Using Ultrasound Image Augmentation and Ensemble Predictions to Prevent Machine-Learning Model Overfitting

Snider, Eric J.; Hernandez-Torres, Sofia I.; Hennessey, Ryan

doi:10.3390/diagnostics13030417

Cited by 12 publications

(5 citation statements)

References 44 publications

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“…This will be critical to evaluate prior to real-time implementation, as subject variability is clinically important, and a deployable model must be generalized to handle this variability. Additional data augmentation or ensemble prediction approaches may be needed to make objection models more generalized going forward [45]. Third, a limited number of object detection models were evaluated in this study, and as a result, a more optimal model may exist.…”

Section: Discussionmentioning

confidence: 99%

Performance Comparison of Object Detection Networks for Shrapnel Identification in Ultrasound Images

2023

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Ultrasound imaging is a critical tool for triaging and diagnosing subjects but only if images can be properly interpreted. Unfortunately, in remote or military medicine situations, the expertise to interpret images can be lacking. Machine-learning image interpretation models that are explainable to the end user and deployable in real time with ultrasound equipment have the potential to solve this problem. We have previously shown how a YOLOv3 (You Only Look Once) object detection algorithm can be used for tracking shrapnel, artery, vein, and nerve fiber bundle features in a tissue phantom. However, real-time implementation of an object detection model requires optimizing model inference time. Here, we compare the performance of five different object detection deep-learning models with varying architectures and trainable parameters to determine which model is most suitable for this shrapnel-tracking ultrasound image application. We used a dataset of more than 16,000 ultrasound images from gelatin tissue phantoms containing artery, vein, nerve fiber, and shrapnel features for training and evaluating each model. Every object detection model surpassed 0.85 mean average precision except for the detection transformer model. Overall, the YOLOv7tiny model had the higher mean average precision and quickest inference time, making it the obvious model choice for this ultrasound imaging application. Other object detection models were overfitting the data as was determined by lower testing performance compared with higher training performance. In summary, the YOLOv7tiny object detection model had the best mean average precision and inference time and was selected as optimal for this application. Next steps will implement this object detection algorithm for real-time applications, an important next step in translating AI models for emergency and military medicine.

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Section: Discussionmentioning

confidence: 99%

Performance Comparison of Object Detection Networks for Shrapnel Identification in Ultrasound Images

2023

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“…A widely used approach is to augment image inputs so that AI models less easily focus on image artifacts not associated with the injury. For this effort, we used rotation, flip, zoom, translation augmentations similar to approaches successfully used in other AI US imaging efforts ( Hussain et al, 2017 ; Xu et al, 2022 ; Snider et al, 2023 ). Another approach taken was to include validation patience during training so that training ceased if validation loss did not decrease for five training epochs.…”

Section: Discussionmentioning

confidence: 99%

An extended focused assessment with sonography in trauma ultrasound tissue-mimicking phantom for developing automated diagnostic technologies

Hernandez-Torres,

Bedolla,

Berard

et al. 2023

Front. Bioeng. Biotechnol.

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Introduction: Medical imaging-based triage is critical for ensuring medical treatment is timely and prioritized. However, without proper image collection and interpretation, triage decisions can be hard to make. While automation approaches can enhance these triage applications, tissue phantoms must be developed to train and mature these novel technologies. Here, we have developed a tissue phantom modeling the ultrasound views imaged during the enhanced focused assessment with sonography in trauma exam (eFAST).Methods: The tissue phantom utilized synthetic clear ballistic gel with carveouts in the abdomen and rib cage corresponding to the various eFAST scan points. Various approaches were taken to simulate proper physiology without injuries present or to mimic pneumothorax, hemothorax, or abdominal hemorrhage at multiple locations in the torso. Multiple ultrasound imaging systems were used to acquire ultrasound scans with or without injury present and were used to train deep learning image classification predictive models.Results: Performance of the artificial intelligent (AI) models trained in this study achieved over 97% accuracy for each eFAST scan site. We used a previously trained AI model for pneumothorax which achieved 74% accuracy in blind predictions for images collected with the novel eFAST tissue phantom. Grad-CAM heat map overlays for the predictions identified that the AI models were tracking the area of interest for each scan point in the tissue phantom.Discussion: Overall, the eFAST tissue phantom ultrasound scans resembled human images and were successful in training AI models. Tissue phantoms are critical first steps in troubleshooting and developing medical imaging automation technologies for this application that can accelerate the widespread use of ultrasound imaging for emergency triage.

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“…Furthermore, the performance of the model can be assessed for each sample. The LOO method is recommended and is quite commonly used in the case of small datasets (from a dozen to several dozen samples) [59][60][61][62][63].…”

Section: Data Analysis Methods and Methodologymentioning

confidence: 99%

Texture Analysis for the Bone Age Assessment from MRI Images of Adolescent Wrists in Boys

Obuchowicz

Nurzynska

Pierzchała³

et al. 2023

JCM

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Currently, bone age is assessed by X-rays. It enables the evaluation of the child’s development and is an important diagnostic factor. However, it is not sufficient to diagnose a specific disease because the diagnoses and prognoses may arise depending on how much the given case differs from the norms of bone age. Background: The use of magnetic resonance images (MRI) to assess the age of the patient would extend diagnostic possibilities. The bone age test could then become a routine screening test. Changing the method of determining the bone age would also prevent the patient from taking a dose of ionizing radiation, making the test less invasive. Methods: The regions of interest containing the wrist area and the epiphyses of the radius are marked on the magnetic resonance imaging of the non-dominant hand of boys aged 9 to 17 years. Textural features are computed for these regions, as it is assumed that the texture of the wrist image contains information about bone age. Results: The regression analysis revealed that there is a high correlation between the bone age of a patient and the MRI-derived textural features derived from MRI. For DICOM T1-weighted data, the best scores reached 0.94 R2, 0.46 RMSE, 0.21 MSE, and 0.33 MAE. Conclusions: The experiments performed have shown that using the MRI images gives reliable results in the assessment of bone age while not exposing the patient to ionizing radiation.

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Using Ultrasound Image Augmentation and Ensemble Predictions to Prevent Machine-Learning Model Overfitting

Cited by 12 publications

References 44 publications

Performance Comparison of Object Detection Networks for Shrapnel Identification in Ultrasound Images

Performance Comparison of Object Detection Networks for Shrapnel Identification in Ultrasound Images

An extended focused assessment with sonography in trauma ultrasound tissue-mimicking phantom for developing automated diagnostic technologies

Texture Analysis for the Bone Age Assessment from MRI Images of Adolescent Wrists in Boys

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