RESUMEN
Accurate identification of metallic orthopedic implant design is important for preoperative planning of revision arthroplasty. Surgical records of implant models are frequently unavailable. The aim of this study was to develop and evaluate a convolutional neural network for identifying orthopedic implant models using radiographs. In this retrospective study, 427 knee and 922 hip unilateral anteroposterior radiographs, including 12 implant models from 650 patients, were collated from an orthopedic center between March 2015 and November 2019 to develop classification networks. A total of 198 images paired with autogenerated image masks were used to develop a U-Net segmentation network to automatically zero-mask around the implants on the radiographs. Classification networks processing original radiographs, and two-channel conjoined original and zero-masked radiographs, were ensembled to provide a consensus prediction. Accuracies of five senior orthopedic specialists assisted by a reference radiographic gallery were compared with network accuracy using McNemar exact test. When evaluated on a balanced unseen dataset of 180 radiographs, the final network achieved a 98.9% accuracy (178 of 180) and 100% top-three accuracy (180 of 180). The network performed superiorly to all five specialists (76.1% [137 of 180] median accuracy and 85.6% [154 of 180] best accuracy; both P < .001), with robustness to scan quality variation and difficult to distinguish implants. A neural network model was developed that outperformed senior orthopedic specialists at identifying implant models on radiographs; real-world application can now be readily realized through training on a broader range of implants and joints, supported by all code and radiographs being made freely available. Supplemental material is available for this article. Keywords: Neural Networks, Skeletal-Appendicular, Knee, Hip, Computer Applications-General (Informatics), Prostheses, Technology Assess-ment, Observer Performance © RSNA, 2021.
RESUMEN
Advancements in surgical technique and understanding of the pathophysiology of mitral valve (MV) dysfunction have led to improved outcomes. Seen as a development beyond measures of morbidity and mortality, health-related quality-of-life (HRQOL) outcome measures are becoming increasingly popular. These measures are important because complications following routine (i.e. low-risk) operations on the MV are uncommon and further markers of outcome are needed. Surgeons are increasingly operating earlier on asymptomatic patients and will need to prove that HRQOL is not impacted. Novel minimally invasive and transcatheter technologies will also need to demonstrate satisfactory HRQOL outcomes prior to widespread use. This systematic review provides an overview of all available literature detailing HRQOL in patients receiving MV interventions. In the 43 studies included, 6865 patients underwent procedures ranging from open replacement to percutaneous repair using devices such as the Mitraclip Clip Delivery System (MitraClip) (Abbott Vascular, Santa Clara, CA, USA). Most studies performed baseline HRQOL assessment, allowing postinterventional comparison. While the underlying literature had deficiencies, most studies report acceptable postintervention HRQOL that was comparable to that of matched general populations. Patient-specific (e.g. female gender, renal dysfunction) and surgical-specific factors (e.g. replacement instead of repair, elevated transmitral gradient) were identified that predispose patients to poorer long-term HRQOL outcomes. These factors are important for clinicians developing strategies to maximize their HRQOL outcomes. Future randomized studies would benefit from HRQOL measurements at specific time points to allow large-scale comparisons. Establishing a common HRQOL instrument for use in MV intervention studies may support detailed comparisons between specific techniques. Physical activity monitors, physiological biomarkers and radiological markers could also be used as innovative indicators of functional outcome.