Artificial intelligence in paediatric radiology: Future opportunities

Davendralingam, Natasha; Sebire, Neil J.; Arthurs, Owen J; Shelmerdine, Susan C.

doi:10.1259/bjr.20200975

Cited by 33 publications

(23 citation statements)

References 78 publications

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“…To date, ML methods have been applied in clinical research with applications in several medical fields, of which many are in pediatric diagnostic imaging [ 48 , 49 , 50 ]. In particular, the ML methodology has been applied to assess skeletal maturity on hand X-rays [ 51 ], to diagnose and classify acute appendicitis using laboratory tests and US [ 52 ], to identify MR biomarkers of the autistic spectrum [ 53 ], and to evaluate CLD using clinical data and MR [ 54 ].…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Evaluation of Biliary Atresia Patients to Predict Long-Term Outcome after the Kasai Procedure

et al. 2021

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Kasai portoenterostomy (KP) represents the first-line treatment for biliary atresia (BA). The purpose was to compare the accuracy of quantitative parameters extracted from laboratory tests, US imaging, and MR imaging studies using machine learning (ML) algorithms to predict the long-term medical outcome in native liver survivor BA patients after KP. Twenty-four patients were evaluated according to clinical and laboratory data at initial evaluation (median follow-up = 9.7 years) after KP as having ideal (n = 15) or non-ideal (n = 9) medical outcomes. Patients were re-evaluated after an additional 4 years and classified in group 1 (n = 12) as stable and group 2 (n = 12) as non-stable in the disease course. Laboratory and quantitative imaging parameters were merged to test ML algorithms. Total and direct bilirubin (TB and DB), as laboratory parameters, and US stiffness, as an imaging parameter, were the only statistically significant parameters between the groups. The best algorithm in terms of accuracy, sensitivity, specificity, and AUCROC was naive Bayes algorithm, selecting only laboratory parameters (TB and DB). This preliminary ML analysis confirms the fundamental role of TB and DB values in predicting the long-term medical outcome for BA patients after KP, even though their values may be within the normal range. Physicians should be alert when TB and DB values change slightly.

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Section: Discussionmentioning

confidence: 99%

Machine Learning Evaluation of Biliary Atresia Patients to Predict Long-Term Outcome after the Kasai Procedure

et al. 2021

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“…Ergebnisse der Bildgebung werden in AI-Anwendungen integriert, die Biomarker für bestimmte neurokognitive Erkrankungen identifizieren, um diese möglichst frühzeitig zu diagnostizieren und eine entsprechende (Früh-)Förderung bzw. Therapie zu ermöglichen [6].…”

Section: Klassifikation Und Quantifizierungunclassified

Künstliche Intelligenz bei Bildauswertung und Diagnosefindung

Mentzel

2021

Monatsschr Kinderheilkd

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“…Pediatric radiology is a subset of radiology [ 14 , 15 , 16 , 17 ]. The aforementioned systematic review findings may not be applicable to the pediatric radiology [ 10 , 12 , 13 , 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

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

2023

Children

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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.

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Artificial intelligence in paediatric radiology: Future opportunities

Cited by 33 publications

References 78 publications

Machine Learning Evaluation of Biliary Atresia Patients to Predict Long-Term Outcome after the Kasai Procedure

Machine Learning Evaluation of Biliary Atresia Patients to Predict Long-Term Outcome after the Kasai Procedure

Künstliche Intelligenz bei Bildauswertung und Diagnosefindung

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

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