PDF(Pubmed)
Abstract：
UNASSIGNED: Demand for total shoulder arthroplasty (TSA) has risen significantly and is projected to continue growing. From 2012 to 2017, the incidence of reverse total shoulder arthroplasty (rTSA) rose from 7.3 cases per 100,000 to 19.3 per 100,000. Anatomical TSA saw a growth from 9.5 cases per 100,000 to 12.5 per 100,000. Failure to identify implants in a timely manner can increase operative time, cost and risk of complications. Several machine learning models have been developed to perform medical image analysis. However, they have not been widely applied in shoulder surgery. The authors developed a machine learning model to identify shoulder implant manufacturers and type from anterior-posterior X-ray images.
UNASSIGNED: The model deployed was a convolutional neural network (CNN), which has been widely used in computer vision tasks. 696 radiographs were obtained from a single institution. 70% were used to train the model, while evaluation was done on 30%.
UNASSIGNED: On the evaluation set, the model performed with an overall accuracy of 93.9% with positive predictive value, sensitivity and F-1 scores of 94% across 10 different implant types (4 reverse, 6 anatomical). Average identification time was 0.110 s per implant.
UNASSIGNED: This proof of concept study demonstrates that machine learning can assist with preoperative planning and improve cost-efficiency in shoulder surgery.
UNASSIGNED: The model deployed was a convolutional neural network (CNN), which has been widely used in computer vision tasks. 696 radiographs were obtained from a single institution. 70% were used to train the model, while evaluation was done on 30%.
UNASSIGNED: On the evaluation set, the model performed with an overall accuracy of 93.9% with positive predictive value, sensitivity and F-1 scores of 94% across 10 different implant types (4 reverse, 6 anatomical). Average identification time was 0.110 s per implant.
UNASSIGNED: This proof of concept study demonstrates that machine learning can assist with preoperative planning and improve cost-efficiency in shoulder surgery.
摘要：
未经评估：全肩关节置换术（TSA）的需求已显著上升，预计将继续增长。从2012年到2017年，反向全肩关节置换术（rTSA）的发生率从每100,000例中的7.3例上升到每100,000例中的19.3例。解剖学TSA从每100,000中的9.5例增加到每100,000中的12.5例。未能及时识别植入物会增加手术时间，成本和并发症的风险。已经开发了几种机器学习模型来执行医学图像分析。然而,它们尚未广泛应用于肩部手术。作者开发了一种机器学习模型，用于从前后X射线图像中识别肩关节植入物制造商和类型。
UNASSIGNED：部署的模型是卷积神经网络（CNN），在计算机视觉任务中得到了广泛的应用。从单个机构获得696张X射线照片。70%被用来训练模型，而对30%进行了评估。
UNASSIGNED：在评估集上，该模型的总体准确率为93.9%，具有阳性预测值，在10种不同类型的植入物中，敏感性和F-1评分为94%(4个反向，6解剖）。每个植入物的平均识别时间为0.110s。
UNASSIGNED：这项概念验证研究表明，机器学习可以帮助进行术前计划并提高肩部手术的成本效益。
UNASSIGNED：部署的模型是卷积神经网络（CNN），在计算机视觉任务中得到了广泛的应用。从单个机构获得696张X射线照片。70%被用来训练模型，而对30%进行了评估。
UNASSIGNED：在评估集上，该模型的总体准确率为93.9%，具有阳性预测值，在10种不同类型的植入物中，敏感性和F-1评分为94%(4个反向，6解剖）。每个植入物的平均识别时间为0.110s。
UNASSIGNED：这项概念验证研究表明，机器学习可以帮助进行术前计划并提高肩部手术的成本效益。
