2022

DOI: 10.1504/ijica.2022.125655

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Convolutional neural networks applied in the detection of pneumonia by X-ray images

Luan Nascimento da Silva

¹

,

Leandro Araújo

²

,

Victor Ferreira

³

et al.

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“…Twenty-three articles met the selection criteria and were included in this review [ 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 ]. Table 1 shows their AI-based CAD application areas in the pediatric radiology and the diagnostic performances.…”

Section: Resultsmentioning

confidence: 99%

“…Table 1 shows their AI-based CAD application areas in the pediatric radiology and the diagnostic performances. These studies covered brain ( n = 9) [ 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 ], respiratory ( n = 9) [ 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 ], musculoskeletal ( n = 2) [ 40 , 41 ], urologic ( n = 2) [ 51 , 52 ] and cardiac imaging ( n = 1) [ 39 ]. The commonest AI-based CAD application area (30.4%, 7/23) was pediatric pneumonia [ 43 , 45 , 46 , 47 , 48 , 49 , 50 ].…”

Section: Resultsmentioning

confidence: 99%

“…These studies covered brain ( n = 9) [ 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 ], respiratory ( n = 9) [ 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 ], musculoskeletal ( n = 2) [ 40 , 41 ], urologic ( n = 2) [ 51 , 52 ] and cardiac imaging ( n = 1) [ 39 ]. The commonest AI-based CAD application area (30.4%, 7/23) was pediatric pneumonia [ 43 , 45 , 46 , 47 , 48 , 49 , 50 ]. No study reported all seven diagnostic accuracy measures [ 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , …”

Section: Resultsmentioning

confidence: 99%

“…The commonest AI-based CAD application area (30.4%, 7/23) was pediatric pneumonia [ 43 , 45 , 46 , 47 , 48 , 49 , 50 ]. No study reported all seven diagnostic accuracy measures [ 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 ]. Most commonly, the papers (30.4%, 7/23) reported four metrics [ 30 , 32 , 35 , 42 , 44 , 45 , 52 ].…”

Section: Resultsmentioning

confidence: 99%

“…Most commonly, the papers (30.4%, 7/23) reported four metrics [ 30 , 32 , 35 , 42 , 44 , 45 , 52 ]. Accuracy ( n = 19) and sensitivity ( n = 18) were the two most frequently used evaluation metrics [ 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 ]. One study only used one measure, AUC [ 40 ].…”

Section: Resultsmentioning

confidence: 99%

See 4 more Smart Citations

Diagnostic Performance of Artificial Intelligence-Based Computer-Aided Detection and Diagnosis in Pediatric Radiology: A Systematic Review

¹

2023

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Artificial intelligence (AI)-based computer-aided detection and diagnosis (CAD) is an important research area in radiology. However, only two narrative reviews about general uses of AI in pediatric radiology and AI-based CAD in pediatric chest imaging have been published yet. The purpose of this systematic review is to investigate the AI-based CAD applications in pediatric radiology, their diagnostic performances and methods for their performance evaluation. A literature search with the use of electronic databases was conducted on 11 January 2023. Twenty-three articles that met the selection criteria were included. This review shows that the AI-based CAD could be applied in pediatric brain, respiratory, musculoskeletal, urologic and cardiac imaging, and especially for pneumonia detection. Most of the studies (93.3%, 14/15; 77.8%, 14/18; 73.3%, 11/15; 80.0%, 8/10; 66.6%, 2/3; 84.2%, 16/19; 80.0%, 8/10) reported model performances of at least 0.83 (area under receiver operating characteristic curve), 0.84 (sensitivity), 0.80 (specificity), 0.89 (positive predictive value), 0.63 (negative predictive value), 0.87 (accuracy), and 0.82 (F1 score), respectively. However, a range of methodological weaknesses (especially a lack of model external validation) are found in the included studies. In the future, more AI-based CAD studies in pediatric radiology with robust methodology should be conducted for convincing clinical centers to adopt CAD and realizing its benefits in a wider context.

