Autonomous driving paper index
Artificial intelligence and machine learning in neurogenic tract dysfunction and spinal cord injury: current status, emerging technologies, and ethical perspectives
One-line summary
Artificial intelligence (AI) and machine learning are increasingly applied across urology, with particular promise for the management of neurogenic lower urinary tract dysfunction (NLUTD) in individuals with spinal cord injury (SCI).
Engineering notes
Key topics: autonomous driving. See the paper for implementation details and experimental results.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
Artificial intelligence (AI) and machine learning are increasingly applied across urology, with particular promise for the management of neurogenic lower urinary tract dysfunction (NLUTD) in individuals with spinal cord injury (SCI). There is also particular promise in the management of NLUTD, a complex, lifelong condition affecting individuals with SCI and also occurring in the context of multiple sclerosis, Parkinson’s disease, and other neurological disorders. This article provides a comprehensive analysis of AI’s current and emerging role in neuro-urology, with dedicated focus on the specific challenges and opportunities presented by NLUTD and SCI. We review the epidemiological and clinical burden of neurogenic bladder dysfunction. We further examine AI applications in uro-oncology, functional urology, digital pathology, and robotic surgery, integrating these with the specialized perspective of neuro-urology. The particular vulnerability of individuals with SCI to algorithmic bias, access inequity, and assistive technology dependency is critically discussed. We identified five key domains where AI may transform care for patients with NLUTD: automated urodynamic interpretation including detection of detrusor overactivity and detrusor-sphincter dyssynergia, predictive risk stratification for renal deterioration, closed-loop neuromodulation, remote digital monitoring, and AI-assisted rehabilitation support. Alongside the substantial opportunities, we articulate the ethical, moral, and societal responsibilities that accompany AI integration in this population, emphasizing that patients with SCI and NLUTD deserve not merely inclusion in AI development but active partnership as co-designers of the technologies that will shape their lives.
Links and sources
Need this topic turned into a technical roadmap?
Full Self Driving can prepare a custom autonomous driving literature review, code map, dataset map, and B2B technology assessment.
Request B2B research
Comments