PAINe - 一种基于人工智能的虚拟助手，用于区分牙源性疼痛和颞下颌源性疼痛。

IF 3.5 2区医学 Q1 DENTISTRY, ORAL SURGERY & MEDICINE

Journal of endodontics Pub Date : 2024-12-01 DOI:10.1016/j.joen.2024.09.008

Bianca Marques de Mattos de Araujo MDS, PhD , Pedro Felipe de Jesus Freitas DDS , Angela Graciela Deliga Schroder MDS, PhD , Erika Calvano Küchler MDS, PhD , Flares Baratto-Filho MDS, PhD , Vania Portela Ditzel Westphalen MDS, PhD , Everdan Carneiro MDS, PhD , Ulisses Xavier da Silva-Neto MDS, PhD , Cristiano Miranda de Araujo MDS, PhD

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引用次数: 0

摘要

导言：与颞下颌功能障碍（TMD）相关的疼痛常常与牙源性疼痛相混淆，这是牙髓诊断中的一个难题。经过验证的筛查问卷可以帮助识别和区分疼痛的来源。因此，本研究旨在开发一种基于人工智能的虚拟助手，利用自然语言处理技术自动对牙痛患者进行初步筛查：PAINe 聊天机器人使用 Python 语言开发，使用 PyCharm 环境和 "openai "库集成了 ChatGPT 4 API，并使用 "streamlit "库构建了界面。聊天机器人集成了经过验证的 TMD 疼痛筛查问卷和一个关于当前疼痛强度的问题，用于对牙痛患者进行 TMD 鉴别诊断。在 50 个随机场景中对回答的准确性进行了评估，以比较聊天机器人与有效问卷。计算了卡帕系数，以评估聊天机器人的回答与有效问卷之间的一致程度：结果：聊天机器人的准确率达到了 86%，并且具有相当高的一致性（Kappa = 0.70）。大多数回复都很清晰，并提供了足够的诊断信息：利用基于大型语言模型的自然语言处理技术实施虚拟助手，对牙痛患者进行初步鉴别诊断筛查，结果表明有效问卷与聊天机器人之间存在很大的一致性。这种方法是筛查这些患者的一种实用、高效的选择。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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PAINe: An Artificial Intelligence–based Virtual Assistant to Aid in the Differentiation of Pain of Odontogenic versus Temporomandibular Origin

Introduction

Pain associated with temporomandibular dysfunction (TMD) is often confused with odontogenic pain, which is a challenge in endodontic diagnosis. Validated screening questionnaires can aid in the identification and differentiation of the source of pain. Therefore, this study aimed to develop a virtual assistant based on artificial intelligence using natural language processing techniques to automate the initial screening of patients with tooth pain.

Methods

The PAINe chatbot was developed in Python (Python Software Foundation, Beaverton, OR) language using the PyCharm (JetBrains, Prague, Czech Republic) environment and the openai library to integrate the ChatGPT 4 API (OpenAI, San Francisco, CA) and the Streamlit library (Snowflake Inc, San Francisco, CA) for interface construction. The validated TMD Pain Screener questionnaire and 1 question regarding the current pain intensity were integrated into the chatbot to perform the differential diagnosis of TMD in patients with tooth pain. The accuracy of the responses was evaluated in 50 random scenarios to compare the chatbot with the validated questionnaire. The kappa coefficient was calculated to assess the agreement level between the chatbot responses and the validated questionnaire.

Results

The chatbot achieved an accuracy rate of 86% and a substantial level of agreement (κ = 0.70). Most responses were clear and provided adequate information about the diagnosis.

Conclusions

The implementation of a virtual assistant using natural language processing based on large language models for initial differential diagnosis screening of patients with tooth pain demonstrated substantial agreement between validated questionnaires and the chatbot. This approach emerges as a practical and efficient option for screening these patients.

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来源期刊

Journal of endodontics 医学-牙科与口腔外科

CiteScore

8.80

自引率

9.50%

发文量

224

审稿时长

42 days

期刊介绍： The Journal of Endodontics, the official journal of the American Association of Endodontists, publishes scientific articles, case reports and comparison studies evaluating materials and methods of pulp conservation and endodontic treatment. Endodontists and general dentists can learn about new concepts in root canal treatment and the latest advances in techniques and instrumentation in the one journal that helps them keep pace with rapid changes in this field.