Introduction This study explores the use of the latest You Only Look Once (YOLO V7) object detection method to enhance kidney detection in medical imaging by training and testing a modified YOLO V7 on medical image formats. Methods Study includes 878 patients with various subtypes of renal cell carcinoma (RCC) and 206 patients with normal kidneys. A total of 5657 MRI scans for 1084 patients were retrieved. 326 patients with 1034 tumors recruited from a retrospective maintained database, and bounding boxes were drawn around their tumors. A primary model was trained on 80% of annotated cases, with 20% saved for testing (primary test set). The best primary model was then used to identify tumors in the remaining 861 patients and bounding box coordinates were generated on their scans using the model. Ten benchmark training sets were created with generated coordinates on not-segmented patients. The final model used to predict the kidney in the primary test set. We reported the positive predictive value (PPV), sensitivity, and mean average precision (mAP). Results The primary training set showed an average PPV of 0.94 +/- 0.01, sensitivity of 0.87 +/- 0.04, and mAP of 0.91 +/- 0.02. The best primary model yielded a PPV of 0.97, sensitivity of 0.92, and mAP of 0.95. The final model demonstrated an average PPV of 0.95 +/- 0.03, sensitivity of 0.98 +/- 0.004, and mAP of 0.95 +/- 0.01. Conclusion Using a semi-supervised approach with a medical image library, we developed a high-performing model for kidney detection. Further external validation is required to assess the model's generalizability.

翻译：摘要 引言 本研究探索采用最新一代You Only Look Once（YOLO V7）目标检测方法，通过训练和测试改进型YOLO V7模型在医学影像格式上的应用，以提升医学影像中的肾脏检测能力。方法 研究纳入878例不同类型肾细胞癌（RCC）患者及206例正常肾脏患者，共获取1084例患者的5657次磁共振成像扫描。从回顾性维护数据库中招募326例患者（含1034个肿瘤），并对其肿瘤绘制边界框。将80%标注病例用于训练初始模型，剩余20%用于测试（初始测试集）。随后，利用最优初始模型对剩余861例患者进行肿瘤识别，并通过模型生成其扫描图像的边界框坐标。基于未分割患者的生成坐标构建十个基准训练集，最终模型用于预测初始测试集中的肾脏区域。报告阳性预测值（PPV）、灵敏度及平均精度均值（mAP）。结果 初始训练集显示平均PPV为0.94±0.01、灵敏度为0.87±0.04、mAP为0.91±0.02。最优初始模型取得PPV 0.97、灵敏度0.92、mAP 0.95。最终模型呈现平均PPV 0.95±0.03、灵敏度0.98±0.004、mAP 0.95±0.01。结论 通过半监督方法结合医学影像库，我们开发出高性能肾脏检测模型。需进一步开展外部验证以评估模型的泛化能力。