Computer-aided Assessment of Catheters and Tubes on Radiographs: How Good Is Artificial Intelligence for Assessment?

Xin, Yi; Adams, Scott; Henderson, Robert D. E.; Babyn, Paul

doi:10.1148/ryai.2020190082

Cited by 27 publications

(24 citation statements)

References 43 publications

(40 reference statements)

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“…Several studies have been performed for the detection of lines and tubes in chest radiographs [44][45][46][47][48][49]. Generally the algorithms are quite good in classifying the presence versus absence of an endotracheal tube [50], but the systems perform worse when the exact position of the tip of the tube is sought.…”

Section: Lines/tubesmentioning

confidence: 99%

Current and emerging artificial intelligence applications in chest imaging: a pediatric perspective

2021

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Artificial intelligence (AI) applications for chest radiography and chest CT are among the most developed applications in radiology. More than 40 certified AI products are available for chest radiography or chest CT. These AI products cover a wide range of abnormalities, including pneumonia, pneumothorax and lung cancer. Most applications are aimed at detecting disease, complemented by products that characterize or quantify tissue. At present, none of the thoracic AI products is specifically designed for the pediatric population. However, some products developed to detect tuberculosis in adults are also applicable to children. Software is under development to detect early changes of cystic fibrosis on chest CT, which could be an interesting application for pediatric radiology. In this review, we give an overview of current AI products in thoracic radiology and cover recent literature about AI in chest radiography, with a focus on pediatric radiology. We also discuss possible pediatric applications.

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Section: Lines/tubesmentioning

confidence: 99%

Current and emerging artificial intelligence applications in chest imaging: a pediatric perspective

2021

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“…The authors declare no conflicts of interest. (19), a review article on computer-aided assessment of catheters and tubes where 100 cases were annotated for public use. However, these studies differ from the present work as neither focused on classification of catheter types, nor have the data been previously used to train a CNN.…”

Section: Significance Statementmentioning

confidence: 99%

“…An extensive review of the current state-of-the-art for artificial intelligence applied to catheter assessment was recently published (19), and we refer the reader there for more detail. In brief, there are four main questions related to assessment of catheters on radiographs: i) is there a catheter present?…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection and Classification of Multiple Catheters in Neonatal Radiographs with Deep Learning

et al. 2021

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We develop and evaluate a deep learning algorithm to classify multiple catheters on neonatal chest and abdominal radiographs. A convolutional neural network (CNN) was trained using a dataset of 777 neonatal chest and abdominal radiographs, with a split of 81%-9%-10% for training-validation-testing, respectively. We employed ResNet-50 (a CNN), pre-trained on ImageNet. Ground truth labelling was limited to tagging each image to indicate the presence or absence of endotracheal tubes (ETTs), nasogastric tubes (NGTs), and umbilical arterial and venous catheters (UACs, UVCs). The data set included 561 images containing 2 or more catheters, 167 images with only one, and 49 with none. Performance was measured with average precision (AP), calculated from the area under the precision-recall curve. On our test data, the algorithm achieved an overall AP (95% confidence interval) of 0.977 (0.679-0.999) for NGTs, 0.989 (0.751-1.000) for ETTs, 0.979 (0.873-0.997) for UACs, and 0.937 (0.785-0.984) for UVCs. Performance was similar for the set of 58 test images consisting of 2 or more catheters, with an AP of 0.975 (0.255-1.000) for NGTs, 0.997 (0.009-1.000) for ETTs, 0.981 (0.797-0.998) for UACs, and 0.937 (0.689-0.990) for UVCs. Our network thus achieves strong performance in the simultaneous detection of these four catheter types. Radiologists may use such an algorithm as a time-saving mechanism to automate reporting of catheters on radiographs.

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“…33 A similar approach has been explored for the detection of endotracheal tubes on adult chest radiographs, achieving an AUROC of 0.99 in classifying the presence of endotracheal tubes. 34 A more in-depth review of the current status of AI for catheter placement assessment can be found in the study by Yi et al 35 In 2019, IBM and MICCAI cohosted the Multimodal Learning for Clinical Decision Support Challenge that was dedicated to catheter detection and classification. 36 Since the data set now is publicly accessible, we expect to see more research regarding assessment of catheters and tubes on radiographs in the near future.…”

Section: Assessment Of Lines and Tubesmentioning

confidence: 99%

Artificial Intelligence Solutions for Analysis of X-ray Images

et al. 2020

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Artificial intelligence (AI) presents a key opportunity for radiologists to improve quality of care and enhance the value of radiology in patient care and population health. The potential opportunity of AI to aid in triage and interpretation of conventional radiographs (X-ray images) is particularly significant, as radiographs are the most common imaging examinations performed in most radiology departments. Substantial progress has been made in the past few years in the development of AI algorithms for analysis of chest and musculoskeletal (MSK) radiographs, with deep learning now the dominant approach for image analysis. Large public and proprietary image data sets have been compiled and have aided the development of AI algorithms for analysis of radiographs, many of which demonstrate accuracy equivalent to radiologists for specific, focused tasks. This article describes (1) the basis for the development of AI solutions for radiograph analysis, (2) current AI solutions to aid in the triage and interpretation of chest radiographs and MSK radiographs, (3) opportunities for AI to aid in noninterpretive tasks related to radiographs, and (4) considerations for radiology practices selecting AI solutions for radiograph analysis and integrating them into existing IT systems. Although comprehensive AI solutions across modalities have yet to be developed, institutions can begin to select and integrate focused solutions which increase efficiency, increase quality and patient safety, and add value for their patients.

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Computer-aided Assessment of Catheters and Tubes on Radiographs: How Good Is Artificial Intelligence for Assessment?

Cited by 27 publications

References 43 publications

Current and emerging artificial intelligence applications in chest imaging: a pediatric perspective

Current and emerging artificial intelligence applications in chest imaging: a pediatric perspective

Automatic Detection and Classification of Multiple Catheters in Neonatal Radiographs with Deep Learning

Artificial Intelligence Solutions for Analysis of X-ray Images

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