Precise Pulmonary Scanning and Reducing Medical Radiation Exposure by Developing a Clinically Applicable Intelligent CT System: Towards Improving Patient Care

Background: Interstitial lung disease requires frequent re-examination, which directly causes excessive cumulative radiation exposure. To date, AI has not been applied to CT for enhancing clinical care; thus, we hypothesize AI 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±0·02mm with a success rate of 98·7% in training set and 2·23±0·10mm 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. Funding Statement: This work was supported by the National Natural Science Foundation of China (81720108022, 91649116, 81571040, 81973145), the Social Development Project of Science and Technology in Jiangsu Province (BE2016605, BE201707), the National Key R&D Program of China (2017YFC0112801), the Key Medical Talents of Jiangsu Province, the ‘13th Five-Year’ Health Promotion Project of Jiangsu Province (B.Z.2016-2020), the Jiangsu Provincial Key Medical Discipline (Laboratory) (ZDXKA2016020), the Project of the Sixth Peak of Talented People (WSN-138, BZ), the China Postdoctoral Science Foundation (2019M651805), the Fundamental Research Funds for the Central Universities grants of China (Grant No. 2632018FY04), the “Double First-Class” University project (CPU2018GY09). Declaration of Interests: The authors declare no competing interests. Ethics Approval Statement: This study was approved by the institutional review board of The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School. Institutional review board (IRB)/ethics committee approvals were obtained. This work was conducted in a manner compliant with the People's Republic of China Health Insurance Portability and Accountability Act and was adherent to the tenets of the Declaration of Helsinki. Patient consents were waived by IRB.