Research and application of intelligent image processing technology in the auxiliary diagnosis of aortic coarctation - Yan, Taocui; Qin, Jinjie; Zhang, Yulin; Li, Qiuni; Han, Baoru; Jin, Xin (Frontiers in Pediatrics, 2023)
Objective: To explore the application of the proposed intelligent image processing method in the diagnosis of aortic coarctation computed tomography angiography (CTA) and to clarify its value in the diagnosis of aortic coarctation based on the diagnosis results. Methods: Fifty-three children with coarctation of the aorta (CoA) and forty children without CoA were selected to constitute the study population. CTA was performed on all subjects. The minimum diameters of the ascending aorta, proximal arch, distal arch, isthmus, and descending aorta were measured using manual and intelligent methods, respectively. The Wilcoxon signed-rank test was used to analyze the differences between the two measurements. The surgical diagnosis results were used as the gold standard, and the diagnostic results obtained by the two measurement methods were compared with the gold standard to quantitatively evaluate the diagnostic results of CoA by the two measurement methods. The Kappa test was used to analyze the consistency of intelligence diagnosis results with the gold standard. Results: Whether people have CoA or not, there was a significant difference (p<0.05) in the measurements of the minimum diameter at most sites using the two methods. However, close final diagnoses were made using the intelligent method and the manual. Meanwhile, the intelligent measurement method obtained higher accuracy, specificity, and AUC (area under the curve) compared to manual measurement in diagnosing CoA based on Karl's classification (accuracy=0.95, specificity=0.9, and AUC=0.94). Furthermore, the diagnostic results of the intelligence method applied to the three criteria agreed well with the gold standard (all kappa≥0.8). The results of the comparative analysis showed that Karl's classification had the best diagnostic effect on CoA. Conclusion: The proposed intelligent method based on image processing can be successfully applied to assist in the diagnosis of CoA.