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Li Y, Zhang L, Yu H, Wang J, Wang S, Liu J, Zheng Q. A comprehensive segmentation of chest X-ray improves deep learning-based WHO radiologically confirmed pneumonia diagnosis in children. Eur Radiol 2024; 34:3471-3482. [PMID: 37930411 DOI: 10.1007/s00330-023-10367-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 08/29/2023] [Accepted: 08/31/2023] [Indexed: 11/07/2023]
Abstract
OBJECTIVES To investigate a comprehensive segmentation of chest X-ray (CXR) in promoting deep learning-based World Health Organization's (WHO) radiologically confirmed pneumonia diagnosis in children. METHODS A total of 4400 participants between January 2016 and June 2021were identified for a cross-sectional study and divided into primary endpoint pneumonia (PEP), other infiltrates, and normal groups according to WHO's diagnostic criteria. The CXR was divided into six segments of left lung, right lung, mediastinum, diaphragm, ext-left lung, and ext-right lung by adopting the RA-UNet. To demonstrate the benefits of lung field segmentation in pneumonia diagnosis, the segmented images and images that were not segmented, which constituted seven segmentation combinations, were fed into the CBAM-ResNet under a three-category classification comparison. The interpretability of the CBAM-ResNet for pneumonia diagnosis was also performed by adopting a Grad-CAM module. RESULTS The RA-UNet achieved a high spatial overlap between manual and automatic segmentation (averaged DSC = 0.9639). The CBAM-ResNet when fed with the six segments achieved superior three-category diagnosis performance (accuracy = 0.8243) over other segmentation combinations and deep learning models under comparison, which was increased by around 6% in accuracy, precision, specificity, sensitivity, F1-score, and around 3% in AUC. The Grad-CAM could capture the pneumonia lesions more accurately, generating a more interpretable visualization and enhancing the superiority and reliability of our study in assisting pediatric pneumonia diagnosis. CONCLUSIONS The comprehensive segmentation of CXR could improve deep learning-based pneumonia diagnosis in childhood with a more reasonable WHO's radiological standardized pneumonia classification instead of conventional dichotomous bacterial pneumonia and viral pneumonia. CLINICAL RELEVANCE STATEMENT The comprehensive segmentation of chest X-ray improves deep learning-based WHO confirmed pneumonia diagnosis in children, laying a strong foundation for the potential inclusion of computer-aided pediatric CXR readings in precise classification of pneumonia and PCV vaccine trials efficacy in children. KEY POINTS • The chest X-ray was comprehensively segmented into six anatomical structures of left lung, right lung, mediastinum, diaphragm, ext-left lung, and ext-right lung. • The comprehensive segmentation improved the three-category classification of primary endpoint pneumonia, other infiltrates, and normal with an increase by around 6% in accuracy, precision, specificity, sensitivity, F1-score, and around 3% in AUC. • The comprehensive segmentation gave rise to a more accurate and interpretable visualization results in capturing the pneumonia lesions.
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Affiliation(s)
- Yuemei Li
- School of Computer and Control Engineering, Yantai University, Yantai, 264005, China
| | - Lin Zhang
- Department of Radiology, Xiamen Children's Hospital, Children's Hospital of Fudan University at Xiamen, Xiamen, Fujian, China
| | - Hu Yu
- School of Computer and Control Engineering, Yantai University, Yantai, 264005, China
| | - Jian Wang
- Department of Radiology, Xiamen Children's Hospital, Children's Hospital of Fudan University at Xiamen, Xiamen, Fujian, China
| | - Shuo Wang
- Yantai University Trier College of Sustainable Technology, Yantai, 264005, Shandong Province, China
- Trier University of Applied Sciences, D-54208, Trier, Germany
| | - Jungang Liu
- Department of Radiology, Xiamen Children's Hospital, Children's Hospital of Fudan University at Xiamen, Xiamen, Fujian, China.
| | - Qiang Zheng
- School of Computer and Control Engineering, Yantai University, Yantai, 264005, China.
