Integration of a deep learning system for automated chest x-ray interpretation in the emergency department: A proof-of-concept

Mosquera, Candelaria; Binder, Fernando; Diaz, Facundo Nahuel; Seehaus, Alberto; Ducrey, Gabriel; Ocantos, Jorge; Aineseder, M.; Rubin, Luciana; Rabinovich, Diego; Quiroga, Angel Ezequiel; Martínez, B.; Beresñak, Alejandro Daniel; Benítez, Sonia; Luna, Daniel

doi:10.1016/j.ibmed.2021.100039

Cited by 6 publications

(7 citation statements)

References 32 publications

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“…Accordingly, LOS for these patients and ED crowding may both increase, although LOS could be affected by other hospital related factors. Machine learning models should be applied carefully in the real world, and the accuracy of such models may not be as favorable as in a retrospective study [ 46 , 47 ]. Further validation studies are needed to evaluate the effects of implementing such a prediction model in clinical settings, including the effects on patient safety, 72 h URV rate, and ED crowding.…”

Section: Discussionmentioning

confidence: 99%

A Machine Learning Model for Predicting Unscheduled 72 h Return Visits to the Emergency Department by Patients with Abdominal Pain

et al. 2021

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Seventy-two-hour unscheduled return visits (URVs) by emergency department patients are a key clinical index for evaluating the quality of care in emergency departments (EDs). This study aimed to develop a machine learning model to predict 72 h URVs for ED patients with abdominal pain. Electronic health records data were collected from the Chang Gung Research Database (CGRD) for 25,151 ED visits by patients with abdominal pain and a total of 617 features were used for analysis. We used supervised machine learning models, namely logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGB), and voting classifier (VC), to predict URVs. The VC model achieved more favorable overall performance than other models (AUROC: 0.74; 95% confidence interval (CI), 0.69–0.76; sensitivity, 0.39; specificity, 0.89; F1 score, 0.25). The reduced VC model achieved comparable performance (AUROC: 0.72; 95% CI, 0.69–0.74) to the full models using all clinical features. The VC model exhibited the most favorable performance in predicting 72 h URVs for patients with abdominal pain, both for all-features and reduced-features models. Application of the VC model in the clinical setting after validation may help physicians to make accurate decisions and decrease URVs.

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Section: Discussionmentioning

confidence: 99%

A Machine Learning Model for Predicting Unscheduled 72 h Return Visits to the Emergency Department by Patients with Abdominal Pain

et al. 2021

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“…*PA = posteroanterior, RSNA = Radiological Society of North America, AUC = area under the receiver operating characteristic curve, and AP = anteroposterior. 44,45 . Only time will tell, but we believe that one thing is certain: The success of deep learning in the analysis of medical imaging has been propelling the field forward so rapidly that now is the time for surgeons to pause and understand how this technology works at a conceptual level, before the technology ends up in front of us and our patients.…”

Section: Insights Into Pain Disparities In Underserved Populationsmentioning

confidence: 99%

“…Until recently, these deep learning algorithms had been confined to research papers, narrow tasks, and specific regions of human anatomy, but the technology is advancing rapidly. Deep learning is now in the early stages of entering the clinical setting, involving validation and proof-of-concept studies 44,45 . Only time will tell, but we believe that one thing is certain: The success of deep learning in the analysis of medical imaging has been propelling the field forward so rapidly that now is the time for surgeons to pause and understand how this technology works at a conceptual level, before the technology ends up in front of us and our patients.…”

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confidence: 99%

Deep Learning and Imaging for the Orthopaedic Surgeon

Hill

Krogue

Jevsevar

et al. 2022

Journal of Bone and Joint Surgery

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ä In the not-so-distant future, orthopaedic surgeons will be exposed to machines that begin to automatically "read" medical imaging studies using a technology called deep learning.ä Deep learning has demonstrated remarkable progress in the analysis of medical imaging across a range of modalities that are commonly used in orthopaedics, including radiographs, computed tomographic scans, and magnetic resonance imaging scans.ä There is a growing body of evidence showing clinical utility for deep learning in musculoskeletal radiography, as evidenced by studies that use deep learning to achieve an expert or near-expert level of performance for the identification and localization of fractures on radiographs.ä Deep learning is currently in the very early stages of entering the clinical setting, involving validation and proof-ofconcept studies for automated medical image interpretation.ä The success of deep learning in the analysis of medical imaging has been propelling the field forward so rapidly that now is the time for surgeons to pause and understand how this technology works at a conceptual level, before (not after) the technology ends up in front of us and our patients. That is the purpose of this article.Disclosure: The Disclosure of Potential Conflicts of Interest forms are provided with the online version of the article (http://links.lww.com/JBJS/H59).

