RESUMEN
The frozen elephant trunk stent has greatly facilitated the repair of elective and emergency arch and proximal descending thoracic aortic aneurysms and dissections. As one of the few tertiary hospitals that routinely inserts and images the floating and frozen elephant trunk grafts, we aim to provide an up-to-date illustration of the contrasting methods of elephant trunk thoracic aortic aneurysm repair through a computed tomography (CT) review and detail the common radiological interpretation pitfalls in addition to the most significant associated complications.
Asunto(s)
Aneurisma de la Aorta Torácica , Implantación de Prótesis Vascular , Aorta Torácica , Aneurisma de la Aorta Torácica/diagnóstico por imagen , Aneurisma de la Aorta Torácica/cirugía , Prótesis Vascular , Implantación de Prótesis Vascular/métodos , Humanos , Stents , Resultado del TratamientoRESUMEN
AIM: To develop a machine learning-based model for the binary classification of chest radiography abnormalities, to serve as a retrospective tool in guiding clinician reporting prioritisation. MATERIALS AND METHODS: The open-source machine learning library, Tensorflow, was used to retrain a final layer of the deep convolutional neural network, Inception, to perform binary normality classification on two, anonymised, public image datasets. Re-training was performed on 47,644 images using commodity hardware, with validation testing on 5,505 previously unseen radiographs. Confusion matrix analysis was performed to derive diagnostic utility metrics. RESULTS: A final model accuracy of 94.6% (95% confidence interval [CI]: 94.3-94.7%) based on an unseen testing subset (n=5,505) was obtained, yielding a sensitivity of 94.6% (95% CI: 94.4-94.7%) and a specificity of 93.4% (95% CI: 87.2-96.9%) with a positive predictive value (PPV) of 99.8% (95% CI: 99.7-99.9%) and area under the curve (AUC) of 0.98 (95% CI: 0.97-0.99). CONCLUSION: This study demonstrates the application of a machine learning-based approach to classify chest radiographs as normal or abnormal. Its application to real-world datasets may be warranted in optimising clinician workload.