牙周诊断的进步:多模态语言模型在全景放射图的视觉解释中的应用
Albert Camlet1, Aida Kusiak1, Agata Ossowska1
1Department of Periodontology and Oral Mucosa Diseases, Medical University of Gdansk, Orzeszkowej 18 St., 80-208 Gdansk, Poland.
Diagnostics (Basel, Switzerland)
|August 14, 2025
概括
像ChatGPT这样的大型语言模型显示了在全景放射图上计数牙的潜力,但高估了剩余的骨高度. 目前的AI准确性不足以进行牙周炎的临床诊断.
科学领域:
- 人工智能在牙科中的应用
- 医学成像分析 医学成像分析
- 牙周诊断 牙周诊断 牙周诊断
背景情况:
- 牙周炎的诊断依赖于临床检查和放射性评估.
- 全景放射图对于评估膜骨损失具有成本效益.
- 大型语言模型 (LLM) 在医学科学中越来越多地使用,一些模型现在正在处理视觉数据.
研究的目的:
- 评估ChatGPT模型 (4.5,o1,o3,o4-mini-high) 在测量剩余骨高度 (RBH) 和从全景放射图中计算牙的有效性.
- 将人工智能驱动的测量与牙科专业人员的评估进行比较.
主要方法:
- 分析了10张全景射线图,评估了RBH测量的ChatGPT模型中的246-271个近似位点.
- 三名牙科检查员对RBH的独立评估.
- 检查人员基于共识的牙计数.
- 将ChatGPT输出与人类评估进行比较.
主要成果:
- 聊天GPT 4.5,o3和o4-mini-high表明与牙计数的临床医生有实质性的协议 (κ = 0.65-0.69).
- 在牙计数方面,ChatGPT o1显示了中度的一致性 (κ = 0.52).
- 所有ChatGPT模型都始终高估了RBH值,ChatGPT 4.5显示了最低的平均偏差 (+11-12个百分点).
结论:
- 聊天GPT 4.5和o3在全景放射图上显示出牙计数的前景,但缺乏临床准确性.
- 目前的ChatGPT模型大大高估了RBH,使它们不适合牙周炎的分类.
- 在AI模型可靠地用于牙周诊断之前,需要进一步开发.
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