Using YOLO v7 to Detect Kidney in Magnetic Resonance Imaging.

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.

Deep learning algorithm (YOLOv7) for automated renal mass detection on contrast-enhanced MRI: a 2D and 2.5D evaluation of results.

INTRODUCTION: Accurate diagnosis and treatment of kidney tumors greatly benefit from automated solutions for detection and classification on MRI. In this study, we explore the application of a deep learning algorithm, YOLOv7, for detecting kidney tumors on contrast-enhanced MRI. MATERIAL AND METHODS: We assessed the performance of YOLOv7 tumor detection on excretory phase MRIs in a large institutional cohort of patients with RCC. Tumors were segmented on MRI using ITK-SNAP and converted to bounding boxes. The cohort was randomly divided into ten benchmarks for training and testing the YOLOv7 algorithm. The model was evaluated using both 2-dimensional and a novel in-house developed 2.5-dimensional approach. Performance measures included F1, Positive Predictive Value (PPV), Sensitivity, F1 curve, PPV-Sensitivity curve, Intersection over Union (IoU), and mean average PPV (mAP). RESULTS: A total of 326 patients with 1034 tumors with 7 different pathologies were analyzed across ten benchmarks. The average 2D evaluation results were as follows: Positive Predictive Value (PPV) of 0.69 ± 0.05, sensitivity of 0.39 ± 0.02, and F1 score of 0.43 ± 0.03. For the 2.5D evaluation, the average results included a PPV of 0.72 ± 0.06, sensitivity of 0.61 ± 0.06, and F1 score of 0.66 ± 0.04. The best model performance demonstrated a 2.5D PPV of 0.75, sensitivity of 0.69, and F1 score of 0.72. CONCLUSION: Using computer vision for tumor identification is a cutting-edge and rapidly expanding subject. In this work, we showed that YOLOv7 can be utilized in the detection of kidney cancers.

Barriers and Facilitators to Risk Reduction of Cardiovascular Disease in Hypertensive Patients in Nigeria.

Background: In Sub-Saharan Africa (SSA), the prevalence of hypertension is increasing due to many factors like rapid population growth, globalization, stress, and urbanization. We aimed to characterize the perceptions of cardiovascular disease (CVD) risk among individuals with hypertension living in Nigeria and identify barriers and facilitators to optimal hypertension management. Methods: This cross-sectional survey study was conducted at a large teaching hospital in Lagos, Nigeria. We used a convenient sample of males and females, aged 18 or older, with a diagnosis of hypertension who presented for outpatient visits in the cardiology, nephrology, or family medicine clinics between November 1 and 30, 2020. A semiquantitative approach was utilized with a survey consisting of closed and open-ended questionnaires focused on patient knowledge, perceptions of CVD risk, and barriers and facilitators of behavioral modifications to reduce CVD risk. Results: There were 256 subjects, and 62% were female. The mean age was 58.3 years (standard deviation (SD) = 12.6). The mean duration of the hypertension diagnosis was 10.1 years. Most participants were quite knowledgeable about hypertension; however, we observed some knowledge gaps, including a belief that too much "worrying or overthinking" was a major cause of hypertension and that an absence of symptoms indicated that hypertension was under control. Barriers to hypertension management include age, discomfort or pain, and lack of time as barriers to exercise. Tasteless meals and having to cook for multiple household members were barriers to decreasing salt intake. Cost and difficulty obtaining medications were barriers to medication adherence. Primary facilitators were family support or encouragement and incorporating lifestyle modifications into daily routines. Conclusion: We identified knowledge gaps about hypertension and CVD among our study population. These gaps enable opportunities to develop targeted interventions by healthcare providers, healthcare systems, and local governments. Our findings also help in the promotion of community-based interventions that address barriers to hypertension control and promote community and family involvement in hypertension management in these settings.