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Gichuhi HW, Magumba M, Kumar M, Mayega RW. A machine learning approach to explore individual risk factors for tuberculosis treatment non-adherence in Mukono district. PLOS GLOBAL PUBLIC HEALTH 2023; 3:e0001466. [PMID: 37399173 DOI: 10.1371/journal.pgph.0001466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 06/05/2023] [Indexed: 07/05/2023]
Abstract
Despite the availability and implementation of well-known efficacious interventions for tuberculosis treatment by the Ministry of Health, Uganda (MoH), treatment non-adherence persists. Moreover, identifying a specific tuberculosis patient at risk of treatment non-adherence is still a challenge. Thus, this retrospective study, based on a record review of 838 tuberculosis patients enrolled in six health facilities, presents, and discusses a machine learning approach to explore the individual risk factors predictive of tuberculosis treatment non-adherence in the Mukono district, Uganda. Five classification machine learning algorithms, logistic regression (LR), artificial neural networks (ANN), support vector machines (SVM), random forest (RF), and AdaBoost were trained, and evaluated by computing their accuracy, F1 score, precision, recall, and the area under the receiver operating curve (AUC) through the aid of a confusion matrix. Of the five developed and evaluated algorithms, SVM (91.28%) had the highest accuracy (AdaBoost, 91.05% performed better than SVM when AUC is considered as evaluation parameter). Looking at all five evaluation parameters globally, AdaBoost is quite on par with SVM. Individual risk factors predictive of non-adherence included tuberculosis type, GeneXpert results, sub-country, antiretroviral status, contacts below 5 years, health facility ownership, sputum test results at 2 months, treatment supporter, cotrimoxazole preventive therapy (CPT) dapsone status, risk group, patient age, gender, middle and upper arm circumference, referral, positive sputum test at 5 and 6 months. Therefore, machine learning techniques, specifically classification types, can identify patient factors predictive of treatment non-adherence and accurately differentiate between adherent and non-adherent patients. Thus, tuberculosis program management should consider adopting the classification machine learning techniques evaluated in this study as a screening tool for identifying and targeting suited interventions to these patients.
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Affiliation(s)
- Haron W Gichuhi
- Department of Biostatistics and Epidemiology, Makerere University School of Public Health, Kampala, Uganda
| | - Mark Magumba
- Department of Information Systems, Makerere University College of Computing, and Information Science, Kampala, Uganda
| | - Manish Kumar
- Public Health Leadership Program, Gilling's School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Roy William Mayega
- Department of Biostatistics and Epidemiology, Makerere University School of Public Health, Kampala, Uganda
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3
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Palmer M, Seddon JA, van der Zalm MM, Hesseling AC, Goussard P, Schaaf HS, Morrison J, van Ginneken B, Melendez J, Walters E, Murphy K. Optimising computer aided detection to identify intra-thoracic tuberculosis on chest x-ray in South African children. PLOS GLOBAL PUBLIC HEALTH 2023; 3:e0001799. [PMID: 37192175 PMCID: PMC10187911 DOI: 10.1371/journal.pgph.0001799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Accepted: 04/10/2023] [Indexed: 05/18/2023]
Abstract
Diagnostic tools for paediatric tuberculosis remain limited, with heavy reliance on clinical algorithms which include chest x-ray. Computer aided detection (CAD) for tuberculosis on chest x-ray has shown promise in adults. We aimed to measure and optimise the performance of an adult CAD system, CAD4TB, to identify tuberculosis on chest x-rays from children with presumptive tuberculosis. Chest x-rays from 620 children <13 years enrolled in a prospective observational diagnostic study in South Africa, were evaluated. All chest x-rays were read by a panel of expert readers who attributed each with a radiological reference of either 'tuberculosis' or 'not tuberculosis'. Of the 525 chest x-rays included in this analysis, 80 (40 with a reference of 'tuberculosis' and 40 with 'not tuberculosis') were allocated to an independent test set. The remainder made up the training set. The performance of CAD4TB to identify 'tuberculosis' versus 'not tuberculosis' on chest x-ray against the radiological reference read was calculated. The CAD4TB software was then fine-tuned using the paediatric training set. We compared the performance of the fine-tuned model to the original model. Our findings were that the area under the receiver operating characteristic curve (AUC) of the original CAD4TB model, prior to fine-tuning, was 0.58. After fine-tuning there was an improvement in the AUC to 0.72 (p = 0.0016). In this first-ever description of the use of CAD to identify tuberculosis on chest x-ray in children, we demonstrate a significant improvement in the performance of CAD4TB after fine-tuning with a set of well-characterised paediatric chest x-rays. CAD has the potential to be a useful additional diagnostic tool for paediatric tuberculosis. We recommend replicating the methods we describe using a larger chest x-ray dataset from a more diverse population and evaluating the potential role of CAD to replace a human-read chest x-ray within treatment-decision algorithms for paediatric tuberculosis.
