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, 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 pulmon...
| Main Authors: | , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2020-04-01
|
| Series: | EBioMedicine |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352396420300992 |
| _version_ | 1818030136328454144 |
|---|---|
| author | Yang Wang Xiaofan Lu Yingwei Zhang Xin Zhang Kun Wang Jiani Liu Xin Li Renfang Hu Xiaolin Meng Shidan Dou Huayin Hao Xiaofen Zhao Wei Hu Cheng Li Yaozong Gao Zhishun Wang Guangming Lu Fangrong Yan Bing Zhang |
| author_facet | Yang Wang Xiaofan Lu Yingwei Zhang Xin Zhang Kun Wang Jiani Liu Xin Li Renfang Hu Xiaolin Meng Shidan Dou Huayin Hao Xiaofen Zhao Wei Hu Cheng Li Yaozong Gao Zhishun Wang Guangming Lu Fangrong Yan Bing Zhang |
| author_sort | Yang Wang |
| collection | DOAJ |
| description | 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±0·02 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. Funding: The National Natural Science Foundation of China. Keywords: Artificial intelligence, Computed tomography, Automatic pulmonary scanning, Interstitial lung disease, Radiation exposure |
| first_indexed | 2024-12-10T05:30:47Z |
| format | Article |
| id | doaj.art-4a0bd25af07e47888cc69aba87130c7b |
| institution | Directory Open Access Journal |
| issn | 2352-3964 |
| language | English |
| last_indexed | 2024-12-10T05:30:47Z |
| publishDate | 2020-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | EBioMedicine |
| spelling | doaj.art-4a0bd25af07e47888cc69aba87130c7b2022-12-22T02:00:33ZengElsevierEBioMedicine2352-39642020-04-0154Precise pulmonary scanning and reducing medical radiation exposure by developing a clinically applicable intelligent CT system: Toward improving patient careYang Wang0Xiaofan Lu1Yingwei Zhang2Xin Zhang3Kun Wang4Jiani Liu5Xin Li6Renfang Hu7Xiaolin Meng8Shidan Dou9Huayin Hao10Xiaofen Zhao11Wei Hu12Cheng Li13Yaozong Gao14Zhishun Wang15Guangming Lu16Fangrong Yan17Bing Zhang18Department of Radiology, The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School, Nanjing 210008, PR ChinaState Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, PR China; Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing 210009, PR ChinaDepartment of Respiratory, The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School, Nanjing 210008, PR ChinaDepartment of Radiology, The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School, Nanjing 210008, PR ChinaDepartment of Radiology, The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School, Nanjing 210008, PR ChinaDepartment of Radiology, The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School, Nanjing 210008, PR ChinaDepartment of Radiology, The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School, Nanjing 210008, PR ChinaDepartment of Research Center of CT BU, Shanghai United Imaging Healthcare Co., Ltd., 2258 Chengbei Rd., Jiading District, Shanghai 201800, PR ChinaDepartment of Research Center of CT BU, Shanghai United Imaging Healthcare Co., Ltd., 2258 Chengbei Rd., Jiading District, Shanghai 201800, PR ChinaDepartment of Research Center of CT BU, Shanghai United Imaging Healthcare Co., Ltd., 2258 Chengbei Rd., Jiading District, Shanghai 201800, PR ChinaDepartment of Research Center of CT BU, Shanghai United Imaging Healthcare Co., Ltd., 2258 Chengbei Rd., Jiading District, Shanghai 201800, PR ChinaDepartment of Research Center of CT BU, Shanghai United Imaging Healthcare Co., Ltd., 2258 Chengbei Rd., Jiading District, Shanghai 201800, PR ChinaDepartment of Research Center of CT BU, Shanghai United Imaging Healthcare Co., Ltd., 2258 Chengbei Rd., Jiading District, Shanghai 201800, PR ChinaDepartment of Research Center of CT BU, Shanghai United Imaging Healthcare Co., Ltd., 2258 Chengbei Rd., Jiading District, Shanghai 201800, PR ChinaDepartment of Research Center of CT BU, Shanghai United Imaging Healthcare Co., Ltd., 2258 Chengbei Rd., Jiading District, Shanghai 201800, PR ChinaDepartment of Psychiatry and Translational Imaging, Vagelos College of Physicians and Surgeons, Columbia University, New York 10032, United StatesDepartment of Medical Imaging, Jinling Hospital, School of Medicine, Nanjing University, Nanjing 210002, PR ChinaState Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, PR China; Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing 210009, PR China; Corresponding author at: Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, No. 639 Longmian Avenue, Nanjing 210009, PR China.Department of Radiology, The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School, Nanjing 210008, PR China; Corresponding author.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±0·02 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. Funding: The National Natural Science Foundation of China. Keywords: Artificial intelligence, Computed tomography, Automatic pulmonary scanning, Interstitial lung disease, Radiation exposurehttp://www.sciencedirect.com/science/article/pii/S2352396420300992 |
