern. In populations without comorbidity and a mean age of 50 years, their prevalence is up to 3.2%. An efficient method for identifying subjects at high risk of an IA is warranted to provide adequate radiological screening guidelines and effectively allocate medical resources. Artificial intelligence (AI) has received worldwide attention for its impressive performance in image-based tasks. It can serve as an adjunct to physicians in clinical settings, improving diagnostic accuracy while reducing physicians' workload. AI can perform tasks such as pattern recognition, object identification, and problem resolution with human-like intelligence. Based on the data collected for training, AI can assist in decisions in a semi-autonomous manner. Similarly, AI can identify a likely diagnosis and also, select a suitable treatment based on health records or imaging data without any explicit programming (instruction set). Aneurysm rupture prediction is the holy grail of prediction modeling. AI can significantly improve rupture prediction, saving lives and limbs in the process. Nowadays, deep learning (DL) has shown significant potential in accurately detecting lesions on medical imaging and has reached, or perhaps surpassed, an expert-level of diagnosis. This is the first step to accurately diagnose UIAs with increased computational radiomicis. This will not only allow diagnosis but also suggest a treatment course. In the future, we will see an increasing role of AI in both the diagnosis and management of IAs.
颅内动脉瘤(IAs)是一个重大的公共卫生问题。在无合并症且平均年龄为50岁的人群中,其患病率高达3.2%。因此,需要一种有效的方法来识别颅内动脉瘤高风险患者,以提供适当的放射学筛查指南并有效分配医疗资源。人工智能(AI)因其在基于图像的任务中令人印象深刻的表现而受到全球关注。它可以在临床环境中作为医生的辅助工具,提高诊断准确性,同时减轻医生的工作量。人工智能可以执行模式识别、目标识别和问题解决等任务,具备类似人类的智能。基于收集用于训练的数据,人工智能可以以半自主的方式协助做出决策。同样,人工智能可以在没有任何明确编程(指令集)的情况下,根据健康记录或成像数据识别可能的诊断,并选择合适的治疗方法。动脉瘤破裂预测是预测建模的圣杯。人工智能可以显著改善破裂预测,在此过程中挽救生命和肢体。如今,深度学习(DL)在医学影像上准确检测病变方面已显示出巨大潜力,并且已经达到或可能超过了专家级诊断水平。这是通过增加计算放射组学来准确诊断未破裂颅内动脉瘤的第一步。这不仅可以实现诊断,还可以建议治疗方案。未来,我们将看到人工智能在颅内动脉瘤的诊断和管理中发挥越来越重要的作用。