A real-time system using deep learning to detect and track ureteral orifices during urinary endoscopy

Current advances in the use of artificial intelligence in predicting and managing urological complications

BACKGROUND

Artificial intelligence (AI) has emerged as a promising avenue for improving patient care and surgical outcomes in urological surgery. However, the extent of AI's impact in predicting and managing complications is not fully elucidated.

OBJECTIVES

We review the application of AI to foresee and manage complications in urological surgery, assess its efficacy, and discuss challenges to its use.

METHODS AND MATERIALS

A targeted non-systematic literature search was conducted using the PubMed and Google Scholar databases to identify studies on AI in urological surgery and its complications. Evidence from the studies was synthesised.

RESULTS

Incorporating AI into various facets of urological surgery has shown promising advancements. From preoperative planning to intraoperative guidance, AI is revolutionising the field, demonstrating remarkable proficiency in tasks such as image analysis, decision-making support, and complication prediction. Studies show that AI programmes are highly accurate, increase surgical precision and efficiency, and reduce complications. However, implementation challenges exist in AI errors, human errors, and ethical issues.

CONCLUSION

AI has great potential in predicting and managing surgical complications of urological surgery. Advancements have been made, but challenges and ethical considerations must be addressed before widespread AI implementation.

Deep learning for real-time detection of nasopharyngeal carcinoma during nasopharyngeal endoscopy

Nasopharyngeal carcinoma (NPC) is known for high curability during early stage of the disease, and early diagnosis relies on nasopharyngeal endoscopy and subsequent pathological biopsy. To enhance the early diagnosis rate by aiding physicians in the real-time identification of NPC and directing biopsy site selection during endoscopy, we assembled a dataset comprising 2,429 nasopharyngeal endoscopy video frames from 690 patients across three medical centers. With these data, we developed a deep learning-based NPC detection model using the you only look once (YOLO) network. Our model demonstrated high performance, with precision, recall, mean average precision, and F1-score values of 0.977, 0.943, 0.977, and 0.960, respectively, for internal test set and 0.825, 0.743, 0.814, and 0.780 for external test set at 0.5 intersection over union. Remarkably, our model demonstrated a high inference speed (52.9 FPS), surpassing the average frame rate (25.0 FPS) of endoscopy videos, thus making real-time detection in endoscopy feasible.

A reinforcement federated learning based strategy for urinary disease dataset processing

Urinary disease is a complex healthcare issue that continues to grow in prevalence. Urine tests have proven valuable in identifying conditions such as kidney disease, urinary tract infections, and lower abdominal pain. While machine learning has made significant strides in automating urinary tract infection detection, the accuracy of existing methods is hindered by concerns surrounding data privacy and the time-intensive nature of training and testing with large datasets. Our proposed method aims to address these limitations and achieve highly accurate urinary tract infection detection across various healthcare laboratories, while simultaneously minimizing data security risks and processing delays. To tackle this challenge, we approach the problem as a combinatorial optimization task. We optimize the accuracy objective as a concave function and minimize computation delay as a convex function. Our work introduces a framework enabled by federated learning and reinforcement learning strategy (FLRLS), leveraging lab urine data. FLRLS employs deterministic agents to optimize the exploration and exploitation of urinary data, while the actual determination of urinary tract infections is performed at a centralized, aggregated node. Experimental results demonstrate that our proposed method improves accuracy by 5% and reduces total delay. By combining federated learning, reinforcement learning, and a combinatorial optimization approach, we achieve both high accuracy and minimal delay in urinary tract infection detection.