Performance of Deep Learning-Based Algorithm for Detection of Pediatric Intussusception on Abdominal Ultrasound Images
Background and Aims. Diagnosing pediatric intussusception from ultrasound images can be a difficult task in many primary care hospitals that lack experienced radiologists. To address this challenge, this study developed an artificial intelligence- (AI-) based system for automatic detection of “conce...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Gastroenterology Research and Practice |
| Online Access: | http://dx.doi.org/10.1155/2022/9285238 |
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| author | Zheming Li Chunze Song Jian Huang Jing Li Shoujiang Huang Baoxin Qian Xing Chen Shasha Hu Ting Shu Gang Yu |
| author_facet | Zheming Li Chunze Song Jian Huang Jing Li Shoujiang Huang Baoxin Qian Xing Chen Shasha Hu Ting Shu Gang Yu |
| author_sort | Zheming Li |
| collection | DOAJ |
| description | Background and Aims. Diagnosing pediatric intussusception from ultrasound images can be a difficult task in many primary care hospitals that lack experienced radiologists. To address this challenge, this study developed an artificial intelligence- (AI-) based system for automatic detection of “concentric circles” signs on ultrasound images, thereby improving the efficiency and accuracy of pediatric intussusception diagnosis. Methods. A total of 440 cases (373 pediatric intussusception and 67 normal cases) were retrospectively collected from Children’s Hospital affiliated to Zhejiang University School of Medicine from January 2020 to December 2020. An improved Faster RCNN deep learning framework was used to detect “concentric circle” signs. Finally, independent validation set was used to evaluate the performance of the developed AI tool. Results. The data of pediatric intussusception were divided into a training set and validation set according to the ratio of 8 : 2, with training set (298 pediatric intussusception) and validation set (75 pediatric intussusception and 67 normal cases). In the “concentric circle” detection model, the detection rate, recall, specificity, and F1 score assessed by the validation set were 92.8%, 95.0%, 92.2%, and 86.4%, respectively. Pediatric intussusception was classified by “concentric circle” signs, and the accuracy, recall, specificity, and F1 score were 93.0%, 92.0%, 94.1%, and 93.2% on the validation set, respectively. Conclusion. The model established in this paper can realize the automatic detection of “concentric circle” signs in the ultrasound images of abdominal intussusception in children; the AI tool can improve the diagnosis speed of pediatric intussusception. It is necessary to further develop an artificial intelligence system for real-time detection of “concentric circles” in ultrasound images for the judgment of children with intussusception. |
| format | Article |
| id | doaj-art-1a887d72064e4b3a9c3a1e8fb6468eaf |
| institution | OA Journals |
| issn | 1687-630X |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Gastroenterology Research and Practice |
| spelling | doaj-art-1a887d72064e4b3a9c3a1e8fb6468eaf2025-08-20T02:01:57ZengWileyGastroenterology Research and Practice1687-630X2022-01-01202210.1155/2022/9285238Performance of Deep Learning-Based Algorithm for Detection of Pediatric Intussusception on Abdominal Ultrasound ImagesZheming Li0Chunze Song1Jian Huang2Jing Li3Shoujiang Huang4Baoxin Qian5Xing Chen6Shasha Hu7Ting Shu8Gang Yu9Department of Data and InformationNational Clinical Research Center for Child HealthDepartment of Data and InformationDepartment of Data and InformationNational Clinical Research Center for Child HealthHuiying Medical Technology (Beijing)Hangzhou Normal UniversityThe Children’s Hospital Zhejiang University School of MedicineNational Institute of Hospital AdministrationDepartment of Data and InformationBackground and Aims. Diagnosing pediatric intussusception from ultrasound images can be a difficult task in many primary care hospitals that lack experienced radiologists. To address this challenge, this study developed an artificial intelligence- (AI-) based system for automatic detection of “concentric circles” signs on ultrasound images, thereby improving the efficiency and accuracy of pediatric intussusception diagnosis. Methods. A total of 440 cases (373 pediatric intussusception and 67 normal cases) were retrospectively collected from Children’s Hospital affiliated to Zhejiang University School of Medicine from January 2020 to December 2020. An improved Faster RCNN deep learning framework was used to detect “concentric circle” signs. Finally, independent validation set was used to evaluate the performance of the developed AI tool. Results. The data of pediatric intussusception were divided into a training set and validation set according to the ratio of 8 : 2, with training set (298 pediatric intussusception) and validation set (75 pediatric intussusception and 67 normal cases). In the “concentric circle” detection model, the detection rate, recall, specificity, and F1 score assessed by the validation set were 92.8%, 95.0%, 92.2%, and 86.4%, respectively. Pediatric intussusception was classified by “concentric circle” signs, and the accuracy, recall, specificity, and F1 score were 93.0%, 92.0%, 94.1%, and 93.2% on the validation set, respectively. Conclusion. The model established in this paper can realize the automatic detection of “concentric circle” signs in the ultrasound images of abdominal intussusception in children; the AI tool can improve the diagnosis speed of pediatric intussusception. It is necessary to further develop an artificial intelligence system for real-time detection of “concentric circles” in ultrasound images for the judgment of children with intussusception.http://dx.doi.org/10.1155/2022/9285238 |
| spellingShingle | Zheming Li Chunze Song Jian Huang Jing Li Shoujiang Huang Baoxin Qian Xing Chen Shasha Hu Ting Shu Gang Yu Performance of Deep Learning-Based Algorithm for Detection of Pediatric Intussusception on Abdominal Ultrasound Images Gastroenterology Research and Practice |
| title | Performance of Deep Learning-Based Algorithm for Detection of Pediatric Intussusception on Abdominal Ultrasound Images |
| title_full | Performance of Deep Learning-Based Algorithm for Detection of Pediatric Intussusception on Abdominal Ultrasound Images |
| title_fullStr | Performance of Deep Learning-Based Algorithm for Detection of Pediatric Intussusception on Abdominal Ultrasound Images |
| title_full_unstemmed | Performance of Deep Learning-Based Algorithm for Detection of Pediatric Intussusception on Abdominal Ultrasound Images |
| title_short | Performance of Deep Learning-Based Algorithm for Detection of Pediatric Intussusception on Abdominal Ultrasound Images |
| title_sort | performance of deep learning based algorithm for detection of pediatric intussusception on abdominal ultrasound images |
| url | http://dx.doi.org/10.1155/2022/9285238 |
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