Chowdhury Adiba Tabassum, Salam Abdus, Naznine Mansura, Abdalla Da'ad, Erdman Lauren, Chowdhury Muhammad E H, Abbas Tariq O
Department of Electrical and Electronic Engineering, University of Dhaka, Dhaka 1000, Bangladesh.
Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshashi 6204, Bangladesh.
Diagnostics (Basel). 2024 Sep 17;14(18):2059. doi: 10.3390/diagnostics14182059.
Artificial intelligence (AI) is providing novel answers to long-standing clinical problems, and it is quickly changing pediatric urology. This thorough analysis focuses on current developments in AI technologies that improve pediatric urology diagnosis, treatment planning, and surgery results. Deep learning algorithms help detect problems with previously unheard-of precision in disorders including hydronephrosis, pyeloplasty, and vesicoureteral reflux, where AI-powered prediction models have demonstrated promising outcomes in boosting diagnostic accuracy. AI-enhanced image processing methods have significantly improved the quality and interpretation of medical images. Examples of these methods are deep-learning-based segmentation and contrast limited adaptive histogram equalization (CLAHE). These methods guarantee higher precision in the identification and classification of pediatric urological disorders, and AI-driven ground truth construction approaches aid in the standardization of and improvement in training data, resulting in more resilient and consistent segmentation models. AI is being used for surgical support as well. AI-assisted navigation devices help with difficult operations like pyeloplasty by decreasing complications and increasing surgical accuracy. AI also helps with long-term patient monitoring, predictive analytics, and customized treatment strategies, all of which improve results for younger patients. However, there are practical, ethical, and legal issues with AI integration in pediatric urology that need to be carefully navigated. To close knowledge gaps, more investigation is required, especially in the areas of AI-driven surgical methods and standardized ground truth datasets for pediatric radiologic image segmentation. In the end, AI has the potential to completely transform pediatric urology by enhancing patient care, increasing the effectiveness of treatments, and spurring more advancements in this exciting area.
人工智能（AI）正在为长期存在的临床问题提供全新的解决方案，并且正在迅速改变小儿泌尿外科。这项全面的分析聚焦于人工智能技术的当前发展，这些技术可改善小儿泌尿外科的诊断、治疗规划和手术效果。深度学习算法有助于以前所未有的精度检测包括肾积水、肾盂成形术和膀胱输尿管反流等疾病中的问题，在这些疾病中，人工智能驱动的预测模型在提高诊断准确性方面已显示出有前景的结果。人工智能增强的图像处理方法显著提高了医学图像的质量和解读。这些方法的例子包括基于深度学习的分割和对比度受限自适应直方图均衡化（CLAHE）。这些方法确保了小儿泌尿系统疾病识别和分类的更高精度，并且人工智能驱动的真实数据构建方法有助于训练数据的标准化和改进，从而产生更具弹性和一致性的分割模型。人工智能也正被用于手术支持。人工智能辅助导航设备通过减少并发症和提高手术准确性来帮助进行诸如肾盂成形术等复杂手术。人工智能还有助于长期患者监测、预测分析和定制治疗策略，所有这些都改善了年轻患者的治疗效果。然而，在小儿泌尿外科中整合人工智能存在实际、伦理和法律问题，需要谨慎应对。为了填补知识空白，需要更多的研究，特别是在人工智能驱动的手术方法和小儿放射图像分割的标准化真实数据集领域。最终，人工智能有潜力通过改善患者护理、提高治疗效果以及推动这一令人兴奋领域的更多进步，彻底改变小儿泌尿外科。
