Research and application of intelligent image processing technology in the auxiliary diagnosis of aortic coarctation
Taocui Yan1, Jinjie Qin2, Yulin Zhang3
1Medical Data Science Academy, College of Medical Informatics, Chongqing Medical University, Chongqing, China.
Insights
An intelligent image processing method effectively aids in diagnosing coarctation of the aorta (CoA). This automated approach demonstrates superior accuracy and specificity compared to manual measurements, improving diagnostic outcomes.
Area of Science:
- Medical Imaging
- Cardiovascular Imaging
- Artificial Intelligence in Medicine
Background:
- Coarctation of the aorta (CoA) is a congenital heart defect requiring accurate diagnosis.
- Computed tomography angiography (CTA) is a key imaging modality for CoA assessment.
- Manual measurement of aortic dimensions can be time-consuming and subject to variability.
Purpose of the Study:
- To evaluate an intelligent image processing method for diagnosing CoA using CTA.
- To compare the diagnostic performance of the intelligent method against manual measurements.
- To determine the clinical value of the intelligent method in CoA diagnosis.
Main Methods:
- A study population included 53 children with CoA and 40 controls.
- CTA scans were analyzed using both manual and intelligent measurement techniques.
- Diagnostic accuracy was compared against surgical results (gold standard) using statistical tests like Kappa.
Main Results:
- The intelligent method showed significant differences in diameter measurements compared to manual methods (p < 0.05).
- The intelligent method achieved higher accuracy (0.95), specificity (0.9), and AUC (0.94) in diagnosing CoA using Karl's classification.
- Diagnostic results from the intelligent method showed excellent agreement with the gold standard (Kappa ≥ 0.8).
Conclusions:
- The proposed intelligent image processing method is effective for assisting in CoA diagnosis.
- This automated approach offers improved accuracy and reliability over manual measurements.
- Karl's classification demonstrated the best diagnostic performance for CoA.
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.
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