| spellingShingle | Yang Wang Xiaofan Lu Yingwei Zhang Xin Zhang Kun Wang Jiani Liu Xin Li Renfang Hu Xiaolin Meng Shidan Dou Huayin Hao Xiaofen Zhao Wei Hu Cheng Li Yaozong Gao Zhishun Wang Guangming Lu Fangrong Yan Bing Zhang Precise pulmonary scanning and reducing medical radiation exposure by developing a clinically applicable intelligent CT system: Toward improving patient care EBioMedicine |
| title | Precise pulmonary scanning and reducing medical radiation exposure by developing a clinically applicable intelligent CT system: Toward improving patient care |
| title_full | Precise pulmonary scanning and reducing medical radiation exposure by developing a clinically applicable intelligent CT system: Toward improving patient care |
| title_fullStr | Precise pulmonary scanning and reducing medical radiation exposure by developing a clinically applicable intelligent CT system: Toward improving patient care |
| title_full_unstemmed | Precise pulmonary scanning and reducing medical radiation exposure by developing a clinically applicable intelligent CT system: Toward improving patient care |
| title_short | Precise pulmonary scanning and reducing medical radiation exposure by developing a clinically applicable intelligent CT system: Toward improving patient care |
| title_sort | precise pulmonary scanning and reducing medical radiation exposure by developing a clinically applicable intelligent ct system toward improving patient care |
| url | http://www.sciencedirect.com/science/article/pii/S2352396420300992 |
| work_keys_str_mv | AT yangwang precisepulmonaryscanningandreducingmedicalradiationexposurebydevelopingaclinicallyapplicableintelligentctsystemtowardimprovingpatientcare AT xiaofanlu precisepulmonaryscanningandreducingmedicalradiationexposurebydevelopingaclinicallyapplicableintelligentctsystemtowardimprovingpatientcare AT yingweizhang precisepulmonaryscanningandreducingmedicalradiationexposurebydevelopingaclinicallyapplicableintelligentctsystemtowardimprovingpatientcare AT xinzhang precisepulmonaryscanningandreducingmedicalradiationexposurebydevelopingaclinicallyapplicableintelligentctsystemtowardimprovingpatientcare AT kunwang precisepulmonaryscanningandreducingmedicalradiationexposurebydevelopingaclinicallyapplicableintelligentctsystemtowardimprovingpatientcare AT jianiliu precisepulmonaryscanningandreducingmedicalradiationexposurebydevelopingaclinicallyapplicableintelligentctsystemtowardimprovingpatientcare AT xinli precisepulmonaryscanningandreducingmedicalradiationexposurebydevelopingaclinicallyapplicableintelligentctsystemtowardimprovingpatientcare AT renfanghu precisepulmonaryscanningandreducingmedicalradiationexposurebydevelopingaclinicallyapplicableintelligentctsystemtowardimprovingpatientcare AT xiaolinmeng precisepulmonaryscanningandreducingmedicalradiationexposurebydevelopingaclinicallyapplicableintelligentctsystemtowardimprovingpatientcare AT shidandou precisepulmonaryscanningandreducingmedicalradiationexposurebydevelopingaclinicallyapplicableintelligentctsystemtowardimprovingpatientcare AT huayinhao precisepulmonaryscanningandreducingmedicalradiationexposurebydevelopingaclinicallyapplicableintelligentctsystemtowardimprovingpatientcare AT xiaofenzhao precisepulmonaryscanningandreducingmedicalradiationexposurebydevelopingaclinicallyapplicableintelligentctsystemtowardimprovingpatientcare AT weihu precisepulmonaryscanningandreducingmedicalradiationexposurebydevelopingaclinicallyapplicableintelligentctsystemtowardimprovingpatientcare AT chengli precisepulmonaryscanningandreducingmedicalradiationexposurebydevelopingaclinicallyapplicableintelligentctsystemtowardimprovingpatientcare AT yaozonggao precisepulmonaryscanningandreducingmedicalradiationexposurebydevelopingaclinicallyapplicableintelligentctsystemtowardimprovingpatientcare AT zhishunwang precisepulmonaryscanningandreducingmedicalradiationexposurebydevelopingaclinicallyapplicableintelligentctsystemtowardimprovingpatientcare AT guangminglu precisepulmonaryscanningandreducingmedicalradiationexposurebydevelopingaclinicallyapplicableintelligentctsystemtowardimprovingpatientcare AT fangrongyan precisepulmonaryscanningandreducingmedicalradiationexposurebydevelopingaclinicallyapplicableintelligentctsystemtowardimprovingpatientcare AT bingzhang precisepulmonaryscanningandreducingmedicalradiationexposurebydevelopingaclinicallyapplicableintelligentctsystemtowardimprovingpatientcare |