Dataset Information

Performance of deep learning-based algorithm for detection of ileocolic intussusception on abdominal radiographs of young children.

ABSTRACT: The purpose of this study was to develop and test the performance of a deep learning-based algorithm to detect ileocolic intussusception using abdominal radiographs of young children. For the training set, children (?5 years old) who underwent abdominal radiograph and ultrasonography (US) for suspicion of intussusception from March 2005 to December 2017 were retrospectively included and divided into control and intussusception groups according to the US results. A YOLOv3-based algorithm was developed to recognize the rectangular area of the right abdomen and to diagnose intussusception. For the validation set, children (?5 years old) who underwent both radiograph and US from January to August 2018 with the suspicion of intussusception were included. Diagnostic performances of an algorithm and radiologists were compared. Total 681 children including 242 children in intussusception group were included in the training set and 75 children including 25 children in intussusception group were included in the validation set. The sensitivity of the algorithm was higher compared with that of the radiologists (0.76 vs. 0.46, p?=?0.013), while specificity was not different between the algorithm and the radiologists (0.96 vs. 0.92, p?=?0.32). Deep learning-based algorithm can aid screening of intussusception using abdominal radiography in young children.

SUBMITTER: Kim S

PROVIDER: S-EPMC6923478 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Performance of deep learning-based algorithm for detection of ileocolic intussusception on abdominal radiographs of young children.

Kim Sungwon S Yoon Haesung H Lee Mi-Jung MJ Kim Myung-Joon MJ Han Kyunghwa K Yoon Ja Kyung JK Kim Hyung Cheol HC Shin Jaeseung J Shin Hyun Joo HJ

Scientific reports 20191219 1

The purpose of this study was to develop and test the performance of a deep learning-based algorithm to detect ileocolic intussusception using abdominal radiographs of young children. For the training set, children (≤5 years old) who underwent abdominal radiograph and ultrasonography (US) for suspicion of intussusception from March 2005 to December 2017 were retrospectively included and divided into control and intussusception groups according to the US results. A YOLOv3-based algorithm was deve ...[more]

PMID: 31857641

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Dataset Information

Performance of deep learning-based algorithm for detection of ileocolic intussusception on abdominal radiographs of young children.

Publications

Performance of deep learning-based algorithm for detection of ileocolic intussusception on abdominal radiographs of young children.

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