Department of Medical Imaging, Royal University Hospital, 7235University of Saskatchewan, Saskatoon, Canada.
Can Assoc Radiol J. 2021 Feb;72(1):60-72. doi: 10.1177/0846537120941671. Epub 2020 Aug 6.
Artificial intelligence (AI) presents a key opportunity for radiologists to improve quality of care and enhance the value of radiology in patient care and population health. The potential opportunity of AI to aid in triage and interpretation of conventional radiographs (X-ray images) is particularly significant, as radiographs are the most common imaging examinations performed in most radiology departments. Substantial progress has been made in the past few years in the development of AI algorithms for analysis of chest and musculoskeletal (MSK) radiographs, with deep learning now the dominant approach for image analysis. Large public and proprietary image data sets have been compiled and have aided the development of AI algorithms for analysis of radiographs, many of which demonstrate accuracy equivalent to radiologists for specific, focused tasks. This article describes (1) the basis for the development of AI solutions for radiograph analysis, (2) current AI solutions to aid in the triage and interpretation of chest radiographs and MSK radiographs, (3) opportunities for AI to aid in noninterpretive tasks related to radiographs, and (4) considerations for radiology practices selecting AI solutions for radiograph analysis and integrating them into existing IT systems. Although comprehensive AI solutions across modalities have yet to be developed, institutions can begin to select and integrate focused solutions which increase efficiency, increase quality and patient safety, and add value for their patients.
人工智能 (AI) 为放射科医生提供了一个重要的机会，可以提高医疗质量，并增强放射科在患者护理和人群健康方面的价值。人工智能在辅助常规 X 射线 (X 光图像) 进行分诊和解读方面具有重要意义，因为 X 光检查是大多数放射科最常见的影像检查。在过去几年中，AI 算法在分析胸部和肌肉骨骼 (MSK) X 光片方面取得了重大进展，深度学习现在是图像分析的主要方法。已经编制了大量公共和专有图像数据集，并为 X 光片分析的 AI 算法的开发提供了帮助，其中许多算法在特定的、专注的任务上的准确性与放射科医生相当。本文描述了 (1) 开发 X 光片分析人工智能解决方案的基础，(2) 目前用于辅助胸部 X 光片和 MSK X 光片分诊和解读的 AI 解决方案，(3) AI 辅助 X 光片相关非解释性任务的机会，以及 (4) 放射科医生选择 X 光片分析的 AI 解决方案并将其集成到现有 IT 系统中时需要考虑的因素。尽管尚未开发出涵盖所有模式的全面 AI 解决方案，但各机构可以开始选择和整合重点解决方案，从而提高效率、提高质量和患者安全性，并为患者增加价值。
