Department of Biomedical Engineering, University of Calgary, Calgary, Alberta, Canada.
Department of Clinical Studies, Ontario Veterinary College, University of Guelph, Guelph, Ontario, Canada.
Vet Radiol Ultrasound. 2024 Jul;65(4):417-428. doi: 10.1111/vru.13373. Epub 2024 Apr 26.
Thoracic radiographs are an essential diagnostic tool in companion animal medicine and are frequently used as a part of routine workups in patients presenting for coughing, respiratory distress, cardiovascular diseases, and for staging of neoplasia. Quality control is a critical aspect of radiology practice in preventing misdiagnosis and ensuring consistent, accurate, and reliable diagnostic imaging. Implementing an effective quality control procedure in radiology can impact patient outcomes, facilitate clinical decision-making, and decrease healthcare costs. In this study, a machine learning-based quality classification model is suggested for canine and feline thoracic radiographs captured in both ventrodorsal and dorsoventral positions. The problem of quality classification was divided into collimation, positioning, and exposure, and then an automatic classification method was proposed for each based on deep learning and machine learning. We utilized a dataset of 899 radiographs of dogs and cats. Evaluations using fivefold cross-validation resulted in an F1 score and AUC score of 91.33 (95% CI: 88.37-94.29) and 91.10 (95% CI: 88.16-94.03), respectively. Results indicated that the proposed automatic quality classification has the potential to be implemented in radiology clinics to improve radiograph quality and reduce nondiagnostic images.
胸部 X 光片是伴侣动物医学中必不可少的诊断工具，常用于因咳嗽、呼吸窘迫、心血管疾病以及肿瘤分期而就诊的患者的常规检查。质量控制是放射科实践中防止误诊和确保一致、准确和可靠的诊断成像的关键方面。在放射科实施有效的质量控制程序可以影响患者的结果，促进临床决策，并降低医疗保健成本。在这项研究中，提出了一种基于机器学习的犬猫胸部 X 光正位和背腹位的质量分类模型。将质量分类问题分为准直、定位和曝光三个部分，然后针对每个部分提出了基于深度学习和机器学习的自动分类方法。我们使用了一个包含 899 张犬猫 X 光片的数据集。五重交叉验证的评估结果得出 F1 得分为 91.33（95%置信区间：88.37-94.29），AUC 得分为 91.10（95%置信区间：88.16-94.03）。结果表明，所提出的自动质量分类方法有可能在放射科诊所实施，以提高 X 光片的质量并减少非诊断性图像。
