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 本研究探索使用最新You Only Look Once（YOLO V7）目标检测方法，通过对医学图像格式训练和测试改进的YOLO V7，增强医学影像中肾脏检测的能力。方法 研究纳入878例不同亚型肾细胞癌（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（MRI）扫描。从回顾性维护数据库中招募326例具有1034个肿瘤的患者，并围绕其肿瘤绘制边界框。使用80%标注案例训练主模型，保留20%用于测试（主测试集）。随后利用最优主模型对剩余861例患者进行肿瘤识别，通过模型在其扫描上生成边界框坐标。基于未分割患者的生成坐标构建十个基准训练集，最终模型用于主测试集的肾脏预测。我们报告了阳性预测值（PPV）、灵敏度和平均精度均值（mAP）。结果 主训练集显示平均PPV为0.94±0.01，灵敏度为0.87±0.04，mAP为0.91±0.02。最优主模型取得PPV 0.97、灵敏度0.92及mAP 0.95。最终模型平均PPV为0.95±0.03，灵敏度为0.98±0.004，mAP为0.95±0.01。结论 采用基于医学图像库的半监督方法，我们开发了高性能肾脏检测模型。需进一步外部验证以评估模型的泛化能力。