Diagnostic Performance of Artificial Intelligence-Based Computer-Aided Detection and Diagnosis in Pediatric Radiology: A Systematic Review
Abstract
:1. Introduction
2. Materials and Methods
2.1. Literature Search
2.2. Article Selection
2.3. Data Extraction and Synthesis
3. Results
Author, Year and Country | Modality | Diagnosis | Diagnostic Performance | ||||||
---|---|---|---|---|---|---|---|---|---|
AUC | Sensitivity | Specificity | PPV | NPV | Accuracy | F1 Score | |||
Brain Imaging | |||||||||
Dou et al. (2022)—China [30] | MRI | Bipolar disorder | 0.830 | 0.909 | 0.769 | NR | NR | 0.854 | NR |
Kuttala et al. (2022)—Australia, India & United Arab Emirates [31] | MRI | ADHD and ASD | 0.850 (ADHA); 0.910 (ASD) | NR | NR | NR | NR | 0.854 (ADHA); 0.978 (ASD) | NR |
Li et al. (2020)—China [32] | MRI | Posterior fossa tumors | 0.865 | 0.929 | 0.800 | NR | NR | 0.878 | NR |
Peruzzo et al. (2016)—Italy [33] | MRI | Malformations of corpus callosum | 0.953 | 0.923 | 0.904 | 0.906 | NR | 0.914 | NR |
Prince et al. (2020)—USA [34] | CT & MRI | ACP | 0.978 | NR | NR | NR | NR | 0.979 | NR |
Tan et al. (2013)—USA [35] | MRI | Congenital sensori-neural hearing loss | 0.900 | 0.890 | 0.860 | NR | NR | 0.870 | NR |
Xiao et al. (2019)—China [36] | MRI | ASD | NR | 0.980 | 0.936 | 0.959 | 0.971 | 0.963 | NR |
Zahia et al. (2020)—Spain [37] | MRI | Dyslexia | NR | 0.750 | 0.714 | 0.600 | NR | 0.727 | 0.670 |
Zhou et al. (2021)—China [38] | MRI | ADHD | 0.698 | 0.609 | 0.676 | NR | NR | 0.643 | 0.626 |
Cardiac Imaging | |||||||||
Lee et al. (2022)—South Korea [39] | US | Kawasaki disease | NR | 0.841 | 0.585 | 0.811 | 0.633 | 0.759 | 0.826 |
Musculoskeletal Imaging | |||||||||
Petibon et al. (2021)—Canada, Israel and USA [40] | SPECT | Low back pain | 0.830 | NR | NR | NR | NR | NR | NR |
Sezer and Sezer (2020)—France and Turkey [41] | US | DDH | NR | 0.962 | 0.980 | NR | NR | 0.977 | NR |
Respiratory Imaging | |||||||||
Behzadi—Khormouji et al. (2020)—Iran and USA [42] | X-ray | Pulmonary consolidation | 0.995 | 0.987 | 0.864 | NR | NR | 0.945 | NR |
Bodapati and Rohith (2022)—India [43] | X-ray | Pneumonia | 0.939 | NR | NR | NR | NR | 0.948 | 0.959 |
Helm et al. (2009)—Canada, UK and USA [44] | CT | Pulmonary nodules | NR | 0.420 | 1.000 | 1.000 | 0.260 | NR | NR |
Jiang and Chen (2022)-China [45] | X-ray | Pneumonia | NR | 0.894 | NR | 0.918 | NR | 0.912 | 0.903 |
Liang and Zheng (2020)-China [46] | X-ray | Pneumonia | 0.953 | 0.967 | NR | 0.891 | NR | 0.905 | 0.927 |
Mahomed et al. (2020)-Netherlands and South Africa [47] | X-ray | Primary-endpoint pneumonia | 0.850 | 0.760 | 0.800 | NR | NR | NR | NR |
Shouman et al. (2022)-Egypt and Saudi Arabia [48] | X-ray | Bacterial and viral pneumonia | 0.999 | 0.987 | 0.987 | 0.979 | NR | 0.986 | 0.983 |
Silva et al. (2022)-Brazil [49] | X-ray | Pneumonia | NR | 0.945 | NR | 0.957 | NR | NR | 0.951 |
Vrbančič and Podgorelec (2022)-Slovenia [50] | X-ray | Pneumonia | 0.952 | 0.976 | 0.927 | 0.973 | NR | 0.963 | 0.974 |
Urologic Imaging | |||||||||
Guan et al. (2022)-China [51] | US | Hydronephrosis | NR | NR | NR | NR | NR | 0.891 | 0.895 |
Zheng et al. (2019)-China and USA [52] | US | CAKUT | 0.920 | 0.86 | 0.880 | NR | NR | 0.870 | NR |
Author, Year and Country | Modality | Diagnosis | AI Type and Model | Study Design | Dataset Source | Test Set Size | Patient/Population | Sample Size Calculation | Internal Validation Type | External Validation | Reference Standard | AI vs. Clinician | Commercial Availability |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Brain Imaging | |||||||||||||
