Shah Henil P, Naqvi Agha Sah, Rajput Parth, Ambra Hanan, Venkatesh Harrini, Saleem Junaid, Saravanan Sudarshan, Wanjari Mayur, Mittal Gaurav
GMERS Medical College, Gujarat, India.
Pakistan Institute of Medical Science, Islamabad, Pakistan.
Narra J. 2025 Apr;5(1):e1361. doi: 10.52225/narra.v5i1.1361. Epub 2025 Mar 5.
Ground-glass opacities (GGOs) are hazy opacities on chest computed tomography (CT) scans that can indicate various lung diseases, including early COVID-19, pneumonia, and lung cancer. Artificial intelligence (AI) is a promising tool for analyzing medical images, such as chest CT scans. The aim of this study was to evaluate AI models' performance in detecting GGO nodules using metrics like accuracy, sensitivity, specificity, F1 score, area under the curve (AUC) and precision. We designed a search strategy to include reports focusing on deep learning algorithms applied to high-resolution CT scans. The search was performed on PubMed, Google Scholar, Scopus, and ScienceDirect to identify studies published between 2016 and 2024. Quality appraisal of included studies was conducted using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool, assessing the risk of bias and applicability concerns across four domains. Two reviewers independently screened studies reporting the diagnostic ability of AI-assisted CT scans in early GGO detection, where the review results were synthesized qualitatively. Out of 5,247 initially identified records, we found 18 studies matching the inclusion criteria of this study. Among evaluated models, DenseNet achieved the highest accuracy of 99.48%, though its sensitivity and specificity were not reported. WOANet showed an accuracy of 98.78%, with a sensitivity of 98.37% and high specificity of 99.19%, excelling particularly in specificity without compromising sensitivity. In conclusion, AI models can potentially detect GGO on chest CT scans. Future research should focus on developing hybrid models that integrate various AI approaches to improve clinical applicability.
磨玻璃影（GGOs）是胸部计算机断层扫描（CT）上的模糊阴影，可提示包括早期新冠病毒病、肺炎和肺癌在内的各种肺部疾病。人工智能（AI）是分析医学影像（如胸部CT扫描）的一种很有前景的工具。本研究的目的是使用准确率、灵敏度、特异性、F1分数、曲线下面积（AUC）和精确率等指标评估人工智能模型在检测磨玻璃结节方面的性能。我们设计了一种检索策略，纳入聚焦于应用于高分辨率CT扫描的深度学习算法的报告。检索在PubMed、谷歌学术、Scopus和ScienceDirect上进行，以识别2016年至2024年发表的研究。使用诊断准确性研究质量评估2（QUADAS - 2）工具对纳入研究进行质量评估，评估四个领域的偏倚风险和适用性问题。两名 reviewers 独立筛选报告人工智能辅助CT扫描在早期磨玻璃影检测中诊断能力的研究，对 review 结果进行定性综合分析。在最初识别的5247条记录中，我们发现18项研究符合本研究的纳入标准。在评估的模型中，DenseNet的准确率最高，为99.48%，不过其灵敏度和特异性未报告。WOANet的准确率为98.78%，灵敏度为98.37%，特异性高达99.19%，尤其在不影响灵敏度的情况下特异性表现出色。总之，人工智能模型有可能在胸部CT扫描上检测到磨玻璃影。未来的研究应专注于开发整合各种人工智能方法的混合模型，以提高临床适用性。
