IILS: Intelligent imaging layout system for automatic imaging report standardization and intra-interdisciplinary clinical workflow optimizationResearch in context
Yang Wang,
Fangrong Yan,
Xiaofan Lu,
Guanming Zheng,
Xin Zhang,
Chen Wang,
Kefeng Zhou,
Yingwei Zhang,
Hui Li,
Qi Zhao,
Hu Zhu,
Fei Chen,
Cailiang Gao,
Zhao Qing,
Jing Ye,
Aijing Li,
Xiaoyan Xin,
Danyan Li,
Han Wang,
Hongming Yu,
Lu Cao,
Chaowei Zhao,
Rui Deng,
Libo Tan,
Yong Chen,
Lihua Yuan,
Zhuping Zhou,
Wen Yang,
Mingran Shao,
Xin Dou,
Nan Zhou,
Fei Zhou,
Yue Zhu,
Guangming Lu,
Bing Zhang
Affiliations
Yang Wang
Department of Radiology, the Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School, Nanjing 210008, China
Fangrong Yan
Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China
Xiaofan Lu
Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China
Guanming Zheng
Department of Statistics, University of Michigan, Ann arbor 48105, USA
Xin Zhang
Department of Radiology, the Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School, Nanjing 210008, China
Chen Wang
Department of Radiology, the Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School, Nanjing 210008, China
Kefeng Zhou
Department of Radiology, NanJing GaoChun People's Hospital, No.9 Chunzhong Road, GaoChun, NanJing, China
Yingwei Zhang
Department of Respiratory, the Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School, Nanjing 210008, China
Hui Li
Department of Respiratory, the Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School, Nanjing 210008, China
Qi Zhao
Department of Respiratory, the Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School, Nanjing 210008, China
Hu Zhu
College of Telecommunication and Information Engineering, Nanjing University of Posts and Telecommunications, No.66 Xin Mofan Road, Nanjing, China
Fei Chen
Department of Radiology, Affiliated Yancheng Hospital, School of Medicine, Southeast University, Yancheng, Jiangsu, China
Cailiang Gao
Department of Radiology, Chongqing Three Gorges Central Hospital, Chongqing 404000, China
Zhao Qing
Department of Radiology, the Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School, Nanjing 210008, China
Jing Ye
Department of Radiology, Northern Jiangsu People's Hospital, No.98 Nantong West Road, Yangzhou, Jiangsu 225001, China
Aijing Li
Department of Radiology, Ningbo No. 2 Hospital, No. 41, Xibei street, Haishu District 315010, Zhejiang, China
Xiaoyan Xin
Department of Radiology, the Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School, Nanjing 210008, China
Danyan Li
Department of Radiology, the Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School, Nanjing 210008, China
Han Wang
Department of Radiology, the Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School, Nanjing 210008, China
Hongming Yu
Department of Radiology, the Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School, Nanjing 210008, China
Lu Cao
FL 8, Ocean International Center E, Chaoyang Rd Side Rd, ShiLiPu, Chaoyang Qu, 100000 Beijing Shi, China
Chaowei Zhao
FL 8, Ocean International Center E, Chaoyang Rd Side Rd, ShiLiPu, Chaoyang Qu, 100000 Beijing Shi, China
Rui Deng
FL 8, Ocean International Center E, Chaoyang Rd Side Rd, ShiLiPu, Chaoyang Qu, 100000 Beijing Shi, China
Libo Tan
FL 8, Ocean International Center E, Chaoyang Rd Side Rd, ShiLiPu, Chaoyang Qu, 100000 Beijing Shi, China
Yong Chen
Department of Medical Administration, the Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School, Nanjing 210008, China
Lihua Yuan
Department of Radiology, the Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School, Nanjing 210008, China
Zhuping Zhou
Department of Radiology, the Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School, Nanjing 210008, China
Wen Yang
Department of Radiology, the Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School, Nanjing 210008, China
Mingran Shao
Department of Radiology, the Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School, Nanjing 210008, China
Xin Dou
Department of Radiology, the Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School, Nanjing 210008, China
Nan Zhou
Department of Radiology, the Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School, Nanjing 210008, China
Fei Zhou
Department of Radiology, the Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School, Nanjing 210008, China
Yue Zhu
Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China
Guangming Lu
Department of Medical Imaging, Jinling Hospital, School of Medicine, Nanjing University, Nanjing, 210002, Jiangsu, China
Bing Zhang
Department of Radiology, the Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School, Nanjing 210008, China; Corresponding author.
Background: To achieve imaging report standardization and improve the quality and efficiency of the intra-interdisciplinary clinical workflow, we proposed an intelligent imaging layout system (IILS) for a clinical decision support system-based ubiquitous healthcare service, which is a lung nodule management system using medical images. Methods: We created a lung IILS based on deep learning for imaging report standardization and workflow optimization for the identification of nodules. Our IILS utilized a deep learning plus adaptive auto layout tool, which trained and tested a neural network with imaging data from all the main CT manufacturers from 11,205 patients. Model performance was evaluated by the receiver operating characteristic curve (ROC) and calculating the corresponding area under the curve (AUC). The clinical application value for our IILS was assessed by a comprehensive comparison of multiple aspects. Findings: Our IILS is clinically applicable due to the consistency with nodules detected by IILS, with its highest consistency of 0·94 and an AUC of 90·6% for malignant pulmonary nodules versus benign nodules with a sensitivity of 76·5% and specificity of 89·1%. Applying this IILS to a dataset of chest CT images, we demonstrate performance comparable to that of human experts in providing a better layout and aiding in diagnosis in 100% valid images and nodule display. The IILS was superior to the traditional manual system in performance, such as reducing the number of clicks from 14·45 ± 0·38 to 2, time consumed from 16·87 ± 0·38 s to 6·92 ± 0·10 s, number of invalid images from 7·06 ± 0·24 to 0, and missing lung nodules from 46·8% to 0%. Interpretation: This IILS might achieve imaging report standardization, and improve the clinical workflow therefore opening a new window for clinical application of artificial intelligence. Fund: The National Natural Science Foundation of China. Keywords: Lung nodule, Artificial intelligence, Deep learning algorithms, Intelligent image layout system, Standardized e-film and visualized structured report, Clinical workflow