Dou et al. (2022)—China [30] | MRI | Bipolar disorder | ML-LR | Prospective | Private dataset by Second Xiangya Hospital, China | 52 scans | 12–18-year-old children | No | 2-fold cross-validation | No | Clinical diagnosis | No | No |
Kuttala et al. (2022)—Australia, India and United Arab Emirates [31] | MRI | ADHD and ASD | DL-GAN and softmax | Retrospective | Public datasets (ADHD-200 and Autism Brain Imaging Data Exchange II) | 217 scans | Children (median ages for baseline and follow-up scans: 12 and 15 years, respectively) | No | NR | No | NR | No | No |
Li et al. (2020)—China [32] | MRI | Posterior fossa tumors | ML-SVM | Prospective | Private dataset by Affiliated Hospital of Zhengzhou University, China | 45 scans | 0–14-year-old children | No | Repeated hold-out with 70:30 random split | No | Histology | No | No |
Peruzzo et al. (2016)—Italy [33] | MRI | Malformations of corpus callosum | ML-SVM | Retrospective | Private dataset by Scientific Institute “Eugenio Medea”, Italy | 104 scans | 2–12-year-old children | No | Leave-one-out cross validation | No | Expert consensus | Yes | No |
Prince et al. (2020)—USA [34] | CT and MRI | ACP | DL-CNN | Retrospective | Public dataset (ATPC Consortium) and private datasets by Children’s Hospital Colorado and St. Jude Children’s Research Hospital, USA | 86 CT-MRI scans | Children | No | 60:40 random split and 5-fold cross validation | No | Histology | Yes | No |
Tan et al. (2013)—USA [35] | MRI | Congenital sensori-neural hearing loss | ML-SVM | Prospective | Private dataset by Cincinnati Children’s Hospital Medical Center, USA | 39 scans | 8–24-month-old children | No | Leave-one-out cross-validation | No | Follow-up | No | No |
Xiao et al. (2019)—China [36] | MRI | ASD | DL-SAE and softmax | Retrospective | Public dataset (Autism Brain Imaging Data Exchange II) | 198 scans | 5–12-year-old children | No | 11-, 33-, 66-, 99- and 198-fold cross-validation | No | Clinical diagnosis | No | No |
Zahia et al. (2020)—Spain [37] | MRI | Dyslexia | DL-CNN | Prospective | Private dataset by University Hospital of Cruces, Spain | 55 scans | 9–12-year-old children | No | 4-fold cross validation | No | Clinical diagnosis | No | No |
Zhou et al. (2021)—China [38] | MRI | ADHD | ML-SVM | Retrospective | Public dataset (Adolescent Brain Cognitive Development Data Repository) | 232 scans | 9–10-year-old children | No | 10-fold cross-validation | No | Clinical diagnosis | No | No |
Cardiac Imaging | |||||||||||||
Lee et al. (2022)—South Korea [39] | US | Kawasaki disease | DL-CNN | Retrospective | Private dataset by Yonsei University Gangnam Severance Hospital, South Korea | 203 scans | Children | No | 10-fold cross-validation | No | Single expert reader | No | No |
Musculoskeletal Imaging | |||||||||||||
Petibon et al. (2021)—Canada, Israel and USA [40] | SPECT | Low back pain | DL-CNN | Retrospective | Private dataset by Boston Children’s Hospital, USA | 65 scans | 10–17 years old children | No | 3-fold cross-validation | No | Other-ground truth established by artificial lesion insertion | Yes | No |
Sezer and Sezer (2020)—France and Turkey [41] | US | DDH | DL-CNN | Prospective | Private dataset | 203 scans | 0–6-month-old children | No | 70:30 random split | No | Single expert reader | No | No |
Respiratory Imaging | |||||||||||||
