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A fully automatic artificial intelligence–based CT image analysis system for accurate detection, diagnosis, and quantitative severity evaluation of pulmonary tuberculosis by Chenggong Yan; Lingfeng Wang; Jie Lin; Jun Xu; Tianjing Zhang; Jin Qi; Xiangying Li; Wei Ni; Guangyao Wu; Jianbin Huang; Yikai Xu; Henry C. Woodruff; Philippe Lambin is a Medicine article available to read on EtoBox.
What is A fully automatic artificial intelligence–based CT image analysis system for accurate detection, diagnosis, and quantitative severity evaluation of pulmonary tuberculosis about?
## Objectives: An accurate and rapid diagnosis is crucial for the appropriate treatment of pulmonary tuberculosis (tb). this study aims to develop an artificial intelligence (ai)-based fully automated ct image analysis system for detection, diagnosis, and burden quantification of pulmonary tb. ## Methods: From december 2007 to september 2020, 892 chest ct scans from pathogen-confirmed tb patients were retrospectively included. a deep learning-based cascading framework was connected to create a processing pipeline. for training and validation of the model, 1921 lesions were manually labeled, classified according to six categories of critical imaging features, and visually scored regarding lesion involvement as the ground truth. a "tb score" was calculated based on a network-activation map to quantitively assess the disease burden. independent testing datasets from two additional hospitals (dataset 2, n = 99; dataset 3, n = 86) and the nih tb portals (n = 171) were used to externally validate the performance of the ai model. ## Results: Ct scans of 526 participants (mean age, 48.5 ± 16.5 years; 206 women) were analyzed. the lung lesion detection subsystem yielded a mean average preci
Who reads A fully automatic artificial intelligence–based CT image analysis system for accurate detection, diagnosis, and quantitative severity evaluation of pulmonary tuberculosis?
It is typically read by researchers, students, and practitioners in Medicine.
- Author
- Chenggong Yan; Lingfeng Wang; Jie Lin; Jun Xu; Tianjing Zhang; Jin Qi; Xiangying Li; Wei Ni; Guangyao Wu; Jianbin Huang; Yikai Xu; Henry C. Woodruff; Philippe Lambin
- Publisher
- Springer Science and Business Media LLC
- Published
- 2021
- Language
- EN
- Field
- Medicine (Health Sciences)