Role of an Automated Deep Learning Algorithm for Reliable Screening of Abnormality in Chest Radiographs: A Prospective Multicenter Quality Improvement Study

Abstract

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. Methods: 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. Results: 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. Conclusion: 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.

Commercially available artificial intelligence tools for fracture detection: the evidence

“…Predominantly, the AI products reviewed had evidence for their performance provided by the vendor for conformity certification, with 7 having independent, peer reviewed publications available (total publications = 18), with the greatest number originating from Gleamer ( n = 8). The majority of the evidence levels for AI product performance were at Level 3 (ie, change in diagnosis with and without AI assistance) and with some products (eg, BoneView Gleamer , 36 , 38 , 39 Annalise Enterprise CXR v1.2 Annalise.AI , 46 Qure.AI qXR 48 ) potentially demonstrating evidence at Level 4 (ie, demonstrating improvement in time for diagnosis, which could be argued may lead to swifter treatment or follow-up for the patient 38 ). There was no evidence available to demonstrate a benefit in actual patient outcome (eg, reduced time for recovery, reduction in repeated hospital visits, etc.…”

Revolutionizing Healthcare: Qure.AI's Innovations in Medical Diagnosis and Treatment

“…Table 1 shows different clinical studies of the effectiveness of Qure.AI, considered as the main evidence base for this review, as these studies demonstrate the use of AI in the health field and its respective results [9][10][11][12][13][14]. As demonstrated in Table 1, numerous international studies highlight the effectiveness of using Qure.AI's qXR in the healthcare field.…”

Validation of a Deep Learning Chest X-ray Interpretation Model: Integrating Large-Scale AI and Large Language Models for Comparative Analysis with ChatGPT

“…A model for automatic diagnosis of different diseases based on chest radiographs using machine learning algorithms has been proposed [ 11 ]. In a multicenter study, AI was used as a chest X-ray screening tool and achieved good performance in detecting normal and abnormal chest X-rays, reducing turnaround time, and assisting radiologists in assessing pathology [ 12 ]. AI solutions for chest X-ray evaluation have been demonstrated to be practical, perform well, and provide benefits in clinical settings [ 13 ].…”