使用基于深度学习的模型预测放射治疗后的毒性结果:系统性审查
D Tan1, N F Mohd Nasir1, H Abdul Manan2
1Centre of Diagnostic, Therapeutic and Investigative Sciences (CODTIS). Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Jalan Raja Muda Aziz, Kuala Lumpur 50300 Malaysia.
概括
深度学习模型在预测放射治疗引起的毒性方面表现一致. 需要对更大的数据集和标准化方法进行进一步的研究,以提高预测准确度.
科学领域:
- 辐射疗法 辐射疗法
- 在瘤学瘤学.
- 人工智能的人工智能
背景情况:
- 放射治疗引起的毒性是癌症治疗中的一个重要问题.
- 准确的毒性预测对于个性化治疗规划至关重要.
- 深度学习 (DL) 提供了改善毒性预测模型的潜力.
结论:
- 深度学习模型显示,对持续的放射治疗毒性预测有希望.
- 未来的研究需要大量,多样化的数据集和标准化的方法.
- 提高研究成果的一致性对于临床应用至关重要.
相关概念视频
01:23Toxicity Testing in Animals
Toxicity tests in animals are grounded on two main assumptions: first, the effects observed in laboratory animals can be extrapolated to humans, especially when adjusted for body surface area; second, high-dose exposure in animals is essential to identify potential human hazards from lower doses. This is based on the quantal dose-response concept, which faces the challenge of extrapolating results from relatively few test animals to much larger human populations. For example, a 0.01% incidence...
01:26Pharmaceutical Poisoning: Treatment Strategies
Treatment strategies for poisoning are a critical aspect of emergency medicine, focusing on preventing the absorption of toxins and enhancing their elimination. When a poisoning incident occurs, the first response is to halt exposure and decontaminate the patient, particularly through gastrointestinal (GI) methods if the poison was ingested.Gastrointestinal Decontamination Techniques:Activated charcoal is the cornerstone of GI decontamination. It works through adsorption, binding the toxin to...