利用基于GPT的视觉语言模型生成牙槽囊肿的诊断发现：一种构建具有结构化输出（SLSO）框架的两阶段自校正循环的初步研究
Generating Findings for Jaw Cysts in Dental Panoramic Radiographs Using a GPT-Based VLM: A Preliminary Study on Building a Two-Stage Self-Correction Loop with Structured Output (SLSO) Framework
AI总结：
本文提出SLSO框架，通过整合图像分析、结构化数据生成等步骤，提升牙槽囊肿的AI诊断准确性。实验显示SLSO在牙数识别、牙移动检测等方面优于传统方法，但对多牙病变的识别仍有局限。
摘要：
视觉语言模型（VLMs）如GPT在医学影像解读中展现出潜力，但在临床实践中生成可靠的放射学发现仍面临挑战，尤其在牙科病理学中。本研究提出一种自校正循环与结构化输出（SLSO）框架作为综合处理方法，以提高牙槽囊肿在牙科全景影像中的AI诊断准确性。利用带有牙槽囊肿的牙科全景影像实施包含图像分析、结构化数据生成、牙齿编号提取、一致性检查和迭代再生的10步综合处理框架。该框架作为GPT输出的外部验证机制。性能在七个评估项目上与传统链式思维（CoT）方法进行比较：透明度、内部结构、边界、根吸收、牙齿移动、与其他结构的关系以及牙齿编号。SLSO框架在多个项目上比CoT方法提高了输出准确性，最显著的改进出现在牙齿编号识别、牙齿移动检测和根吸收评估中。在成功案例中，经过最多五次再生后可以实现一致的结构化输出。该框架强制明确的负发现描述并抑制幻觉，尽管对跨越多牙的广泛病变的准确识别仍有限。本研究确立了所提出综合处理方法的可行性，并为未来使用更大、更多样化的数据集进行验证研究奠定了基础。
英文摘要：
Vision-language models (VLMs) such as GPT (Generative Pre-Trained Transformer) have shown potential for medical image interpretation; however, challenges remain in generating reliable radiological findings in clinical practice, as exemplified by dental pathologies. This study proposes a Self-correction Loop with Structured Output (SLSO) framework as an integrated processing methodology to enhance the accuracy and reliability of AI-generated findings for jaw cysts in dental panoramic radiographs. Dental panoramic radiographs with jaw cysts were used to implement a 10-step integrated processing framework incorporating image analysis, structured data generation, tooth number extraction, consistency checking, and iterative regeneration. The framework functioned as an external validation mechanism for GPT outputs. Performance was compared against the conventional Chain-of-Thought (CoT) method across seven evaluation items: transparency, internal structure, borders, root resorption, tooth movement, relationships with other structures, and tooth number. The SLSO framework improved output accuracy for multiple items compared to the CoT method, with the most notable improvements observed in tooth number identification, tooth movement detection, and root resorption assessment. In successful cases, consistently structured outputs were achieved after up to five regenerations. The framework enforced explicit negative finding descriptions and suppressed hallucinations, although accurate identification of extensive lesions spanning multiple teeth remained limited. This investigation established the feasibility of the proposed integrated processing methodology and provided a foundation for future validation studies with larger, more diverse datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
