External validation of an artificial intelligence solution for the detection of elbow fractures and joint effusions in children - 07/03/24
, Raphaël Veil b, 1
Bienvenido a EM-consulte, la referencia de los profesionales de la salud.
Artículo gratuito.
Conéctese para beneficiarse!
Highlights |
• | An external validation of an artificial intelligence solution was performed in a real-life context to estimate its performances in the diagnosis of elbow fracture and/or joint effusion. |
• | The senior radiologist's conclusion, blinded to the artificial intelligence solution conclusion, was used as the standard of reference. |
• | The artificial intelligence solution demonstrates a negative predictive value of 92% in the diagnosis of elbow fracture and/or joint effusion in children. |
Abstract |
Purpose |
The purpose of this study was to conduct an external validation of an artificial intelligence (AI) solution for the detection of elbow fractures and joint effusions using radiographs from a real-life cohort of children.
Materials and methods |
This single-center retrospective study was conducted on 758 radiographic sets (1637 images) obtained from consecutive emergency room visits of 712 children (mean age, 7.27 ± 3.97 [standard deviation] years; age range, 7 months and 10 days to 15 years and 10 months), referred for a trauma of the elbow. For each set, fracture and/or effusion detection by eleven senior radiologists (reference standard) and AI solution was recorded. Diagnostic performance of the AI solution was measured via four different approaches: fracture detection (presence/absence of fracture as binary variable), fracture enumeration, fracture localization and lesion detection (fracture and/or a joint effusion used as constructed binary variable).
Results |
The sensitivity of the AI solution for each of the four approaches was >89%. Greatest sensitivity of the AI solution was obtained for lesion detection (95.0%; 95% confidence interval: 92.1–96.9). The specificity of the AI solution ranged between 63% (for lesion detection) and 77% (for fracture detection). For all four approaches, the negative predictive values were >92% and the positive predictive values ranged between 54% (for fracture enumeration and localization) and 73% (for lesion detection). Specificity was lower for plastered children for all approaches (P < 0.001).
Conclusion |
The AI solution demonstrates high performances for detecting elbow's fracture and/or joint effusion in children. However, in our context of use, 8% of the radiographic sets ruled-out by the algorithm concerned children with a genuine traumatic elbow lesion.
El texto completo de este artículo está disponible en PDF.Keywords : Artificial intelligence, Elbow, Emergency, Pediatric fracture, Radiography
List of abbreviations : CI, FN, FP, PLR, NLR, NPV, PACS, PPV, ROI, TN, TP
Esquema
Vol 105 - N° 3
P. 104-109 - mars 2024 Regresar al número
Artículo precedente
- Detection and severity quantification of pulmonary embolism with 3D CT data using an automated deep learning-based artificial solution
- Aissam Djahnine, Carole Lazarus, Mathieu Lederlin, Sébastien Mulé, Rafael Wiemker, Salim Si-Mohamed, Emilien Jupin-Delevaux, Olivier Nempont, Youssef Skandarani, Mathieu De Craene, Segbedji Goubalan, Caroline Raynaud, Younes Belkouchi, Amira Ben Afia, Clement Fabre, Gilbert Ferretti, Constance De Margerie, Pierre Berge, Renan Liberge, Nicolas Elbaz, Maxime Blain, Pierre-Yves Brillet, Guillaume Chassagnon, Farah Cadour, Caroline Caramella, Mostafa El Hajjam, Samia Boussouar, Joya Hadchiti, Xavier Fablet, Antoine Khalil, Hugues Talbot, Alain Luciani, Nathalie Lassau, Loic Boussel
- Comparison of two deep-learning image reconstruction algorithms on cardiac CT images: A phantom study
- Joël Greffier, Maxime Pastor, Salim Si-Mohamed, Cynthia Goutain-Majorel, Aude Peudon-Balas, Mourad Zoubir Bensalah, Julien Frandon, Jean-Paul Beregi, Djamel Dabli
Bienvenido a EM-consulte, la referencia de los profesionales de la salud.