Use of Artificial Intelligence to Reduce Radiation Exposure at Fluoroscopy-Guided Endoscopic Procedures

Abstract

OBJECTIVES: Exposure to ionizing radiation remains a hazard for patients and healthcare providers. We evaluated the utility of an artificial intelligence (AI)-enabled fluoroscopy system to minimize radiation exposure during image-guided endoscopic procedures. METHODS: We conducted a prospective study of 100 consecutive patients who underwent fluoroscopy-guided endoscopic procedures. Patients underwent interventions using either conventional or AI-equipped fluoroscopy system that uses ultrafast collimation to limit radiation exposure to the region of interest. The main outcome measure was to compare radiation exposure with patients, which was measured by dose area product. Secondary outcome was radiation scatter to endoscopy personnel measured using dosimeter. RESULTS: Of 100 patients who underwent procedures using traditional (n = 50) or AI-enabled (n = 50) fluoroscopy systems, there was no significant difference in demographics, body mass index, procedural type, and procedural or fluoroscopy time between the conventional and the AI-enabled fluoroscopy systems. Radiation exposure to patients was lower (median dose area product 2,178 vs 5,708 mGym2, P = 0.001) and scatter effect to endoscopy personnel was less (total deep dose equivalent 0.28 vs 0.69 mSv; difference of 59.4%) for AI-enabled fluoroscopy as compared to conventional system. On multivariate linear regression analysis, after adjusting for patient characteristics, procedural/fluoroscopy duration, and type of fluoroscopy system, only AI-equipped fluoroscopy system (coefficient 3,331.9 [95% confidence interval: 1,926.8–4,737.1, P < 0.001) and fluoroscopy duration (coefficient 813.2 [95% confidence interval: 640.5–985.9], P < 0.001) were associated with radiation exposure. DISCUSSION: The AI-enabled fluoroscopy system significantly reduces radiation exposure to patients and scatter effect to endoscopy personnel (see Graphical abstract, Supplementary Digital Content, http://links.lww.com/AJG/B461).

Artificial Intelligence and the Medical Physicist: Welcome to the Machine

“…ML has the potential to reduce imaging radiation exposure, which is a hazard for patients and workers, without penalizing image quality [152].…”

American Society for Gastrointestinal Endoscopy radiation and fluoroscopy safety in GI endoscopy

“…The radiation scatter was 59.4% less for the AIF system as compared with the conventional fluoroscopy system. 96 This is an important incremental development in the field, and it is anticipated that other fluoroscopy manufacturers will release their version of AIF-based systems in the future.…”

Wearable Dosimeters for Medical and Defence Applications: A State of the Art Review

“…AI has also been used to reduce the radiation exposure during Fluoroscopy‐ Guided Endoscopic procedures where X‐rays are made to pass through the human body. [ 70 ] A dataset from 100 patients was analyzed and the radiation exposure to patients and scatter effect to endoscopy personnel was much less in AI enabled fluoroscopy. A convolutional neural network (CNN) algorithm in deep learning and a visual imagery technique for recognizing ROI in fluoroscopy images was used.…”