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Affiliation(s)
- Megan Palmer
- Faculty of Medicine and Health Sciences, Department of Paediatrics and Child Health, Demond Tutu TB Centre, Stellenbosch University, Cape Town, South Africa
| | - James A. Seddon
- Faculty of Medicine and Health Sciences, Department of Paediatrics and Child Health, Demond Tutu TB Centre, Stellenbosch University, Cape Town, South Africa
- Department of Infectious Disease, Imperial College London, London, United Kingdom
| | - Marieke M. van der Zalm
- Faculty of Medicine and Health Sciences, Department of Paediatrics and Child Health, Demond Tutu TB Centre, Stellenbosch University, Cape Town, South Africa
| | - Anneke C. Hesseling
- Faculty of Medicine and Health Sciences, Department of Paediatrics and Child Health, Demond Tutu TB Centre, Stellenbosch University, Cape Town, South Africa
| | - Pierre Goussard
- Faculty of Medicine and Health Sciences, Department of Paediatrics and Child Health, Stellenbosch University, Stellenbosch, South Africa
| | - H. Simon Schaaf
- Faculty of Medicine and Health Sciences, Department of Paediatrics and Child Health, Demond Tutu TB Centre, Stellenbosch University, Cape Town, South Africa
| | - Julie Morrison
- Faculty of Medicine and Health Sciences, Department of Paediatrics and Child Health, Stellenbosch University, Stellenbosch, South Africa
| | | | - Jaime Melendez
- Radboud University Medical Center, Nijmegen, The Netherlands
- Delft Imaging, ‘s-Hertogenbosch, The Netherlands
| | - Elisabetta Walters
- Faculty of Medicine and Health Sciences, Department of Paediatrics and Child Health, Demond Tutu TB Centre, Stellenbosch University, Cape Town, South Africa
- Newcastle-upon-Tyne NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Keelin Murphy
- Radboud University Medical Center, Nijmegen, The Netherlands
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Rixe N, Frisch A, Wang Z, Martin JM, Suresh S, Florin TA, Ramgopal S. The development of a novel natural language processing tool to identify pediatric chest radiograph reports with pneumonia. Front Digit Health 2023; 5:1104604. [PMID: 36910570 PMCID: PMC9992200 DOI: 10.3389/fdgth.2023.1104604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 01/16/2023] [Indexed: 02/25/2023] Open
Abstract
Objective Chest 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 methods We 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. Results Of 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%). Conclusion NLP 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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Affiliation(s)
- Nancy Rixe
- Division of Pediatric Emergency Medicine, UPMC Children's Hospital of Pittsburgh, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States
| | - Adam Frisch
- Department of Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States
| | - Zhendong Wang
- School of Computing and Information, University of Pittsburgh, Pittsburgh, PA, United States
| | - Judith M Martin
- Division of General Academic Pediatrics, UPMC Children's Hospital of Pittsburgh, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States
| | - Srinivasan Suresh
- Division of Pediatric Emergency Medicine, UPMC Children's Hospital of Pittsburgh, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States.,Division of Health Informatics, UPMC Children's Hospital of Pittsburgh, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States
| | - Todd A Florin
- Division of Emergency Medicine, Department of Pediatrics, Ann & Robert H. Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
| | - Sriram Ramgopal
- Division of Emergency Medicine, Department of Pediatrics, Ann & Robert H. Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
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5
