Feasibility of a deep learning artificial intelligence model for the diagnosis of pediatric ileocolic intussusception with grayscale ultrasonography

Kim Se Woo; 천정은; 최영훈; Hwang Jae-Yeon; Shin Su-Mi; Yeon Jin Cho; Seunghyun Lee; Seul Bi Lee

doi:10.14366/usg.23153

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저자정보: Kim Se Woo (Department of Radiology, Seoul National University Hospital, Seoul, Korea.Department of Radiology, Seoul National University College of Medicine, Seoul, Korea.) 천정은 (서울대학교) 최영훈 (서울대학교병원) Hwang Jae-Yeon (Department of Radiology, Pusan National University Yangsan Hospital, Yangsan, Korea.Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan) Shin Su-Mi (Department of Radiology, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul, the Republic of Korea.) Yeon Jin Cho (Department of Radiology, Seoul National University Children’s Hospital) Seunghyun Lee (Department of Radiology, Seoul National University Children’s Hospital) Seul Bi Lee (Department of Radiology, Seoul National University Children’s Hospital)

저널정보: 대한초음파의학회 ULTRASONOGRAPHY ULTRASONOGRAPHY Vol.43 No.1

발행연도: 2024.1

수록면: 57 - 67 (11page)

DOI: 10.14366/usg.23153

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Purpose: This study explored the feasibility of utilizing a deep learning artificial intelligence (AI) model to detect ileocolic intussusception on grayscale ultrasound images.Methods: This retrospective observational study incorporated ultrasound images of children who underwent emergency ultrasonography for suspected ileocolic intussusception. After excluding video clips, Doppler images, and annotated images, 40,765 images from two tertiary hospitals were included (positive-to-negative ratio: hospital A, 2,775:35,373; hospital B, 140:2,477). Images from hospital A were split into a training set, a tuning set, and an internal test set (ITS) at a ratio of 7:1.5:1.5. Images from hospital B comprised an external test set (ETS). For each image indicating intussusception, two radiologists provided a bounding box as the ground-truth label. If intussusception was suspected in the input image, the model generated a bounding box with a confidence score (0-1) at the estimated lesion location. Average precision (AP) was used to evaluate overall model performance. The performance of practical thresholds for the modelgenerated confidence score, as determined from the ITS, was verified using the ETS.Results: The AP values for the ITS and ETS were 0.952 and 0.936, respectively. Two confidence thresholds, CTopt and CTprecision, were set at 0.557 and 0.790, respectively. For the ETS, the perimage precision and recall were 95.7% and 80.0% with CTopt, and 98.4% and 44.3% with CTprecision. For per-patient diagnosis, the sensitivity and specificity were 100.0% and 97.1% with CTopt, and 100.0% and 99.0% with CTprecision. The average number of false positives per patient was 0.04 with CTopt and 0.01 for CTprecision.Conclusion: The feasibility of using an AI model to diagnose ileocolic intussusception on ultrasonography was demonstrated. However, further study involving bias-free data is warranted for robust clinical validation.

Purpose: This study explored the feasibility of utilizing a deep learning artificial intelligence (AI) model to detect ileocolic intussusception on grayscale ultrasound images. Methods: This retrospective observational study incorporated ultrasound images of children who underwent emergency ultrasonography for suspected ileocolic intussusception. After excluding video clips, Doppler images, and annotated images, 40,765 images from two tertiary hospitals were included (positive-to-negative ratio: Seoul National University Hospital, 2,775:35,373; Pusan National University Yangsan Hospital, 140:2,477). Images from Seoul National University Hospital were split into a training set, a tuning set, and an internal test set (ITS) at a ratio of 7:1.5:1.5. Images from Pusan National University Yangsan Hospital comprised an external test set (ETS). For each image indicating intussusception, two radiologists provided a bounding box as the ground- truth label. If intussusception was suspected in the input image, the model generated a bounding box with a confidence score (0-1) at the estimated lesion location. Average precision (AP) was used to evaluate overall model performance. The performance of practical thresholds for the model- generated confidence score, as determined from the ITS, was verified using the ETS. Results: The AP values for the ITS and ETS were 0.952 and 0.936, respectively. Two confidence thresholds, CTopt and CTprecision, were set at 0.557 and 0.790, respectively. For the ETS, the per- image precision and recall were 95.7% and 80.0% with CTopt, and 98.4% and 44.3% with CTprecision. For per-patient diagnosis, the sensitivity and specificity were 100.0% and 97.1% with CTopt, and 100.0% and 99.0% with CTprecision. The average number of false positives per patient was 0.04 with CTopt and 0.01 for CTprecision. Conclusion: The feasibility of using an AI model to diagnose ileocolic intussusception on ultrasonography was demonstrated. However, further study involving bias-free data is warranted for robust clinical validation.

#Intussusception #Ultrasound #Deep learning #Artificial intelligence #Pediatric emergency medicine

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