Diagnostic Accuracy of AI for Opportunistic Screening of Abdominal Aortic Aneurysm in CT: A Systematic Review and Narrative Synthesis

Abstract

In this review, we focused on the applicability of artificial intelligence (AI) for opportunistic abdominal aortic aneurysm (AAA) detection in computed tomography (CT). We used the academic search system PubMed as the primary source for the literature search and Google Scholar as a supplementary source of evidence. We searched through 2 February 2022. All studies on automated AAA detection or segmentation in noncontrast abdominal CT were included. For bias assessment, we developed and used an adapted version of the QUADAS-2 checklist. We included eight studies with 355 cases, of which 273 (77%) contained AAA. The highest risk of bias and level of applicability concerns were observed for the “patient selection” domain, due to the 100% pathology rate in the majority (75%) of the studies. The mean sensitivity value was 95% (95% CI 100–87%), the mean specificity value was 96.6% (95% CI 100–75.7%), and the mean accuracy value was 95.2% (95% CI 100–54.5%). Half of the included studies performed diagnostic accuracy estimation, with only one study having data on all diagnostic accuracy metrics. Therefore, we conducted a narrative synthesis. Our findings indicate high study heterogeneity, requiring further research with balanced noncontrast CT datasets and adherence to reporting standards in order to validate the high sensitivity value obtained.

Overview of modern digital diagnostic image markup tools

“…В настоящее время искусственный интеллект активно применяют для решения различных задач, таких как целевая диагностика (например, сегментация и 3D-визуализация сосудов при операционном планировании [1]), оппортунистический скрининг разных видов патологии [2], оценка динамики и прогнозирование течения заболевания [3] и др. Качество работы алгоритма искусственного интеллекта и его диагностическая точность во многом за-висят от размера и содержания наборов данных для обучения и тестирования [4].…”

Possibilities of densitometry in the assessment of diffuse changes in the lung parenchyma

“…Нейронные сети (рис. 3) определяют ключевые параметры самостоятельно в процессе обучения, поэтому качество работы сети напрямую зависит от объема и репрезентативности обучающей выборки [61]. Преимуществом нейронных сетей является использование неявных признаков, которые трудно формализовать (например, паттерны при COVID-19 или вид гистограммы плотности), недостатком -высокие требования к качеству и сбалансированности обучающей выборки, а также существенные трудности в определении причин некорректной работы алгоритма [62].…”

Optimising anesthesia support during operations on the abdominal aorta and its branches

“…The arterial branches that are responsible for delivering oxygen-rich blood to all of the body's tissues are created along the route by the aorta. All of the body's arteries emerge from the aorta, which is the primary source of oxygenated blood before they proceed distally to their target tissues [16,17]. Major arterial branches from the aorta include the left and right coronary arteries, left common carotid, brachiocephalic, celiac trunk, left subclavian, left and right renal, superior mesenteric, inferior mesenteric, gonadal, right and left common iliac arteries, and median sacral [18] The inferior and superior mesenteric arteries supply the hindgut and midgut, respectively, while the celiac trunk mostly nourishes the foregut.…”