Deep learning algorithms for detecting and visualising intussusception on plain abdominal radiography in children: a retrospective multicenter study

Kwon, Gitaek; Ryu, Jongbin; Oh, Jaehoon; Lim, Jongwoo; Kang, Bo Kyeong; Ahn, Chiwon; Bae, Junwon; Lee, Dong Hoon

doi:10.1038/s41598-020-74653-1

Cited by 17 publications

(16 citation statements)

References 32 publications

(35 reference statements)

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“…The first stage is to detect the wrist ROI, then the segmentation model uses this detected wrist ROI as the input image. This cascade system can focus on this ROI and segment ten wrist bones more precisely, which could be helpful for our study, wherein the ROI is a small section of the overall image [ 25 , 26 ].…”

Section: Methodsmentioning

confidence: 99%

Automatic Segmentation for Favourable Delineation of Ten Wrist Bones on Wrist Radiographs Using Convolutional Neural Network

Kang

Han

et al. 2022

JPM

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Purpose: This study aimed to develop and validate an automatic segmentation algorithm for the boundary delineation of ten wrist bones, consisting of eight carpal and two distal forearm bones, using a convolutional neural network (CNN). Methods: We performed a retrospective study using adult wrist radiographs. We labeled the ground truth masking of wrist bones, and propose that the Fine Mask R-CNN consisted of wrist regions of interest (ROI) using a Single-Shot Multibox Detector (SSD) and segmentation via Mask R-CNN, plus the extended mask head. The primary outcome was an improvement in the prediction of delineation via the network combined with ground truth masking, and this was compared between two networks through five-fold validations. Results: In total, 702 images were labeled for the segmentation of ten wrist bones. The overall performance (mean (SD] of Dice coefficient) of the auto-segmentation of the ten wrist bones improved from 0.93 (0.01) using Mask R-CNN to 0.95 (0.01) using Fine Mask R-CNN (p < 0.001). The values of each wrist bone were higher when using the Fine Mask R-CNN than when using the alternative (all p < 0.001). The value derived for the distal radius was the highest, and that for the trapezoid was the lowest in both networks. Conclusion: Our proposed Fine Mask R-CNN model achieved good performance in the automatic segmentation of ten overlapping wrist bones derived from adult wrist radiographs.

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Section: Methodsmentioning

confidence: 99%

Automatic Segmentation for Favourable Delineation of Ten Wrist Bones on Wrist Radiographs Using Convolutional Neural Network

Kang

Han

et al. 2022

JPM

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“…In recent years, however, studies investigating risk stratification for intussusception in children have demonstrated the utility of abdominal X-rays as an initial diagnostic modality, with one study reporting sensitivity and specificity values of 0.77 and 0.79, respectively [ 29 ]. Unlike ultrasound (US), plain radiography is unaffected by operator skill and equipment variability and remains an inexpensive option for a first-line screening test [ 19 ]. As such, the implementation of AI algorithms in abdominal radiography may have a broad patient impact, and it shows promise as an initial point of entry.…”

Section: Diseases Of the Digestive Tractmentioning

confidence: 99%

“…The sensitivity of the algorithm was higher when compared with radiologist interpretation alone (0.76 vs. 0.46), while there were no significant differences in the specificity (0.96 vs. 0.92) [ 30 ]. More recent studies with larger sample sizes have demonstrated improved detection of intussusception with ranges between 0.91–0.94 and 0.85–0.91, respectively [ 19 ]. Other authors have described similar findings with AUC values of 0.95 and 0.97 and an accuracy of 0.93 and 0.95 [ 19 ].…”

Section: Diseases Of the Digestive Tractmentioning

confidence: 99%

“…More recent studies with larger sample sizes have demonstrated improved detection of intussusception with ranges between 0.91–0.94 and 0.85–0.91, respectively [ 19 ]. Other authors have described similar findings with AUC values of 0.95 and 0.97 and an accuracy of 0.93 and 0.95 [ 19 ]. Thus, as more data are gathered, hospitals may train these algorithms and institute them for routine use in emergency radiology.…”

