Correspondence on: ‘Artificial intelligence aneurysm measurement tool finds growth in all aneurysms that ruptured during conservative management’ by Sahlein et al

We read with great interest the recent publication by Sahlein et al, describing thoughtprovoking data on a small number of aneurysms undergoing routine imaging surveillance that went on to rupture. These aneurysms had been followed with noninvasive neurovascular imaging and were believed to have remained stable in size and morphology on review by an experienced multidisciplinary group at a high volume aneurysm center. The patient images were retrospectively analyzed by Rapid Aneurysm (RapidAI, Menlo Park, CA), a semiautomated segmentation software, rendering discernable and quantifiable volume increases that had escaped the human eye. These findings beg the clinically relevant question: Can routine artificial intelligence (AI) interpretation of aneurysm surveillance imaging better guide medical decisionmaking to prevent ruptures and their significant sequelae? Conversely, can AI also avoid false positive interpretations of aneurysm growth that lead to unnecessary anxiety or treatment? While those of us who evaluate and treat aneurysms strive to do our best for our patients, our clinical decisionmaking suffers in knowing that our decisions may be based on imprecise volumetric data. Natural history data are flawed with methodological limitations, including selection bias favoring a more benign natural history, crude measurements, and no adjustment for developing technology over time. Even presentday measurements are subject to variability in human measurement and are imperfect despite drastic improvements in threedimensional imaging technology. Small aneurysms can be manipulated to measure whether the aneurysm is larger or smaller by simply changing the angle in which it is viewed. Conventional imaging modalities may not register minor morphological changes such as the appearance of blebs. Aneurysm measurements are a complex quantification which should include morphology, but are often only summarized by a maximum diameter. Maximal diameter excludes small sidewall blisters that may form during surveillance, assuming they can be identified. These pilot data resonate for those of us who have been haunted with the knowledge that current technology does not provide accurate and reliable measurements of intracranial aneurysms, as we believe that aneurysm morphology is not spherical (and change in morphology over time) and is a crucial consideration in their management. How have we historically dealt with this quandary, basing massively impactful decisions on such imperfect data? Without a means to resolve this conflict, we bury this uncomfortable truth and instead rely on experience and over time develop an approach based on a gestalt of a myriad of factors, including age, smoking history, family history, aneurysm location, size and morphology, and any changes in the aneurysm over time, among other factors. This paper, which likely raises more questions than answers, provides promise for an analytical approach to aneurysm management. AI technology can arm us with a means to better study aneurysm behavior to give the provider and patient more confidence that our clinicaldecision making is based on more accurate surveillance data. We look forward to further enlightenment with the application of this technology in future clinical practice.