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Ramgopal S, Sanchez-Pinto LN, Horvat CM, Carroll MS, Luo Y, Florin TA. Artificial intelligence-based clinical decision support in pediatrics. Pediatr Res 2023; 93:334-341. [PMID: 35906317 PMCID: PMC9668209 DOI: 10.1038/s41390-022-02226-1] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 06/29/2022] [Accepted: 07/18/2022] [Indexed: 11/24/2022]
Abstract
Machine learning models may be integrated into clinical decision support (CDS) systems to identify children at risk of specific diagnoses or clinical deterioration to provide evidence-based recommendations. This use of artificial intelligence models in clinical decision support (AI-CDS) may have several advantages over traditional "rule-based" CDS models in pediatric care through increased model accuracy, with fewer false alerts and missed patients. AI-CDS tools must be appropriately developed, provide insight into the rationale behind decisions, be seamlessly integrated into care pathways, be intuitive to use, answer clinically relevant questions, respect the content expertise of the healthcare provider, and be scientifically sound. While numerous machine learning models have been reported in pediatric care, their integration into AI-CDS remains incompletely realized to date. Important challenges in the application of AI models in pediatric care include the relatively lower rates of clinically significant outcomes compared to adults, and the lack of sufficiently large datasets available necessary for the development of machine learning models. In this review article, we summarize key concepts related to AI-CDS, its current application to pediatric care, and its potential benefits and risks. IMPACT: The performance of clinical decision support may be enhanced by the utilization of machine learning-based algorithms to improve the predictive performance of underlying models. Artificial intelligence-based clinical decision support (AI-CDS) uses models that are experientially improved through training and are particularly well suited toward high-dimensional data. The application of AI-CDS toward pediatric care remains limited currently but represents an important area of future research.
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Affiliation(s)
- Sriram Ramgopal
- Division of Emergency Medicine, Ann & Robert H. Lurie Children's Hospital of Chicago, Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
| | - L. Nelson Sanchez-Pinto
- grid.16753.360000 0001 2299 3507Division of Critical Care Medicine, Ann & Robert H. Lurie Children’s Hospital of Chicago, Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL USA ,grid.16753.360000 0001 2299 3507Department of Preventive Medicine (Health and Biomedical Informatics), Feinberg School of Medicine, Northwestern University, Chicago, IL USA
| | - Christopher M. Horvat
- grid.21925.3d0000 0004 1936 9000Department of Critical Care Medicine, UPMC Children’s Hospital of Pittsburgh, University of Pittsburgh School of Medicine, Pittsburgh, PA USA
| | - Michael S. Carroll
- grid.16753.360000 0001 2299 3507Data Analytics and Reporting, Ann & Robert H. Lurie Children’s Hospital of Chicago, Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL USA
| | - Yuan Luo
- grid.16753.360000 0001 2299 3507Department of Preventive Medicine (Health and Biomedical Informatics), Feinberg School of Medicine, Northwestern University, Chicago, IL USA
| | - Todd A. Florin
- grid.16753.360000 0001 2299 3507Division of Emergency Medicine, Ann & Robert H. Lurie Children’s Hospital of Chicago, Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL USA
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Ramgopal S, Heffernan ME, Bendelow A, Davis MM, Carroll MS, Florin TA, Alpern ER, Macy ML. Parental Perceptions on Use of Artificial Intelligence in Pediatric Acute Care. Acad Pediatr 2023; 23:140-147. [PMID: 35577283 DOI: 10.1016/j.acap.2022.05.006] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 04/26/2022] [Accepted: 05/07/2022] [Indexed: 02/09/2023]
