Background:　Aortic　dissection　is　a　life-threatening　clinical　emergency,　but　it　is　often　missed　and　misdiagnosed　due　to　the　limitations　of　diagnostic　technology.　In　this　study,　we　developed　a　deep　learning-based　algorithm　for　identifying　the　true　and　false　lumens　in　the　aorta　on　non-contrast-enhanced　computed　tomography　(NCE-CT)　scans　and　to　ascertain　the　presence　of　aortic　dissection.　Additionally,　we　compared　the　diagnostic　performance　of　this　algorithm　with　that　of　radiologists　in　detecting　aortic　dissection.　Methods:　We　included　320　patients　with　suspected　acute　aortic　syndrome　from　three　centers　(Beijing　Anzhen　Hospital　Affiliated　to　Capital　Medical　University,　Fujian　Provincial　Hospital,　and　Xiangya　Hospital　of　Central　South　University)　between　May　2020　and　May　2022　in　this　retrospective　study.　All　patients　underwent　simultaneous　NCE-CT　and　contrast-enhanced　CT　(CE-CT).　The　cohort　comprised　160　patients　with　aortic　dissection　and　160　without　aortic　dissection.　A　deep　learning　algorithm,　three-dimensional　(3D)　full-resolution　U-Net,　was　continuously　trained　and　refined　to　segment　the　true　and　false　lumens　of　the　aorta　to　determine　the　presence　of　aortic　dissection.　The　algorithm’s　efficacy　in　detecting　dissections　was　evaluated　using　the　receiver　operating　characteristic　(ROC)　curve,　including　the　area　under　the　curve　(AUC),　sensitivity,　and　specificity.　Furthermore,　a　comparative　analysis　of　the　diagnostic　capabilities　between　our　algorithm　and　three　radiologists　was　conducted.　Results:　In　diagnosing　aortic　dissection　using　NCE-CT　images,　the　developed　algorithm　demonstrated　an　accuracy　of　93.8%　[95%　confidence　interval　(CI):　89.8–98.3%],　a　sensitivity　of　91.6%　(95%　CI:　86.7–95.8%),　and　a　specificity　of　95.6%　(95%　CI:　91.2–99.3%).　In　contrast,　the　radiologists　achieved　an　accuracy　of　88.8%　(95%　CI:　83.5–94.1%),　a　sensitivity　of　90.6%　(95%　CI:　83.5–94.1%),　and　a　specificity　of　94.1%　(95%　CI:　72.9–97.6%).　There　was　no　significant　difference　between　the　algorithm’s　performance　and　radiologists’　mean　performance　in　accuracy,　sensitivity,　or　specificity　(P＞0.05)　.　Conclusions:　The　algorithm　proficiently　segments　the　true　and　false　lumens　in　aortic　NCE-CT　images,　exhibiting　diagnostic　capabilities　comparable　to　those　of　radiologists　in　detecting　aortic　dissection.　This　suggests　that　the　algorithm　could　reduce　misdiagnoses　in　clinical　practice,　thereby　enhancing　patient　care.　©　AME　Publishing　Company.