Atherosclerosis Imaging Quantitative Computed Tomography (AI-QCT) to guide referral to invasive coronary angiography in the randomized controlled CONSERVE trial.
Yumin KimAndrew D ChoiAnha TelluriIsabella LipkinAndrew J BradleyAlfateh SidahmedRebecca JonasDaniele AndreiniRavi BathinaAndrea BaggianoRodrigo CerciEui-Young ChoiJung-Hyun ChoiSo-Yeon ChoiNamsik ChungJason ColeJoon-Hyung DohSang-Jin HaAe-Young HerCezary KepkaJang-Young KimJin Won KimSang-Wook KimWoong KimGianluca PontoneTodd C VillinesIksung ChoIbrahim DanadRan HeoSang-Eun LeeJi Hyun LeeHyung-Bok ParkJi-Min SungTami CrabtreeJames P EarlsJames K MinSeo Young SongPublished in: Clinical cardiology (2023)
In stable patients referred for ACC/AHA guideline-indicated nonemergent ICA, application of artificial intelligence and machine learning for AI-QCT can significantly reduce ICA rates and costs with no change in 1-year MACE.
Keyphrases
- artificial intelligence
- machine learning
- big data
- deep learning
- computed tomography
- end stage renal disease
- phase iii
- high resolution
- ejection fraction
- chronic kidney disease
- newly diagnosed
- open label
- phase ii
- clinical trial
- prognostic factors
- cardiovascular disease
- magnetic resonance imaging
- positron emission tomography
- double blind
- primary care
- randomized controlled trial
- placebo controlled
- type diabetes
- patient reported outcomes
- contrast enhanced
- dual energy