Abstract
BACKGROUND Family engagement is critical in the implementation of artificial intelligence (AI)-based clinical decision support tools, which will play an increasing role in health care in the future. We sought to understand parental perceptions of computer-assisted health care of children in the emergency department (ED). METHODS We conducted a population-weighted household panel survey of parents with minor children in their home in a large US city to evaluate perceptions of the use of computer programs for the care of children with respiratory illness. We identified demographics associated with discomfort with AI using survey-weighted logistic regression. RESULTS Surveys were completed by 1620 parents (panel response rate = 49.7%). Most respondents were comfortable with the use of computer programs to determine the need for antibiotics (77.6%) or bloodwork (76.5%), and to interpret radiographs (77.5%). In multivariable analysis, Black non-Hispanic parents reported greater discomfort with AI relative to White non-Hispanic parents (odds ratio [OR] 1.67, 95% confidence interval [CI] 1.03-2.70) as did younger parents (18-25 years) relative to parents ≥46 years (OR 2.48, 95% CI 1.31-4.67). The greatest perceived benefits of computer programs were finding something a human would miss (64.2%, 95% CI 60.9%-67.4%) and obtaining a more rapid diagnosis (59.6%; 56.2%-62.9%). Areas of greatest concern were diagnostic errors (63.0%, 95% CI 59.6%-66.4%), and recommending incorrect treatment (58.9%, 95% CI 55.5%-62.3%). CONCLUSIONS Parents were generally receptive to computer-assisted management of children with respiratory illnesses in the ED, though reservations emerged. Black non-Hispanic and younger parents were more likely to express discomfort about AI.
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Affiliation(s)
- Sriram Ramgopal
- Division of Emergency Medicine, Department of Pediatrics, Ann & Robert H. Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine (S Ramgopal, TA Florin, ER Alpern, and ML Macy), Chicago, Ill.
| | - Marie E Heffernan
- Mary Ann & J. Milburn Smith Child Health Outcomes, Research, and Evaluation Center, Stanley Manne Children's Research Institute, Ann & Robert H. Lurie Children's Hospital of Chicago (ME Heffernan, MM Davis, M Carroll, and ML Macy), Chicago, Ill; Department of Pediatrics, Ann & Robert H. Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine (ME Heffernan and MM Davis), Chicago, Ill
| | - Anne Bendelow
- Data Analytics and Reporting, Ann & Robert H. Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine (A Bendelow and M Carroll), Chicago, Ill
| | - Matthew M Davis
- Mary Ann & J. Milburn Smith Child Health Outcomes, Research, and Evaluation Center, Stanley Manne Children's Research Institute, Ann & Robert H. Lurie Children's Hospital of Chicago (ME Heffernan, MM Davis, M Carroll, and ML Macy), Chicago, Ill; Department of Pediatrics, Ann & Robert H. Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine (ME Heffernan and MM Davis), Chicago, Ill
| | - Michael S Carroll
- Mary Ann & J. Milburn Smith Child Health Outcomes, Research, and Evaluation Center, Stanley Manne Children's Research Institute, Ann & Robert H. Lurie Children's Hospital of Chicago (ME Heffernan, MM Davis, M Carroll, and ML Macy), Chicago, Ill; Data Analytics and Reporting, Ann & Robert H. Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine (A Bendelow and M Carroll), Chicago, Ill
| | - Todd A Florin
- Division of Emergency Medicine, Department of Pediatrics, Ann & Robert H. Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine (S Ramgopal, TA Florin, ER Alpern, and ML Macy), Chicago, Ill
| | - Elizabeth R Alpern
- Division of Emergency Medicine, Department of Pediatrics, Ann & Robert H. Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine (S Ramgopal, TA Florin, ER Alpern, and ML Macy), Chicago, Ill
