Research and application of intelligent image processing technology in the auxiliary diagnosis of aortic coarctation
ObjectiveTo 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.MethodsFifty-three children with coarcta...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-02-01
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| Series: | Frontiers in Pediatrics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fped.2023.1131273/full |
| _version_ | 1797896024053776384 |
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| author | Taocui Yan Jinjie Qin Yulin Zhang Qiuni Li Baoru Han Xin Jin |
| author_facet | Taocui Yan Jinjie Qin Yulin Zhang Qiuni Li Baoru Han Xin Jin |
| author_sort | Taocui Yan |
| collection | DOAJ |
| description | ObjectiveTo 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.MethodsFifty-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.ResultsWhether 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.ConclusionThe proposed intelligent method based on image processing can be successfully applied to assist in the diagnosis of CoA. |
| first_indexed | 2024-04-10T07:35:06Z |
| format | Article |
| id | doaj.art-0abad1471f8b486cac109f76a4bde681 |
| institution | Directory Open Access Journal |
| issn | 2296-2360 |
| language | English |
| last_indexed | 2024-04-10T07:35:06Z |
| publishDate | 2023-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Pediatrics |
| spelling | doaj.art-0abad1471f8b486cac109f76a4bde6812023-02-23T12:42:27ZengFrontiers Media S.A.Frontiers in Pediatrics2296-23602023-02-011110.3389/fped.2023.11312731131273Research and application of intelligent image processing technology in the auxiliary diagnosis of aortic coarctationTaocui Yan0Jinjie Qin1Yulin Zhang2Qiuni Li3Baoru Han4Xin Jin5Medical Data Science Academy, College of Medical Informatics, Chongqing Medical University, Chongqing, ChinaDepartment of Radiology, Children's Hospital of Chongqing Medical University, Chongqing, ChinaTechnology Research and Development Department of Chongqing Intech Technology Co., LTD, Chongqing,, ChinaMedical Data Science Academy, College of Medical Informatics, Chongqing Medical University, Chongqing, ChinaMedical Data Science Academy, College of Medical Informatics, Chongqing Medical University, Chongqing, ChinaDepartment of CardiothoracicSurgery, Ministry of Education Key Laboratory of Child Development and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, National Clinical Research Center for Child Health and Disorders, Chongqing Key Laboratory of Pediatrics, Children's Hospital of Chongqing Medical University, Chongqing, ChinaObjectiveTo 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.MethodsFifty-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.ResultsWhether 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.ConclusionThe proposed intelligent method based on image processing can be successfully applied to assist in the diagnosis of CoA.https://www.frontiersin.org/articles/10.3389/fped.2023.1131273/fullcoarctation of the aorta (COA)computed tomography angiography (CTA)intelligent image processingintelligent measurementauxiliary diagnosis |
| spellingShingle | Taocui Yan Jinjie Qin Yulin Zhang Qiuni Li Baoru Han Xin Jin Research and application of intelligent image processing technology in the auxiliary diagnosis of aortic coarctation Frontiers in Pediatrics coarctation of the aorta (COA) computed tomography angiography (CTA) intelligent image processing intelligent measurement auxiliary diagnosis |
| title | Research and application of intelligent image processing technology in the auxiliary diagnosis of aortic coarctation |
| title_full | Research and application of intelligent image processing technology in the auxiliary diagnosis of aortic coarctation |
| title_fullStr | Research and application of intelligent image processing technology in the auxiliary diagnosis of aortic coarctation |
| title_full_unstemmed | Research and application of intelligent image processing technology in the auxiliary diagnosis of aortic coarctation |
| title_short | Research and application of intelligent image processing technology in the auxiliary diagnosis of aortic coarctation |
| title_sort | research and application of intelligent image processing technology in the auxiliary diagnosis of aortic coarctation |
| topic | coarctation of the aorta (COA) computed tomography angiography (CTA) intelligent image processing intelligent measurement auxiliary diagnosis |
| url | https://www.frontiersin.org/articles/10.3389/fped.2023.1131273/full |
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