Development and Validation of a Deep Learning Model for Automated View Classification of Pediatric Focused Assessment with Sonography for Trauma (FAST)

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:10.1101/2020.10.14.20206607

Cited by 4 publications

(3 citation statements)

References 38 publications

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“…They found that ML could distinguish between the presence and absence of hemoperitoneum with a sensitivity of 100% and specificity of 90% and concluded that such DL algorithms can help clinicians interpret trauma USG results. A study applying a customized DL algorithm to USG still images and video clips of pediatric trauma patients showed that, compared to experts, the DL algorithm was able to detect hemoperitoneum in Morrison’s pouch USG images with a sensitivity of 91.9% (95% CI, 91.7–92.1%) and a specificity of 97.9% (95% CI, 97.8–98.0%) [ 32 ]. The authors showed that the accurate classification of USG images via DL algorithms is critical for ensuring the quality and feasibility of multi-level DL FAST models and improving the assessment of injured children.…”

Section: Discussionmentioning

confidence: 99%

“…As the concept of “point-of-care USG” is gaining popularity, free fluid in clinical situations is being identified via a comprehensive evaluation of continuous dynamic images rather than by the evaluation of still images. Additionally, the performance of customized DL algorithms in classifying USG images may be better with video clips than with still frames [ 32 ]. Therefore, further research on the performance of AutoML in analyzing USG images from video clips is needed.…”

Section: Discussionmentioning

confidence: 99%

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Use of Automated Machine Learning for Classifying Hemoperitoneum on Ultrasonographic Images of Morrison’s Pouch: A Multicenter Retrospective Study

Jeong

Lee

et al. 2023

JCM

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This study evaluated automated machine learning (AutoML) in classifying the presence or absence of hemoperitoneum in ultrasonography (USG) images of Morrison’s pouch. In this multicenter, retrospective study, 864 trauma patients from trauma and emergency medical centers in South Korea were included. In all, 2200 USG images (1100 hemoperitoneum and 1100 normal) were collected. Of these, 1800 images were used for training and 200 were used for the internal validation of AutoML. External validation was performed using 100 hemoperitoneum images and 100 normal images collected separately from a trauma center that were not included in the training and internal validation sets. Google’s open-source AutoML was used to train the algorithm in classifying hemoperitoneum in USG images, followed by internal and external validation. In the internal validation, the sensitivity, specificity, and area under the receiver operating characteristic (AUROC) curve were 95%, 99%, and 0.97, respectively. In the external validation, the sensitivity, specificity, and AUROC were 94%, 99%, and 0.97, respectively. The performances of AutoML in the internal and external validation were not statistically different (p = 0.78). A publicly available, general-purpose AutoML can accurately classify the presence or absence of hemoperitoneum in USG images of the Morrison’s pouch of real-world trauma patients.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Use of Automated Machine Learning for Classifying Hemoperitoneum on Ultrasonographic Images of Morrison’s Pouch: A Multicenter Retrospective Study

Jeong

Lee

et al. 2023

JCM

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Deep learning (DL) has been applied with success 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, and such models traditionally require large amounts of training data, straining the limited (human) resources available for expert labeling/annotation.

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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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ENRICH: Exploiting Image Similarity to Maximize Efficient Machine Learning in Medical Imaging

Chinn

Arora

Arnaout

et al. 2021

Preprint

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Deep learning (DL) requires labeled data. Labeling medical images requires medical expertise, which is often a bottleneck. It is therefore useful to prioritize labeling those images that are most likely to improve a model's performance, a practice known as instance selection. Here we introduce ENRICH, a method that selects images for labeling based on how much novelty each image adds to the growing training set. In our implementation, we use cosine similarity between autoencoder embeddings to measure that novelty. We show that ENRICH achieves nearly maximal performance on classification and segmentation tasks using only a fraction of available images, and outperforms the default practice of selecting images at random. We also present evidence that instance selection may perform categorically better on medical vs. non-medical imaging tasks. In conclusion, ENRICH is a simple, computationally efficient method for prioritizing images for expert labeling for DL.

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Development and Validation of a Deep Learning Model for Automated View Classification of Pediatric Focused Assessment with Sonography for Trauma (FAST)

Cited by 4 publications

References 38 publications

Use of Automated Machine Learning for Classifying Hemoperitoneum on Ultrasonographic Images of Morrison’s Pouch: A Multicenter Retrospective Study

Use of Automated Machine Learning for Classifying Hemoperitoneum on Ultrasonographic Images of Morrison’s Pouch: A Multicenter Retrospective Study

ENRICHing Medical Imaging Training Sets Enables More Efficient Machine Learning

ENRICH: Exploiting Image Similarity to Maximize Efficient Machine Learning in Medical Imaging

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