Behzadi—Khormouji et al. (2020)—Iran and USA [42] | X-ray | Pulmonary consolidation | DL-CNN | Retrospective | Public dataset by Guangzhou Women and Children’s Medical Center, China | 582 images | 1–5-year-old children | No | 90:10 random split | No | Expert consensus | No | No |
Bodapati and Rohith (2022)—India [43] | X-ray | Pneumonia | DL-CNN and CapsNet | Retrospective | Public dataset by Guangzhou Women and Children’s Medical Center, China | 640 images | 1–5-year-old children | No | NR | No | NR | No | No |
Helm et al. (2009)—Canada, UK and USA [44] | CT | Pulmonary nodules | NR | Retrospective | Private dataset by a tertiary pediatric hospital | 29 scans | 3 years and 11 months to 18-year-old children | No | NR | Yes | Expert and reader consensus | Yes | Yes |
Jiang and Chen (2022)—China [45] | X-ray | Pneumonia | DL-ViT | Retrospective | Public dataset by Guangzhou Women and Children’s Medical Center, China | 624 images | 1–5-year-old children | No | NR | No | NR | No | No |
Liang and Zheng (2020)—China [46] | X-ray | Pneumonia | DL-CNN | Retrospective | Public dataset by Guangzhou Women and Children’s Medical Center, China | 624 images | 1–5-year-old children | No | 90:10 random split | No | NR | No | No |
Mahomed et al. (2020)—Netherlands and South Africa [47] | X-ray | Primary-endpoint pneumonia | ML-SVM | Prospective | Private dataset by Chris Hani Baragwanath Academic Hospital, South Africa | 858 digitized images | 1–59-month-old children | No | 10-fold cross-validation | No | Reader consensus | Yes | No |
Shouman et al. (2022)—Egypt and Saudi Arabia [48] | X-ray | Bacterial and viral pneumonia | DL-CNN and LSTM | Retrospective | Public dataset by Guangzhou Women and Children’s Medical Center, China | 586 images | 1–5-year-old children | No | 90:10 random split | No | NR | No | No |
Silva et al. (2022)—Brazil [49] | X-ray | Pneumonia | DL-CNN | Retrospective | Public dataset by Guangzhou Women and Children’s Medical Center, China | 1172 images | 1–5-year-old children | No | NR | No | NR | No | No |
Vrbančič and Podgorelec (2022)—Slovenia [50] | X-ray | Pneumonia | DL-CNN and SGD | Retrospective | Public dataset by Guangzhou Women and Children’s Medical Center, China | 5858 images | 1–5-year-old children | No | 10-fold cross-validation | No | Expert readers | No | No |
Urologic Imaging | |||||||||||||
Guan et al. (2022)—China [51] | US | Hydronephrosis | DL-CNN | Prospective | Private dataset by Beijing Children’s Hospital, China | 3257 images | Children | No | 10-fold cross-validation | No | Readers and experts without consensus | No | No |
Zheng et al. (2019)—China and USA [52] | US | CAKUT | DL-SVM | Retrospective | Private dataset by Children’s Hospital of Philadelphia, USA | 100 scans | Children with mean age of 111 days (SD: 262) | No | 10-fold cross-validation | No | Clinical diagnosis | No | No |
4. Discussion
5. Conclusions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Ng, C.K.C. Diagnostic Performance of Artificial Intelligence-Based Computer-Aided Detection and Diagnosis in Pediatric Radiology: A Systematic Review. Children 2023, 10, 525. https://doi.org/10.3390/children10030525
Ng CKC. Diagnostic Performance of Artificial Intelligence-Based Computer-Aided Detection and Diagnosis in Pediatric Radiology: A Systematic Review. Children. 2023; 10(3):525. https://doi.org/10.3390/children10030525
Chicago/Turabian StyleNg, Curtise K. C. 2023. "Diagnostic Performance of Artificial Intelligence-Based Computer-Aided Detection and Diagnosis in Pediatric Radiology: A Systematic Review" Children 10, no. 3: 525. https://doi.org/10.3390/children10030525