人工智能用于吞咽困难筛查：一种机器学习方法。
Artificial intelligence for dysphagia screening: A machine learning approach.
文献信息
| PMID | 42809591 |
|---|---|
| 原文 | 在 PubMed 查看原文 ↗ |
| 发表日期 | 2026 |
| 作者 | Rafaela Soares Rech |
| 作者单位 | Graduate Program in Dentistry, Faculty of Dentistry, Federal University of Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil. |
| 期刊 | PLOS digital health |
| SCI 分区 | Q1 |
| IF | 7.7 |
| 研究类型 | AI/ML · 临床 |
| 所属专科 | 咽喉科 |
中文摘要
背景： 在高危人群中早期、可靠地筛查口咽性吞咽困难症状是有效管理的关键第一步。本研究旨在提出一种机器学习模型，该模型能够基于诊断准确性预测个体是否存在确诊吞咽困难的风险。方法：这是一项探索性横断面诊断准确性研究。对成人和老年人进行筛查和评估，并对有吞咽主诉以及言语治疗师临床评估中发现体征或症状的参与者进行视频荧光吞咽检查以确认吞咽困难。基于理论模型测试了八种算法以评估其诊断准确性：朴素贝叶斯、K近邻、逻辑回归、决策树、随机森林、支持向量机、多层感知机和卷积神经网络。使用Python中的Pandas库进行数据可视化和操作。结果：共纳入465名参与者，其中153人患有吞咽困难。提出了一项包含15个与个人、健康和口腔健康特征相关条目的筛查。最终表现最好的四个模型是朴素贝叶斯（准确率0.76，灵敏度0.84）、逻辑回归（准确率0.79，灵敏度0.82）、支持向量机（准确率0.78，灵敏度0.79）和卷积神经网络（准确率0.78，灵敏度0.74）。结论：基于不同机器学习方法的人工智能在吞咽困难筛查中具有前景，显示出足够的诊断准确性。未来应开展研究以外部验证所提出的筛查模型。
英文摘要
Early and reliable screening for oropharyngeal dysphagia symptoms in at-risk populations is a crucial first stage in effective management. This study aims to propose a machine learning model that can predict whether individuals are at risk of having confirmed dysphagia based on diagnostic accuracy. This was an exploratory cross-sectional diagnostic accuracy study. Adults and older adults were screened and assessed, and a videofluoroscopy swallowing study was performed to confirm dysphagia in participants with complaints about swallowing and signs or symptoms identified in the clinical assessment by a speech therapist. Eight algorithms were tested based on a theoretical model to evaluate their diagnostic accuracy: Naive Bayes, K-Nearest Neighbors, Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, Multilayer Perceptron, and Convolutional Neural Networks. The Pandas library in Python was used for data visualization and manipulation. In total, 465 participants were included, of whom 153 had dysphagia. A screening with 15 items related to personal, health, and oral health characteristics is proposed. The final four best-performing models were Naive Bayes (accuracy 0.76, sensitivity 0.84), Logistic Regression (accuracy 0.79, sensitivity 0.82), Support Vector Machine (accuracy 0.78, sensitivity 0.79), and Convolutional Neural Network (accuracy 0.78, sensitivity 0.74). The use of artificial intelligence based on different machine-learning approaches is promising for dysphagia screening, demonstrating adequate diagnostic accuracy. Future studies should be conducted to externally validate the proposed screening models.