  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.
