Almoammar Khalid A
Department of Pediatric Dentistry and Orthodontics, College of Dentistry, King Saud University, P.O. Box 60169, Riyadh 11545, Saudi Arabia.
Children (Basel). 2024 Jan 23;11(2):140. doi: 10.3390/children11020140.
Cleft lip and palate (CLP) is the most common craniofacial malformation, with a range of physical, psychological, and aesthetic consequences. In this comprehensive review, our main objective is to thoroughly examine the relationship between CLP anomalies and the use of artificial intelligence (AI) in children. Additionally, we aim to explore how the integration of AI technology can bring about significant advancements in the fields of diagnosis, treatment methods, and predictive outcomes. By analyzing the existing evidence, we will highlight state-of-the-art algorithms and predictive AI models that play a crucial role in achieving precise diagnosis, susceptibility assessment, and treatment planning for children with CLP anomalies. Our focus will specifically be on the efficacy of alveolar bone graft and orthodontic interventions. The findings of this review showed that deep learning (DL) models revolutionize the diagnostic process, predict susceptibility to CLP, and enhance alveolar bone grafts and orthodontic treatment. DL models surpass human capabilities in terms of precision, and AI algorithms applied to large datasets can uncover the intricate genetic and environmental factors contributing to CLP. Additionally, Machine learning aids in preoperative planning for alveolar bone grafts and provides personalized treatment plans in orthodontic treatment. In conclusion, these advancements inspire optimism for a future where AI seamlessly integrates with CLP management, augmenting its analytical capabilities.
唇腭裂（CLP）是最常见的颅面畸形，会产生一系列身体、心理和美学方面的后果。在这篇综述中，我们的主要目标是全面研究CLP异常与儿童人工智能（AI）应用之间的关系。此外，我们旨在探讨AI技术的整合如何能在诊断、治疗方法和预测结果等领域带来重大进展。通过分析现有证据，我们将重点介绍在实现对CLP异常儿童的精确诊断、易感性评估和治疗规划方面发挥关键作用的先进算法和预测性AI模型。我们将特别关注牙槽骨移植和正畸干预的效果。本综述的结果表明，深度学习（DL）模型彻底改变了诊断过程，预测CLP的易感性，并增强了牙槽骨移植和正畸治疗效果。DL模型在精度方面超越了人类能力，应用于大型数据集的AI算法能够揭示导致CLP的复杂遗传和环境因素。此外，机器学习有助于牙槽骨移植的术前规划，并在正畸治疗中提供个性化治疗方案。总之，这些进展为AI与CLP管理无缝集成并增强其分析能力的未来带来了乐观情绪。
