Detection of high-grade small bowel obstruction on conventional radiography with convolutional neural networks

Cheng, Phillip M.; Tejura, Tapas; Tran, Khoa; Whang, Gilbert

doi:10.1007/s00261-017-1294-1

Cited by 38 publications

(22 citation statements)

References 16 publications

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“…Second, as part of an automated imaging pipeline, the model can be used to route images to more specialized networks for abnormality detection. For instance, the model can first identify a chest image so that it can then be analyzed by a network specialized for detecting anomalies in chest radiographs [ 9 ], abdominal radiographs [ 10 ], or musculoskeletal radiographs [ 11 – 14 ]. Again, our model did not achieve perfect accuracy for all classes.…”

Section: Discussionmentioning

confidence: 99%

Practical applications of deep learning: classifying the most common categories of plain radiographs in a PACS using a neural network

et al. 2020

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Objectives The goal of the present study was to classify the most common types of plain radiographs using a neural network and to validate the network’s performance on internal and external data. Such a network could help improve various radiological workflows. Methods All radiographs from the year 2017 (n = 71,274) acquired at our institution were retrieved from the PACS. The 30 largest categories (n = 58,219, 81.7% of all radiographs performed in 2017) were used to develop and validate a neural network (MobileNet v1.0) using transfer learning. Image categories were extracted from DICOM metadata (study and image description) and mapped to the WHO manual of diagnostic imaging. As an independent, external validation set, we used images from other institutions that had been stored in our PACS (n = 5324). Results In the internal validation, the overall accuracy of the model was 90.3% (95%CI: 89.2–91.3%), whereas, for the external validation set, the overall accuracy was 94.0% (95%CI: 93.3–94.6%). Conclusions Using data from one single institution, we were able to classify the most common categories of radiographs with a neural network. The network showed good generalizability on the external validation set and could be used to automatically organize a PACS, preselect radiographs so that they can be routed to more specialized networks for abnormality detection or help with other parts of the radiological workflow (e.g., automated hanging protocols; check if ordered image and performed image are the same). The final AI algorithm is publicly available for evaluation and extension. Key Points • Data from one single institution can be used to train a neural network for the correct detection of the 30 most common categories of plain radiographs. • The trained model achieved a high accuracy for the majority of categories and showed good generalizability to images from other institutions. • The neural network is made publicly available and can be used to automatically organize a PACS or to preselect radiographs so that they can be routed to more specialized neural networks for abnormality detection.

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Section: Discussionmentioning

confidence: 99%

Practical applications of deep learning: classifying the most common categories of plain radiographs in a PACS using a neural network

et al. 2020

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“…Many of the papers focused on detection tasks use transfer learning with architectures from computer vision . Examples of this approach can be found in many publications, including those for lesion detection in breast ultrasound, for the detection of bowel obstructions in radiography, and for the detection of the third lumbar vertebra slice in a CT scan . Usage of CNNs in lesion detection is not limited to architectures taken directly from computer vision but also includes some applications where custom architectures are used …”

Section: Application Areas In Radiological Imaging and Radiation Therapymentioning

confidence: 99%

Deep learning in medical imaging and radiation therapy

et al. 2018

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The goals of this review paper on deep learning (DL) in medical imaging and radiation therapy are to (a) summarize what has been achieved to date; (b) identify common and unique challenges, and strategies that researchers have taken to address these challenges; and (c) identify some of the promising avenues for the future both in terms of applications as well as technical innovations. We introduce the general principles of DL and convolutional neural networks, survey five major areas of application of DL in medical imaging and radiation therapy, identify common themes, discuss methods for dataset expansion, and conclude by summarizing lessons learned, remaining challenges, and future directions.

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“…Small intestinal obstruction: Cheng et al [ 98 , 99 ] used CNN to analyze abdominal radiographs to assist in the diagnosis of small intestinal obstruction (SIO). The sensitivity and specificity of the CNN diagnostic system were 83.8% and 68.1%, respectively, based on the training set of 2210 abdominal radiographs.…”

Section: Ai In Common Small Intestinal Diseasesmentioning

confidence: 99%

Artificial intelligence in small intestinal diseases: Application and prospects

Yu¹,

Li²,

Yao³

et al. 2021

WJG

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The small intestine is located in the middle of the gastrointestinal tract, so small intestinal diseases are more difficult to diagnose than other gastrointestinal diseases. However, with the extensive application of artificial intelligence in the field of small intestinal diseases, with its efficient learning capacities and computational power, artificial intelligence plays an important role in the auxiliary diagnosis and prognosis prediction based on the capsule endoscopy and other examination methods, which improves the accuracy of diagnosis and prediction and reduces the workload of doctors. In this review, a comprehensive retrieval was performed on articles published up to October 2020 from PubMed and other databases. Thereby the application status of artificial intelligence in small intestinal diseases was systematically introduced, and the challenges and prospects in this field were also analyzed.

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Detection of high-grade small bowel obstruction on conventional radiography with convolutional neural networks

Cited by 38 publications

References 16 publications

Practical applications of deep learning: classifying the most common categories of plain radiographs in a PACS using a neural network

Practical applications of deep learning: classifying the most common categories of plain radiographs in a PACS using a neural network

Deep learning in medical imaging and radiation therapy

Artificial intelligence in small intestinal diseases: Application and prospects

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