Medical Systems Research & Development Center, FUJIFILM Corporation, Minato, Tokyo, Japan.
Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan.
PLoS One. 2024 Mar 21;19(3):e0300325. doi: 10.1371/journal.pone.0300325. eCollection 2024.
Worldwide, lung cancer is the leading cause of cancer-related deaths. To manage lung nodules, radiologists observe computed tomography images, review various imaging findings, and record these in radiology reports. The report contents should be of high quality and uniform regardless of the radiologist. Here, we propose an artificial intelligence system that automatically generates descriptions related to lung nodules in computed tomography images. Our system consists of an image recognition method for extracting contents-namely, bronchopulmonary segments and nodule characteristics from images-and a natural language processing method to generate fluent descriptions. To verify our system's clinical usefulness, we conducted an experiment in which two radiologists created nodule descriptions of findings using our system. Through our system, the similarity of the described contents between the two radiologists (p = 0.001) and the comprehensiveness of the contents (p = 0.025) improved, while the accuracy did not significantly deteriorate (p = 0.484).
在全球范围内，肺癌是癌症相关死亡的主要原因。为了管理肺结节，放射科医生观察计算机断层扫描图像，审查各种影像学发现，并将这些记录在放射科报告中。无论放射科医生是谁，报告内容都应具有高质量和统一性。在这里，我们提出了一种人工智能系统，该系统可以自动生成与计算机断层扫描图像中肺结节相关的描述。我们的系统由一种图像识别方法组成，用于从图像中提取内容，即支气管肺段和结节特征，以及一种自然语言处理方法来生成流畅的描述。为了验证我们系统的临床实用性，我们进行了一项实验，其中两名放射科医生使用我们的系统创建了结节发现的描述。通过我们的系统，两名放射科医生描述内容的相似性（p = 0.001）和内容的全面性（p = 0.025）得到了提高，而准确性并没有显著恶化（p = 0.484）。