| | - Michelle L Macy
- Division of Emergency Medicine, Department of Pediatrics, Ann & Robert H. Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine (S Ramgopal, TA Florin, ER Alpern, and ML Macy), Chicago, Ill; Mary Ann & J. Milburn Smith Child Health Outcomes, Research, and Evaluation Center, Stanley Manne Children's Research Institute, Ann & Robert H. Lurie Children's Hospital of Chicago (ME Heffernan, MM Davis, M Carroll, and ML Macy), Chicago, Ill
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Padash S, Mohebbian MR, Adams SJ, Henderson RDE, Babyn P. Pediatric chest radiograph interpretation: how far has artificial intelligence come? A systematic literature review. Pediatr Radiol 2022; 52:1568-1580. [PMID: 35460035 PMCID: PMC9033522 DOI: 10.1007/s00247-022-05368-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 02/28/2022] [Accepted: 03/24/2022] [Indexed: 10/24/2022]
Abstract
Most artificial intelligence (AI) studies have focused primarily on adult imaging, with less attention to the unique aspects of pediatric imaging. The objectives of this study were to (1) identify all publicly available pediatric datasets and determine their potential utility and limitations for pediatric AI studies and (2) systematically review the literature to assess the current state of AI in pediatric chest radiograph interpretation. We searched PubMed, Web of Science and Embase to retrieve all studies from 1990 to 2021 that assessed AI for pediatric chest radiograph interpretation and abstracted the datasets used to train and test AI algorithms, approaches and performance metrics. Of 29 publicly available chest radiograph datasets, 2 datasets included solely pediatric chest radiographs, and 7 datasets included pediatric and adult patients. We identified 55 articles that implemented an AI model to interpret pediatric chest radiographs or pediatric and adult chest radiographs. Classification of chest radiographs as pneumonia was the most common application of AI, evaluated in 65% of the studies. Although many studies report high diagnostic accuracy, most algorithms were not validated on external datasets. Most AI studies for pediatric chest radiograph interpretation have focused on a limited number of diseases, and progress is hindered by a lack of large-scale pediatric chest radiograph datasets.
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Affiliation(s)
- Sirwa Padash
- Department of Medical Imaging, University of Saskatchewan, 103 Hospital Drive, Saskatoon, Saskatchewan, S7N 0W8, Canada. .,Department of Radiology, Mayo Clinic, Rochester, MN, USA.
| | - Mohammad Reza Mohebbian
- grid.25152.310000 0001 2154 235XDepartment of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, Saskatchewan Canada
| | - Scott J. Adams
- grid.25152.310000 0001 2154 235XDepartment of Medical Imaging, University of Saskatchewan, 103 Hospital Drive, Saskatoon, Saskatchewan S7N 0W8 Canada
| | - Robert D. E. Henderson
- grid.25152.310000 0001 2154 235XDepartment of Medical Imaging, University of Saskatchewan, 103 Hospital Drive, Saskatoon, Saskatchewan S7N 0W8 Canada
| | - Paul Babyn
- grid.25152.310000 0001 2154 235XDepartment of Medical Imaging, University of Saskatchewan, 103 Hospital Drive, Saskatoon, Saskatchewan S7N 0W8 Canada
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Korppi M. No need to take chest radiographs in pediatric community-acquired pneumonia if complications are not suspected. Pediatr Pulmonol 2022; 57:315-316. [PMID: 34583426 DOI: 10.1002/ppul.25696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 09/02/2021] [Accepted: 09/21/2021] [Indexed: 11/10/2022]
Affiliation(s)
- Matti Korppi
- Center for Child Health Research, University of Tampere and University Hospital, Tampere, Finland
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