Anweigi Lamyia, Naceur Iheb Ben, Awad Jomana, Ahmeda Mohamed, Barhom Noha, Tamimi Faleh
College of Dental Medicine, QU Health, Qatar University, Doha, Qatar.
Dentistry, Newcastle University, Newcastle upon Tyne, UK.
J Oral Rehabil. 2025 Sep;52(9):1377-1385. doi: 10.1111/joor.13986. Epub 2025 May 9.
Natural language understanding (NLU), a subfield of artificial intelligence, focuses on the computational understanding of human language. This technology offers an objective and quantitative approach to analysing interviews in qualitative research. This study hypothesises that NLU can assess the impact of oral health on quality of life by analysing semi-structured interviews.
This study aimed to assess the utility of NLU in evaluating oral health-related quality of life by analysing semi-structured interviews with individuals diagnosed with hypodontia.
A cross-sectional qualitative study was conducted on 10 participants (aged 16-25 years) suffering from hypodontia. Semi-structured interviews were transcribed and analysed using IBM Watson NLU text analysis. The analysis identified entities, keywords, sentiments (positive and negative) and emotions (joy, sadness, anger, fear and disgust) expressed in the interviews.
NLU analysis revealed a predominantly negative sentiment towards hypodontia and its management, with 93.2% of identified entities presenting a negative sentiment and only 6.8% showing a positive sentiment. Patient sentiment correlated inversely with age (R = -0.49), treatment waiting time (R = -0.22) and OHIP score (R = -20). Negative sentiments and sadness were most prominent when discussing the history of dental problems and feelings about their teeth, whereas joy and positive sentiments were expressed regarding successful dental work. Keywords associated with negative sentiment were primarily related to treatment length and delays.
NLU effectively identified patients' negative sentiments and emotional responses to oral health conditions, demonstrating its potential as a valuable tool in qualitative dental research.
自然语言理解（NLU）作为人工智能的一个子领域，专注于对人类语言的计算理解。这项技术为定性研究中的访谈分析提供了一种客观且定量的方法。本研究假设NLU可以通过分析半结构化访谈来评估口腔健康对生活质量的影响。
本研究旨在通过分析对患有牙量不足症的个体进行的半结构化访谈，评估NLU在评估口腔健康相关生活质量方面的效用。
对10名患有牙量不足症的参与者（年龄在16 - 25岁之间）进行了一项横断面定性研究。使用IBM Watson NLU文本分析对转录的半结构化访谈进行分析。该分析确定了访谈中表达的实体、关键词、情感（积极和消极）以及情绪（喜悦、悲伤、愤怒、恐惧和厌恶）。
NLU分析显示，对牙量不足症及其治疗主要存在负面情绪，所识别的实体中有93.2%呈现负面情绪，只有6.8%呈现正面情绪。患者的情绪与年龄（R = -0.49）、治疗等待时间（R = -0.22）和OHIP评分（R = -20）呈负相关。在讨论牙齿问题的历史和对牙齿的感受时，负面情绪和悲伤最为突出，而在谈到成功的牙科治疗时则表达了喜悦和积极情绪。与负面情绪相关的关键词主要与治疗时长和延误有关。
NLU有效地识别了患者对口腔健康状况的负面情绪和情感反应，证明了其作为定性牙科研究中一种有价值工具的潜力。