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“…Increasing the diagnostic utilization of radiography, which can be performed relatively easily and quickly in the early stages of trauma, may be helpful for emergency patients. Recently, studies using artificial intelligence have been reported on patients with various emergency diseases who visit the emergency room [ 5 , 6 ]. A deep learning model was developed to predict the exacerbation of COVID-19 pneumonia and diagnose pneumothorax, pleural effusion, and fracture in chest X-ray images, and the developed model showed a similar or higher area under the curve (AUC) than the diagnostic accuracy of specialists [ 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, studies using artificial intelligence have been reported on patients with various emergency diseases who visit the emergency room [ 5 , 6 ]. A deep learning model was developed to predict the exacerbation of COVID-19 pneumonia and diagnose pneumothorax, pleural effusion, and fracture in chest X-ray images, and the developed model showed a similar or higher area under the curve (AUC) than the diagnostic accuracy of specialists [ 5 , 6 ]. In addition to chest X-ray images, studies to detect femoral neck fractures using other X-ray images and to classify displaced and non-displaced fractures have also been reported [ 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

Machine learning model based on radiomics features for AO/OTA classification of pelvic fractures on pelvic radiographs

Park,

Lee,

Kim

et al. 2024

PLoS ONE

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Depending on the degree of fracture, pelvic fracture can be accompanied by vascular damage, and in severe cases, it may progress to hemorrhagic shock. Pelvic radiography can quickly diagnose pelvic fractures, and the Association for Osteosynthesis Foundation and Orthopedic Trauma Association (AO/OTA) classification system is useful for evaluating pelvic fracture instability. This study aimed to develop a radiomics-based machine-learning algorithm to quickly diagnose fractures on pelvic X-ray and classify their instability. data used were pelvic anteroposterior radiographs of 990 adults over 18 years of age diagnosed with pelvic fractures, and 200 normal subjects. A total of 93 features were extracted based on radiomics:18 first-order, 24 GLCM, 16 GLRLM, 16 GLSZM, 5 NGTDM, and 14 GLDM features. To improve the performance of machine learning, the feature selection methods RFE, SFS, LASSO, and Ridge were used, and the machine learning models used LR, SVM, RF, XGB, MLP, KNN, and LGBM. Performance measurement was evaluated by area under the curve (AUC) by analyzing the receiver operating characteristic curve. The machine learning model was trained based on the selected features using four feature-selection methods. When the RFE feature selection method was used, the average AUC was higher than that of the other methods. Among them, the combination with the machine learning model SVM showed the best performance, with an average AUC of 0.75±0.06. By obtaining a feature-importance graph for the combination of RFE and SVM, it is possible to identify features with high importance. The AO/OTA classification of normal pelvic rings and pelvic fractures on pelvic AP radiographs using a radiomics-based machine learning model showed the highest AUC when using the SVM classification combination. Further research on the radiomic features of each part of the pelvic bone constituting the pelvic ring is needed.

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Integration of a deep learning system for automated chest x-ray interpretation in the emergency department: A proof-of-concept

Cited by 6 publications

References 32 publications

A Machine Learning Model for Predicting Unscheduled 72 h Return Visits to the Emergency Department by Patients with Abdominal Pain

A Machine Learning Model for Predicting Unscheduled 72 h Return Visits to the Emergency Department by Patients with Abdominal Pain

Deep Learning and Imaging for the Orthopaedic Surgeon

Machine learning model based on radiomics features for AO/OTA classification of pelvic fractures on pelvic radiographs

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