Development and Validation of a Deep Learning Strategy for Automated View Classification of Pediatric Focused Assessment With Sonography for Trauma

Kornblith, Aaron E.; Addo, Newton; Dong, Ruolei; Rogers, R. D.; Grupp‐Phelan, Jacqueline; Butte, Atul S.; Gupta, P. K.; Callcut, Rachael A.; Arnaout, Rima

doi:10.1002/jum.15868

Cited by 8 publications

(6 citation statements)

References 37 publications

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“…Furthermore, anatomic identification has been used in trauma imaging. Artificial intelligence and DL platforms have been applied to more than 1 million pediatric FAST examinations, to categorize the view, with up to 97% accuracy, based on density, shape, and location of the tissue displayed 27 . This technology is already being used clinically in orthopedic imaging for trauma victims, whereby fracture imaging, such as x-rays, is being classified through AI 28 .…”

Section: Discussionmentioning

confidence: 99%

“…Artificial intelligence and DL platforms have been applied to more than 1 million pediatric FAST examinations, to categorize the view, with up to 97% accuracy, based on density, shape, and location of the tissue displayed. 27 This technology is already being used clinically in orthopedic imaging for trauma victims, whereby fracture imaging, such as x-rays, is being classified through AI. 28 Artificial intelligence provides reasonable prediction of a fracture, 95% to 100%, and good accuracy in fracture classification of 83% to 98%.…”

Section: Discussionmentioning

confidence: 99%

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Artificial intelligence evaluation of focused assessment with sonography in trauma

Bumgardner

2023

J Trauma Acute Care Surg

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BACKGROUND The focused assessment with sonography in trauma (FAST) is a widely used imaging modality to identify the location of life-threatening hemorrhage in a hemodynamically unstable trauma patient. This study evaluates the role of artificial intelligence in interpretation of the FAST examination abdominal views, as it pertains to adequacy of the view and accuracy of fluid survey positivity. METHODS Focused assessment with sonography for trauma examination images from 2015 to 2022, from trauma activations, were acquired from a quaternary care level 1 trauma center with more than 3,500 adult trauma evaluations, annually. Images pertaining to the right upper quadrant and left upper quadrant views were obtained and read by a surgeon or radiologist. Positivity was defined as fluid present in the hepatorenal or splenorenal fossa, while adequacy was defined by the presence of both the liver and kidney or the spleen and kidney for the right upper quadrant or left upper quadrant views, respectively. Four convolutional neural network architecture models (DenseNet121, InceptionV3, ResNet50, Vgg11bn) were evaluated. RESULTS A total of 6,608 images, representing 109 cases were included for analysis within the “adequate” and “positive” data sets. The models relayed 88.7% accuracy, 83.3% sensitivity, and 93.6% specificity for the adequate test cohort, while the positive cohort conferred 98.0% accuracy, 89.6% sensitivity, and 100.0% specificity against similar models. Augmentation improved the accuracy and sensitivity of the positive models to 95.1% accurate and 94.0% sensitive. DenseNet121 demonstrated the best accuracy across tasks. CONCLUSION Artificial intelligence can detect positivity and adequacy of FAST examinations with 94% and 97% accuracy, aiding in the standardization of care delivery with minimal expert clinician input. Artificial intelligence is a feasible modality to improve patient care imaging interpretation accuracy and should be pursued as a point-of-care clinical decision-making tool. LEVEL OF EVIDENCE Diagnostic Test/Criteria; Level III.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Artificial intelligence evaluation of focused assessment with sonography in trauma

Bumgardner

2023

J Trauma Acute Care Surg

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“…They can also segment, track, and measure substructures within images [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17] . DL thus has great promise for helping meet the overwhelming need for accurate, reliable, and scalable image interpretation that currently exists in medicine due to a near-universal shortage of trained human experts 5,6,[18][19][20][21] .…”

Section: Background and Significancementioning

confidence: 99%

“…

Objective: Deep learning (DL) has been applied in proofs of concept across biomedical imaging, including across modalities and medical specialties [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17] . Labeled data is critical to training and testing DL models, but human expert labelers are limited.

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confidence: 99%

ENRICHing Medical Imaging Training Sets Enables More Efficient Machine Learning

Chinn

Arora

Arnaout

et al. 2021

Preprint

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Deep learning (DL) has been applied with success in proofs of concept across biomedical imaging, including across modalities and medical specialties1-17. Labeled data is critical to training and testing DL models, and such models traditionally require large amounts of training data, straining the limited (human) resources available for expert labeling/annotation. It would be ideal to prioritize labeling those images that are most likely to improve model performance and skip images that are redundant. However, straightforward, robust, and quantitative metrics for measuring and eliminating redundancy in datasets have not yet been described. Here, we introduce a new method, ENRIch (Eliminate Needless Redundancy in Imaging datasets), for assessing image dataset redundancy and test it on a well benchmarked medical imaging dataset3. First, we compute pairwise similarity metrics for images in a given dataset, resulting in a matrix of pairwise similarity values. We then rank images based on this matrix and use these rankings to curate the dataset, to minimize dataset redundancy. Using this method, we achieve similar AUC scores in a binary classification task with just a fraction of our original dataset (AUC of 0.99 +/- 1.35e-05 on 44 percent of available images vs. AUC of 0.99 +/- 9.32e-06 on all available images, p-value 0.0002) and better scores than the same sized training subsets chosen at random. We also demonstrate similar Jaccard scores in a multi-class segmentation task while eliminating redundant images (average Jaccard index of 0.58 on 80 percent of available images vs. 0.60 on all available images). Thus, algorithms that reduce dataset redundancy based on image similarity can significantly reduce the number of training images required, while preserving performance, in medical imaging datasets.

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“…First demonstrated for non-medical tasks, deep learning has shown remarkable utility for medical imaging in recent years [1][2][3]-with little to no need to adapt neural network architectures for medical domains. The use of augmentation techniques to enhance the diversity of available training data critical to training robust and generalizable deep learning models [4].…”

Section: Introductionmentioning

confidence: 99%

Domain-guided data augmentation for deep learning on medical imaging

Athalye

Arnaout

2023

PLoS ONE

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While domain-specific data augmentation can be useful in training neural networks for medical imaging tasks, such techniques have not been widely used to date. Our objective was to test whether domain-specific data augmentation is useful for medical imaging using a well-benchmarked task: view classification on fetal ultrasound FETAL-125 and OB-125 datasets. We found that using a context-preserving cut-paste strategy, we could create valid training data as measured by performance of the resulting trained model on the benchmark test dataset. When used in an online fashion, models trained on this hybrid data performed similarly to those trained using traditional data augmentation (FETAL-125 F-score 85.33 ± 0.24 vs 86.89 ± 0.60, p-value 0.014; OB-125 F-score 74.60 ± 0.11 vs 72.43 ± 0.62, p-value 0.004). Furthermore, the ability to perform augmentations during training time, as well as the ability to apply chosen augmentations equally across data classes, are important considerations in designing a bespoke data augmentation. Finally, we provide open-source code to facilitate running bespoke data augmentations in an online fashion. Taken together, this work expands the ability to design and apply domain-guided data augmentations for medical imaging tasks.

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Development and Validation of a Deep Learning Strategy for Automated View Classification of Pediatric Focused Assessment With Sonography for Trauma

Cited by 8 publications

References 37 publications

Artificial intelligence evaluation of focused assessment with sonography in trauma

Artificial intelligence evaluation of focused assessment with sonography in trauma

ENRICHing Medical Imaging Training Sets Enables More Efficient Machine Learning

Domain-guided data augmentation for deep learning on medical imaging

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