背景:创伤性膝关节损伤具有挑战性,要通过放射线照相术进行准确诊断,并且在较小程度上进行诊断,通过CT,骨折有时被忽视。伴随症状如关节积液或脂关节积血提示骨折,表明需要进一步成像。人工智能(AI)可以自动进行图像分析,提高诊断准确性,并有助于优先考虑临床重要的X射线或CT研究。
目的:开发和评估一种AI算法,用于检测膝关节X线和选定的CT图像中的任何类型的积液,并区分简单的积液和指示关节内骨折的脂关节积血。
方法:这项回顾性研究分析了2016年1月至2023年2月的创伤后膝关节影像学检查,将图像分为脂关节积血,简单积液,或正常。它利用FishNet-150算法进行图像分类,类激活图突出显示决策影响区域。人工智能的诊断准确性经过了黄金标准的验证,根据具有至少四年经验的放射科医生的评估。
结果:分析包括515例患者的CT图像和637例创伤后患者的X射线,鉴别脂关节积血,简单积液,和正常的发现。AI显示检测任何积液的AUC为0.81,简单积液为0.78,X线中的脂血关节炎为0.83;分别为0.89、0.89和0.91,在CT中。
结论:AI算法可有效检测膝关节积液,并区分创伤后患者的单纯积液和脂肪-关节积血,需要进一步的研究来验证这些结果。
BACKGROUND: Traumatic knee injuries are challenging to diagnose accurately through radiography and to a lesser extent, through CT, with fractures sometimes overlooked. Ancillary signs like joint effusion or lipo-hemarthrosis are indicative of fractures, suggesting the need for further imaging. Artificial Intelligence (AI) can automate image analysis, improving diagnostic accuracy and help prioritizing clinically important X-ray or CT studies.
OBJECTIVE: To develop and evaluate an AI algorithm for detecting effusion of any kind in knee X-rays and selected CT images and distinguishing between simple effusion and lipo-hemarthrosis indicative of intra-articular fractures.
METHODS: This retrospective study analyzed post traumatic knee imaging from January 2016 to February 2023, categorizing images into lipo-hemarthrosis, simple effusion, or normal. It utilized the FishNet-150 algorithm for image classification, with class activation maps highlighting decision-influential regions. The AI\'s diagnostic accuracy was validated against a gold standard, based on the evaluations made by a radiologist with at least four years of experience.
RESULTS: Analysis included CT images from 515 patients and X-rays from 637 post traumatic patients, identifying lipo-hemarthrosis, simple effusion, and normal findings. The AI showed an AUC of 0.81 for detecting any effusion, 0.78 for simple effusion, and 0.83 for lipo-hemarthrosis in X-rays; and 0.89, 0.89, and 0.91, respectively, in CTs.
CONCLUSIONS: The AI algorithm effectively detects knee effusion and differentiates between simple effusion and lipo-hemarthrosis in post-traumatic patients for both X-rays and selected CT images further studies are needed to validate these results.