Section: Diseases Of the Digestive Tractmentioning

confidence: 99%

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An Extra Set of Intelligent Eyes: Application of Artificial Intelligence in Imaging of Abdominopelvic Pathologies in Emergency Radiology

et al. 2022

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Imaging in the emergent setting carries high stakes. With increased demand for dedicated on-site service, emergency radiologists face increasingly large image volumes that require rapid turnaround times. However, novel artificial intelligence (AI) algorithms may assist trauma and emergency radiologists with efficient and accurate medical image analysis, providing an opportunity to augment human decision making, including outcome prediction and treatment planning. While traditional radiology practice involves visual assessment of medical images for detection and characterization of pathologies, AI algorithms can automatically identify subtle disease states and provide quantitative characterization of disease severity based on morphologic image details, such as geometry and fluid flow. Taken together, the benefits provided by implementing AI in radiology have the potential to improve workflow efficiency, engender faster turnaround results for complex cases, and reduce heavy workloads. Although analysis of AI applications within abdominopelvic imaging has primarily focused on oncologic detection, localization, and treatment response, several promising algorithms have been developed for use in the emergency setting. This article aims to establish a general understanding of the AI algorithms used in emergent image-based tasks and to discuss the challenges associated with the implementation of AI into the clinical workflow.

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“…Third, there was a difference in resolution between the medical images and the input images due to resizing. Therefore, it is possible for information loss to occur when attempting to detect FNF since the medical images were downsized [35]. Finally, external validation in phase 1, applying the network of trained with single hospital directly to another hospital showed lower accuracy than the results using a single hospital.…”

Section: Limitationsmentioning

confidence: 99%

External Validation of Deep Learning Algorithm for Detecting and Visualizing Femoral Neck Fracture Including Displaced and Non-displaced Fracture on Plain X-ray

Bae

et al. 2021

J Digit Imaging

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This study aimed to develop a method for detection of femoral neck fracture (FNF) including displaced and non-displaced fractures using convolutional neural network (CNN) with plain X-ray and to validate its use across hospitals through internal and external validation sets. This is a retrospective study using hip and pelvic anteroposterior films for training and detecting femoral neck fracture through residual neural network (ResNet) 18 with convolutional block attention module (CBAM) + + . The study was performed at two tertiary hospitals between February and May 2020 and used data from January 2005 to December 2018. Our primary outcome was favorable performance for diagnosis of femoral neck fracture from negative studies in our dataset. We described the outcomes as area under the receiver operating characteristic curve (AUC), accuracy, Youden index, sensitivity, and specificity. A total of 4,189 images that contained 1,109 positive images (332 non-displaced and 777 displaced) and 3,080 negative images were collected from two hospitals. The test values after training with one hospital dataset were 0.999 AUC, 0.986 accuracy, 0.960 Youden index, and 0.966 sensitivity, and 0.993 specificity. Values of external validation with the other hospital dataset were 0.977, 0.971, 0.920, 0.939, and 0.982, respectively. Values of merged hospital datasets were 0.987, 0.983, 0.960, 0.973, and 0.987, respectively. A CNN algorithm for FNF detection in both displaced and non-displaced fractures using plain X-rays could be used in other hospitals to screen for FNF after training with images from the hospital of interest. KeywordsFemur • Fracture • Deep learning • Machine learning • Artificial intelligence • AI Abbreviations FNF Femoral neck fracture CNN Convolution neural network ResNet Residual neural network CBAM Convolutional block attention module AUC Area under the receiver operating characteristic curve CT Computed tomography MRI Magnetic resonance imaging Junwon Bae and Sangjoon Yu contributed this work equally as first author.

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Deep learning algorithms for detecting and visualising intussusception on plain abdominal radiography in children: a retrospective multicenter study

Cited by 17 publications

References 32 publications

Automatic Segmentation for Favourable Delineation of Ten Wrist Bones on Wrist Radiographs Using Convolutional Neural Network

Automatic Segmentation for Favourable Delineation of Ten Wrist Bones on Wrist Radiographs Using Convolutional Neural Network

An Extra Set of Intelligent Eyes: Application of Artificial Intelligence in Imaging of Abdominopelvic Pathologies in Emergency Radiology

External Validation of Deep Learning Algorithm for Detecting and Visualizing Femoral Neck Fracture Including Displaced and Non-displaced Fracture on Plain X-ray

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