Is There a Correlation Between Behçet Disease and Lower Urinary Tract Symptoms?

The Current State of Artificial Intelligence Application in Urology

Artificial intelligence (AI) is being used in many areas of healthcare, including disease diagnosis and personalized treatment and rehabilitation management. Medical AI research and development has primarily focused on diagnosis, prediction, treatment, and management as an aid to patient care. AI is being utilized primarily in the areas of personal healthcare and diagnostic imaging. In the field of urology, significant investments are being made in the development of urination monitoring systems in the field of personal healthcare and ureteral stricture and urinary stone diagnosis solutions in the field of diagnostic imaging. In addition, AI technology is also being applied in the field of neurogenic bladder to develop risk monitoring systems based on video and audio data. This paper examines the application of AI to urological diseases and discusses the current trends and future prospects of AI research.

Development and Evaluation of Urolithiasis Detection Technology Based on a Multimethod Algorithm

Purpose: In this paper, we propose an optimal ureter stone detection model utilizing multiple artificial intelligence technologies. Specifically, the proposed model of urinary tract stone detection merges an artificial intelligence model and an image processing model, resulting in a multimethod approach.Methods: We propose an optimal urinary tract stone detection algorithm based on artificial intelligence technology. This method was intended to increase the accuracy of urinary tract stone detection by combining deep learning technology (Fast R-CNN) and image processing technology (Watershed).Results: As a result of deriving the confusion matrix, the sensitivity and specificity of urinary tract stone detection were calculated to be 0.90 and 0.91, and the accuracy for their position was 0.84. This value was higher than 0.8, which is the standard for accuracy. This finding confirmed that accurate guidance to the stones area was possible when the developed platform was used to support actual surgery.Conclusions: The performance evaluation of the method proposed herein indicated that it can effectively play an auxiliary role in diagnostic decision-making with a clinically acceptable range of safety. In particular, in the case of ambush stones or urinary stones accompanying ureter polyps, the value that could be obtained through combination therapy based on diagnostic assistance could be evaluated.

New Trends in Innovative Technologies Applying Artificial Intelligence to Urinary Diseases

Artificial intelligence (AI) is used in various fields of medicine, with applications encompassing all areas of medical services, such as the development of medical robots, the diagnosis and personalized treatment of diseases, and personalized healthcare. Medical AI research and development have been largely focused on diagnosis, prediction, treatment, and management as an auxiliary means of patient care. AI is mainly used in the fields of personal healthcare and diagnostic imaging. In urology, substantial investments are being made in the development of urination monitoring systems in the personal healthcare field and diagnostic solutions for ureteral stricture and urolithiasis in the diagnostic imaging field. This paper describes AI applications for urinary diseases and discusses current trends and future perspectives in AI research.

A Study on the Optimal Artificial Intelligence Model for Determination of Urolithiasis

Purpose

This paper aims to develop a clinical decision support system (CDSS) that can help detect the stone that is most important to the diagnosis of urolithiasis. Among them, especially for the development of artificial intelligence (AI) models that support a final judgment in CDSS, we would like to study the optimal AI model by comparing and evaluating them.

Methods

This paper proposes the optimal ureter stone detection model using various AI technologies. The use of AI technology compares and evaluates methods such as machine learning (support vector machine), deep learning (ResNet-50, Fast R-CNN), and image processing (watershed) to find a more effective method for detecting ureter stones.

Results

The final value of sensitivity, which is calculated using true positive (TP) and false negative and is a measure of the probability of TP results, showed high recognition accuracy, with an average value of 0.93 for ResNet-50. This finding confirmed that accurate guidance to the stones area was possible when the developed platform was used to support actual surgery.

Conclusions

The general situation in the most effective way to the detection stone can be found. But a variety of variables may be slightly different the difference through the term could tell. Future works, on urological diseases, are diverse and the research will be expanded by customizing AI models specialized for those diseases.

Development of an Artificial Intelligence-Based Support Technology for Urethral and Ureteral Stricture Surgery

Purpose

This paper proposes a technological system that uses artificial intelligence to recognize and guide the operator to the exact stenosis area during endoscopic surgery in patients with urethral or ureteral strictures. The aim of this technological solution was to increase surgical efficiency.

Methods

The proposed system utilizes the ResNet-50 algorithm, an artificial intelligence technology, and analyzes images entering the endoscope during surgery to detect the stenosis location accurately and provide intraoperative clinical assistance. The ResNet-50 algorithm was chosen to facilitate accurate detection of the stenosis site.

Results

The high recognition accuracy of the system was confirmed by an average final sensitivity value of 0.96. Since sensitivity is a measure of the probability of a true-positive test, this finding confirms that the system provided accurate guidance to the stenosis area when used for support in actual surgery.

Conclusions

The proposed method supports surgery for patients with urethral or ureteral strictures by applying the ResNet-50 algorithm. The system analyzes images entering the endoscope during surgery and accurately detects stenosis, thereby assisting in surgery. In future research, we intend to provide both conservative and flexible boundaries of the strictures.

A Contemporary Review of Behcet's Syndrome

Behcet's syndrome (BS) is a chronic systemic inflammatory vasculitis with a wide range of clinical manifestations including recurrent oral and genital ulcers; cutaneous lesions; and ophthalmic, neurologic, and gastrointestinal involvement. BS has a global distribution but is particularly prevalent in so-called Silk Road populations. Disease onset is usually around the third or fourth decade of life, and the sex ratio is roughly 1:1. Both environmental and genetic factors contribute to the etiology of BS, although the detailed mechanisms remain unclear. At present, there is no laboratory examination with diagnostic value for BS; therefore, a diagnosis is made based on clinical manifestations. The International Study Group diagnostic criteria published in 1990 is the most widely used and recognized, but in order to improve sensitivity, the International Criteria for Behcet's Disease is developed in 2014. Evaluating disease activity in BS is an important basis for treatment selection and monitoring, the simplified Behcet's Disease Current Activity Form (2006 version) is a well-established scoring method. Given that multiple organs are affected in BS, it must be differentiated from other diseases with similar manifestations or that may be induced by drug treatment. The goal of BS treatment is to eradicate triggers and/or aggravating factors, alleviate and control clinical symptoms, prevent and treat any damage to organs, slow disease progression, and improve the patient's quality of life. The clinical management of BS depends on the affected organs and disease severity. In this review, we summarize the current state of knowledge of BS pathogenesis and therapeutic options.