“…Twenty-three articles met the selection criteria and were included in this review [ 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 ]. Table 1 shows their AI-based CAD application areas in the pediatric radiology and the diagnostic performances.…”

Section: Resultsmentioning

confidence: 99%

“…Table 1 shows their AI-based CAD application areas in the pediatric radiology and the diagnostic performances. These studies covered brain ( n = 9) [ 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 ], respiratory ( n = 9) [ 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 ], musculoskeletal ( n = 2) [ 40 , 41 ], urologic ( n = 2) [ 51 , 52 ] and cardiac imaging ( n = 1) [ 39 ]. The commonest AI-based CAD application area (30.4%, 7/23) was pediatric pneumonia [ 43 , 45 , 46 , 47 , 48 , 49 , 50 ].…”

Section: Resultsmentioning

confidence: 99%

“…These studies covered brain ( n = 9) [ 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 ], respiratory ( n = 9) [ 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 ], musculoskeletal ( n = 2) [ 40 , 41 ], urologic ( n = 2) [ 51 , 52 ] and cardiac imaging ( n = 1) [ 39 ]. The commonest AI-based CAD application area (30.4%, 7/23) was pediatric pneumonia [ 43 , 45 , 46 , 47 , 48 , 49 , 50 ]. No study reported all seven diagnostic accuracy measures [ 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , …”

Section: Resultsmentioning

confidence: 99%

“…The commonest AI-based CAD application area (30.4%, 7/23) was pediatric pneumonia [ 43 , 45 , 46 , 47 , 48 , 49 , 50 ]. No study reported all seven diagnostic accuracy measures [ 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 ]. Most commonly, the papers (30.4%, 7/23) reported four metrics [ 30 , 32 , 35 , 42 , 44 , 45 , 52 ].…”

Section: Resultsmentioning

confidence: 99%

“…Most commonly, the papers (30.4%, 7/23) reported four metrics [ 30 , 32 , 35 , 42 , 44 , 45 , 52 ]. Accuracy ( n = 19) and sensitivity ( n = 18) were the two most frequently used evaluation metrics [ 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 ]. One study only used one measure, AUC [ 40 ].…”

Section: Resultsmentioning

confidence: 99%

See 3 more Smart Citations

Diagnostic Performance of Artificial Intelligence-Based Computer-Aided Detection and Diagnosis in Pediatric Radiology: A Systematic Review

¹

2023

View full text Add to dashboard Cite

Artificial intelligence (AI)-based computer-aided detection and diagnosis (CAD) is an important research area in radiology. However, only two narrative reviews about general uses of AI in pediatric radiology and AI-based CAD in pediatric chest imaging have been published yet. The purpose of this systematic review is to investigate the AI-based CAD applications in pediatric radiology, their diagnostic performances and methods for their performance evaluation. A literature search with the use of electronic databases was conducted on 11 January 2023. Twenty-three articles that met the selection criteria were included. This review shows that the AI-based CAD could be applied in pediatric brain, respiratory, musculoskeletal, urologic and cardiac imaging, and especially for pneumonia detection. Most of the studies (93.3%, 14/15; 77.8%, 14/18; 73.3%, 11/15; 80.0%, 8/10; 66.6%, 2/3; 84.2%, 16/19; 80.0%, 8/10) reported model performances of at least 0.83 (area under receiver operating characteristic curve), 0.84 (sensitivity), 0.80 (specificity), 0.89 (positive predictive value), 0.63 (negative predictive value), 0.87 (accuracy), and 0.82 (F1 score), respectively. However, a range of methodological weaknesses (especially a lack of model external validation) are found in the included studies. In the future, more AI-based CAD studies in pediatric radiology with robust methodology should be conducted for convincing clinical centers to adopt CAD and realizing its benefits in a wider context.

Applying Artificial Intelligence to Pediatric Chest Imaging: Reliability of Leveraging Adult-Based Artificial Intelligence Models

¹

,

²

,

³

2023

Journal of the American College of Radiology

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