Integration and evaluation of chest X-ray artificial intelligence in clinical practice

Koon-Pong Wong; Suzanne Y Homer; Sindy H Wei; Nazanin Yaghmai; Oscar A Estrada Paz; Timothy J Young; Russell G Buhr; Igor Barjaktarevic; Liza Shrestha; Morgan Daly; Jonathan Goldin; Dieter R Enzmann; Matthew S Brown

doi:10.1117/1.JMI.10.5.051805

Integration and evaluation of chest X-ray artificial intelligence in clinical practice

J Med Imaging (Bellingham). 2023 Sep;10(5):051805. doi: 10.1117/1.JMI.10.5.051805. Epub 2023 Apr 25.

Affiliations

¹ David Geffen School of Medicine at UCLA, Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, Los Angeles, California, United States.
² David Geffen School of Medicine at UCLA, Acute Care Imaging Section, Department of Radiological Sciences, Los Angeles, California, United States.
³ David Geffen School of Medicine at UCLA, Division of Pulmonary, Critical Care and Sleep Medicine, Los Angeles, California, United States.

Abstract

Purpose: To integrate and evaluate an artificial intelligence (AI) system that assists in checking endotracheal tube (ETT) placement on chest x-rays (CXRs) in clinical practice.

Approach: In clinical use over 17 months, 214 CXR images were ordered to check ETT placement with AI assistance by intensive care unit (ICU) physicians. The system was built on the SimpleMind Cognitive AI platform and integrated into a clinical workflow. It automatically identified the ETT and checked its placement relative to the trachea and carina. The ETT overlay and misplacement alert messages generated by the AI system were compared with radiology reports as the reference. A survey study was also conducted to evaluate usefulness of the AI system in clinical practice.

Results: The alert messages indicating that either the ETT was misplaced or not detected had a positive predictive value of 42% (21/50) and negative predictive value of 98% (161/164) based on the radiology reports. In the survey, radiologist and ICU physician users indicated that they agreed with the AI outputs and that they were useful.

Conclusions: The AI system performance in real-world clinical use was comparable to that seen in previous experiments. Based on this and physician survey results, the system can be deployed more widely at our institution, using insights gained from this evaluation to make further algorithm improvements and quality assurance of the AI system.

Keywords: artificial intelligence; chest x-ray; clinical translation; endotracheal tube; user survey.