Pediatric chest radiograph interpretation: how far has artificial intelligence come? A systematic literature review

Padash, Sirwa; Mohebbian, Mohammad Reza; Adams, Scott; Henderson, Robert D. E.; Babyn, Paul

doi:10.1007/s00247-022-05368-w

Cited by 25 publications

(10 citation statements)

References 86 publications

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“…As such, our findings likely reflect the inherent difference in specificity between screening for pneumonia based solely on a radiograph report and the impact of incorporating additional clinical data like vital signs, physical exam findings, laboratory results, comorbid conditions and response to initial treatments, all of which are integrated into a final diagnosis code. We (46), among others (47). These algorithms may be combined with radiologist interpretation to potentially work together in a complementary fashion to improve the overall accuracy of the predictive model.…”

Section: Discussionmentioning

confidence: 86%

The development of a novel natural language processing tool to identify pediatric chest radiograph reports with pneumonia

Rixe

Frisch

Wang

et al. 2023

Front. Digit. Health

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ObjectiveChest radiographs are frequently used to diagnose community-acquired pneumonia (CAP) for children in the acute care setting. Natural language processing (NLP)-based tools may be incorporated into the electronic health record and combined with other clinical data to develop meaningful clinical decision support tools for this common pediatric infection. We sought to develop and internally validate NLP algorithms to identify pediatric chest radiograph (CXR) reports with pneumonia.Materials and methodsWe performed a retrospective study of encounters for patients from six pediatric hospitals over a 3-year period. We utilized six NLP techniques: word embedding, support vector machines, extreme gradient boosting (XGBoost), light gradient boosting machines Naïve Bayes and logistic regression. We evaluated their performance of each model from a validation sample of 1,350 chest radiographs developed as a stratified random sample of 35% admitted and 65% discharged patients when both using expert consensus and diagnosis codes.ResultsOf 172,662 encounters in the derivation sample, 15.6% had a discharge diagnosis of pneumonia in a primary or secondary position. The median patient age in the derivation sample was 3.7 years (interquartile range, 1.4–9.5 years). In the validation sample, 185/1350 (13.8%) and 205/1350 (15.3%) were classified as pneumonia by content experts and by diagnosis codes, respectively. Compared to content experts, Naïve Bayes had the highest sensitivity (93.5%) and XGBoost had the highest F1 score (72.4). Compared to a diagnosis code of pneumonia, the highest sensitivity was again with the Naïve Bayes (80.1%), and the highest F1 score was with the support vector machine (53.0%).ConclusionNLP algorithms can accurately identify pediatric pneumonia from radiography reports. Following external validation and implementation into the electronic health record, these algorithms can facilitate clinical decision support and inform large database research.

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Section: Discussionmentioning

confidence: 86%

The development of a novel natural language processing tool to identify pediatric chest radiograph reports with pneumonia

Rixe

Frisch

Wang

et al. 2023

Front. Digit. Health

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“…In the past 5 years, there has been a rapid expansion in the application of artificial intelligence technology in examining chest X-ray images, the majority of which have been concentrated on adults, and its application in pediatric is still scant [ 12 ]. It is common knowledge that the imaging manifestations of children’s chest will alter throughout their normal growth and development.…”

Section: Discussionmentioning

confidence: 99%

Residual networks models detection of atrial septal defect from chest radiographs

Luo,

Li,

et al. 2023

Radiol med

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Object The purpose of this study was to explore a machine learning-based residual networks (ResNets) model to detect atrial septal defect (ASD) on chest radiographs. Methods This retrospective study included chest radiographs consecutively collected at our hospital from June 2017 to May 2022. Qualified chest radiographs were obtained from patients who had finished echocardiography. These chest radiographs were labeled as positive or negative for ASD based on the echocardiographic reports and were divided into training, validation, and test dataset. Six ResNets models were employed to examine and compare by using the training dataset and was tuned using the validation dataset. The area under the curve, recall, precision and F1-score were taken as the evaluation metrics for classification result in the test dataset. Visualizing regions of interest for the ResNets models using heat maps. Results This study included a total of 2105 chest radiographs of children with ASD (mean age 4.14 ± 2.73 years, 54% male), patients were randomly assigned to training, validation, and test dataset with an 8:1:1 ratio. Healthy children’s images were supplemented to three datasets in a 1:1 ratio with ASD patients. Following the training, ResNet-10t and ResNet-18D have a better estimation performance, with precision, recall, accuracy, F1-score, and the area under the curve being (0.92, 0.93), (0.91, 0.91), (0.90, 0.90), (0.91, 0.91) and (0.97, 0.96), respectively. Compared to ResNet-18D, ResNet-10t was more focused on the distribution of the heat map of the interest region for most chest radiographs from ASD patients. Conclusion The ResNets model is feasible for identifying ASD through children’s chest radiographs. ResNet-10t stands out as the preferable estimation model, providing exceptional performance and clear interpretability.

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“…Further, few public databases exist for children, and clinical data are often buried within institutional medical records, making access to a sufficient volume of high quality multi‐institutional data challenging 28 . For example, of the 29 publicly available chest radiograph databases, only seven include any pediatric data, and two are exclusively for children 29 . Moreover, companies employing AI in health care, such as Google Health's Deep Mind and IBM's Watson Health, have published landmark studies consisting of solely adult data, suggesting the landscape of pediatric representation among proprietary data is similarly limited 30–32 …”

Section: Problem 3: Lack Of Datamentioning

confidence: 99%

“… 28 For example, of the 29 publicly available chest radiograph databases, only seven include any pediatric data, and two are exclusively for children. 29 Moreover, companies employing AI in health care, such as Google Health's Deep Mind and IBM's Watson Health, have published landmark studies consisting of solely adult data, suggesting the landscape of pediatric representation among proprietary data is similarly limited. 30 , 31 , 32 …”

Section: Problem 3: Lack Of Datamentioning

confidence: 99%

Will artificial intelligence widen the therapeutic gap between children and adults?

Nagy,

Sisk,

Lai

et al. 2023

Pediatric Investigation

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Pediatric chest radiograph interpretation: how far has artificial intelligence come? A systematic literature review

Cited by 25 publications

References 86 publications

The development of a novel natural language processing tool to identify pediatric chest radiograph reports with pneumonia

The development of a novel natural language processing tool to identify pediatric chest radiograph reports with pneumonia

Residual networks models detection of atrial septal defect from chest radiographs

Will artificial intelligence widen the therapeutic gap between children and adults?

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