Diagnostic accuracy and performance of artificial intelligence in measuring left atrial volumes and function on multiphasic CT in patients with atrial fibrillation

Aquino G. J., Chamberlin J., Yacoub B., Kocher M. R., Kabakus I., Akkaya S., ...More

EUROPEAN RADIOLOGY, vol.32, no.8, pp.5256-5264, 2022 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 32 Issue: 8
  • Publication Date: 2022
  • Doi Number: 10.1007/s00330-022-08657-y
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Agricultural & Environmental Science Database, Biotechnology Research Abstracts, CINAHL, EMBASE, MEDLINE
  • Page Numbers: pp.5256-5264
  • Keywords: Heart atria, Atrial fibrillation, Computed tomography, Artificial intelligence, Atrial function, MULTIDETECTOR COMPUTED-TOMOGRAPHY, QUANTIFICATION, ASSOCIATION, ENLARGEMENT, SOFTWARE, ABLATION, VALUES, SIZE, MDCT
  • Karadeniz Technical University Affiliated: Yes


Objectives To evaluate the effectiveness of a novel artificial intelligence (AI) algorithm for fully automated measurement of left atrial (LA) volumes and function using cardiac CT in patients with atrial fibrillation. Methods We included 79 patients (mean age 63 +/- 12 years; 35 with atrial fibrillation (AF) and 44 controls) between 2017 and 2020 in this retrospective study. Images were analyzed by a trained AI algorithm and an expert radiologist. Left atrial volumes were obtained at cardiac end-systole, end-diastole, and pre-atrial contraction, which were then used to obtain LA function indices. Intraclass correlation coefficient (ICC) analysis of the LA volumes and function parameters was performed and receiver operating characteristic (ROC) curve analysis was used to compare the ability to detect AF patients. Results The AI was significantly faster than manual measurement of LA volumes (4 s vs 10.8 min, respectively). Agreement between the manual and automated methods was good to excellent overall, and there was stronger agreement in AF patients (all ICCs >= 0.877; p < 0.001) than controls (all ICCs >= 0.799; p < 0.001). The AI comparably estimated LA volumes in AF patients (all within 1.3 mL of the manual measurement), but overestimated volumes by clinically negligible amounts in controls (all by <= 4.2 mL). The AI's ability to distinguish AF patients from controls using the LA volume index was similar to the expert's (AUC 0.81 vs 0.82, respectively; p = 0.62). Conclusion The novel AI algorithm efficiently performed fully automated multiphasic CT-based quantification of left atrial volume and function with similar accuracy as compared to manual quantification. Summary statement Novel CT-based AI algorithm efficiently quantifies left atrial volumes and function with similar accuracy as manual quantification in controls and atrial fibrillation patients.