Govindarajan Arunkumar, Govindarajan Aarthi, Tanamala Swetha, Chattoraj Subhankar, Reddy Bhargava, Agrawal Rohitashva, Iyer Divya, Srivastava Anumeha, Kumar Pradeep, Putha Preetham
Aarthi Scans & Labs, Chennai 600026, India.
Qure.ai, Mumbai 400063, India.
Diagnostics (Basel). 2022 Nov 7;12(11):2724. doi: 10.3390/diagnostics12112724.
In medical practice, chest X-rays are the most ubiquitous diagnostic imaging tests. However, the current workload in extensive health care facilities and lack of well-trained radiologists is a significant challenge in the patient care pathway. Therefore, an accurate, reliable, and fast computer-aided diagnosis (CAD) system capable of detecting abnormalities in chest X-rays is crucial in improving the radiological workflow. In this prospective multicenter quality-improvement study, we have evaluated whether artificial intelligence (AI) can be used as a chest X-ray screening tool in real clinical settings. A team of radiologists used the AI-based chest X-ray screening tool (qXR) as a part of their daily reporting routine to report consecutive chest X-rays for this prospective multicentre study. This study took place in a large radiology network in India between June 2021 and March 2022. A total of 65,604 chest X-rays were processed during the study period. The overall performance of AI achieved in detecting normal and abnormal chest X-rays was good. The high negatively predicted value (NPV) of 98.9% was achieved. The AI performance in terms of area under the curve (AUC), NPV for the corresponding subabnormalities obtained were blunted CP angle (0.97, 99.5%), hilar dysmorphism (0.86, 99.9%), cardiomegaly (0.96, 99.7%), reticulonodular pattern (0.91, 99.9%), rib fracture (0.98, 99.9%), scoliosis (0.98, 99.9%), atelectasis (0.96, 99.9%), calcification (0.96, 99.7%), consolidation (0.95, 99.6%), emphysema (0.96, 99.9%), fibrosis (0.95, 99.7%), nodule (0.91, 99.8%), opacity (0.92, 99.2%), pleural effusion (0.97, 99.7%), and pneumothorax (0.99, 99.9%). Additionally, the turnaround time (TAT) decreased by about 40.63% from pre-qXR period to post-qXR period. The AI-based chest X-ray solution (qXR) screened chest X-rays and assisted in ruling out normal patients with high confidence, thus allowing the radiologists to focus more on assessing pathology on abnormal chest X-rays and treatment pathways.
在医学实践中，胸部X光检查是最常用的诊断成像检查。然而，目前大型医疗保健机构的工作量以及缺乏训练有素的放射科医生，是患者护理流程中的一项重大挑战。因此，一个能够检测胸部X光片中异常情况的准确、可靠且快速的计算机辅助诊断（CAD）系统，对于改善放射学工作流程至关重要。在这项前瞻性多中心质量改进研究中，我们评估了人工智能（AI）是否可以在实际临床环境中用作胸部X光筛查工具。一组放射科医生将基于人工智能的胸部X光筛查工具（qXR）作为日常报告流程的一部分，为这项前瞻性多中心研究报告连续的胸部X光片。本研究于2021年6月至2022年3月在印度的一个大型放射学网络中进行。在研究期间共处理了65604张胸部X光片。人工智能在检测正常和异常胸部X光片方面的整体表现良好。实现了98.9%的高阴性预测值（NPV）。人工智能在曲线下面积（AUC）方面的表现，以及针对相应亚异常情况获得的NPV分别为：钝圆的肋膈角（0.97，99.5%）、肺门畸形（0.86，99.9%）、心脏扩大（0.96，99.7%）、网状结节状阴影（0.91，99.9%）、肋骨骨折（0.98，99.9%）、脊柱侧弯（0.98，99.9%）、肺不张（0.96，99.9%）、钙化（0.96，99.7%）、实变（0.95，99.6%）、肺气肿（0.96，99.9%）、纤维化（0.95，99.7%）、结节（0.91，99.8%）、不透明阴影（0.92，99.2%）、胸腔积液（0.97，99.7%）和气胸（0.99，99.9%）。此外，周转时间（TAT）从qXR应用前到应用后减少了约40.63%。基于人工智能的胸部X光解决方案（qXR）对胸部X光片进行筛查，并协助高置信度地排除正常患者，从而使放射科医生能够更加专注于评估异常胸部X光片上的病变及治疗途径。
