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

Abstract

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.

Comparative Study of Eclipse and RayStation Multi-Criteria Optimization-Based Prostate Radiotherapy Treatment Planning Quality

“…Use of artificial intelligence (AI) such as machine learning (ML) and deep learning has become popular in medical imaging [ 7 , 9 , 43 , 44 , 45 ]. Apart from the MCO, knowledge-based planning function based on ML technology (known as RapidPlan) has also become available in recent versions of Eclipse [ 46 ].…”

Generative Adversarial Network (Generative Artificial Intelligence) in Pediatric Radiology: A Systematic Review

“…Additionally, the number of AI research articles in radiology has grown exponentially over recent years [ 1 , 2 ]. Various commercial AI products have been available for applications in clinical practice such as radiological examination dose optimization [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 ], computer-aided detection and diagnosis (CAD) [ 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 ], and medical image segmentation [ 49 , 50 , 51 , 52 , 53 ]. Predominantly, these applications in radiology are based on deductive AI techniques [ 1 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ...…”

“…Pediatric radiology is a subset of radiology [ 26 , 28 , 29 , 66 , 67 ]. The aforementioned review findings may not be applicable to pediatric radiology [ 28 , 29 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 67 ].…”

“…Pediatric radiology is a subset of radiology [ 26 , 28 , 29 , 66 , 67 ]. The aforementioned review findings may not be applicable to pediatric radiology [ 28 , 29 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 67 ]. For example, the application of GAN for prostate cancer segmentation appears not relevant to children [ 60 , 68 ].…”

“…For example, the application of GAN for prostate cancer segmentation appears not relevant to children [ 60 , 68 ]. Although several literature reviews about AI in pediatric radiology have been published, none of them focused on the GAN [ 26 , 28 , 29 , 67 ]. Given that the GAN is an important topic area in radiology and the recent literature reviews focused on its applications in this discipline, it is timely to conduct a systematic review of its applications in pediatric radiology [ 29 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 ].…”