Precise pulmonary scanning and reducing medical radiation exposure by developing a clinically applicable intelligent CT system: Toward improving patient care

Background: Interstitial lung disease requires frequent re-examination, which directly causes excessive cumulative radiation exposure. To date, Al has not been applied to CT for enhancing clinical care; thus, we hypothesize Al may empower CT with intelligence to realize automatic and accurate pulmonary scanning, thus dramatically decrease medical radiation exposure without compromising patient care. Methods: Facial boundary detection was realized by recognizing adjacent jaw position through training and testing a region proposal network (RPN) on 76,882 human faces using a preinstalled 2-dimensional camera; the lung-fields was then segmented by V-Net on another training set with 314 subjects and calculated the moving distance of the scanning couch based on a pre-generated calibration table. A multi-cohort study, including 1,186 patients was used for validation and radiation dose quantification under three clinical scenarios. Findings: A U-HAPPY (United imaging Human Automatic Planbox for Pulmonary) scanning CT was designed. Error distance of RPN was 4.46 +/- 0.02 pixels with a success rate of 98.7% in training set and 2.23 +/- 0.10 pixels with 100% success rate in testing set. Average Dice's coefficient was 0.99 in training set and 0.96 in testing set. A calibration table with 1,344,000 matches was generated to support the linkage between camera and scanner. This real-time automation makes an accurate plan-box to cover exact location and area needed to scan, thus reducing amounts of radiation exposures significantly (all, P<0.001). Interpretation: U-HAPPY CT designed for pulmonary imaging acquisition standardization is promising for reducing patient risk and optimizing public health expenditures. (C) 2020 The Author(s). Published by Elsevier B.V.