Department of Diagnostic Imaging, Sheba Medical Center, Tel Hashomer, Israel; Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel; Department of Nuclear Medicine, Sourasky Medical Center, Tel-Aviv, Israel.
Eur J Radiol. 2024 Jun;175:111460. doi: 10.1016/j.ejrad.2024.111460. Epub 2024 Apr 10.
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.
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.
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.
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.
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.
通过 X 光和在较小程度上通过 CT 对创伤性膝关节损伤进行准确诊断具有挑战性，骨折有时会被忽略。关节积液或脂肪-血液关节积血等辅助征象提示骨折，需要进一步进行影像学检查。人工智能 (AI) 可以实现图像分析自动化，提高诊断准确性，并有助于优先进行临床上重要的 X 光或 CT 研究。
开发和评估一种用于检测膝关节 X 光和选定 CT 图像中任何类型积液的 AI 算法，并区分单纯性积液和提示关节内骨折的脂肪-血液关节积血。
这项回顾性研究分析了 2016 年 1 月至 2023 年 2 月期间创伤后膝关节的影像学检查结果，将图像分为脂肪-血液关节积血、单纯性积液或正常。它使用 FishNet-150 算法进行图像分类，使用类激活图突出决策相关区域。根据至少具有四年经验的放射科医生的评估，将 AI 的诊断准确性与金标准进行验证。
分析包括 515 名患者的 CT 图像和 637 名创伤后患者的 X 光图像，确定了脂肪-血液关节积血、单纯性积液和正常发现。AI 在 X 光片中检测任何积液的 AUC 为 0.81，检测单纯性积液的 AUC 为 0.78，检测脂肪-血液关节积血的 AUC 为 0.83；在 CT 中，分别为 0.89、0.89 和 0.91。
该 AI 算法可有效检测创伤后膝关节积液，并区分 X 光和选定 CT 图像中的单纯性积液和脂肪-血液关节积血，需要进一步研究来验证这些结果。
