Artificial intelligence-based detection and assessment of ascites on CT scans

Wang，Zheng1,3; Xiao，Ying2,6; Peng，Li1,3; Zhang，Zhuolin1,3; Li，Xiaojun4; Xue，Yang1,3; Zhang，Jie4; Zhang，Jianglin5,7,8

2023-08-15

10.1016/j.eswa.2023.119979

Expert Systems with Applications   Impact Factor & Quartile

0957-4174

Clinically important ascites are a result of multifactorial pathogenesis. Planning therapy merely depends on precisely detecting and quantitatively classifying ascites to minimize potential adverse effects. However, manually segmenting and quantifying ascites is time-consuming asascites typically appear on multiple CT scans. In this study, an AI-based approach (SRFLab) is developed to quantify ascites from CT scans automatically. First, abdomen sections are automatically acquired from the retrospectively screened CT volume using multitask classification (AcquNet). The proposed CNN is retrieved under a task-specific objective using transfer learning. Alternatively, ascites are learned from a supervision representation fusion CNN (QuanNet) to evaluate fluid formation. Experimental results demonstrate that the proposed schema leads to good performance compared to other existing methods. AcquNet achieved a mean accuracy of 97.80% ± and a 1.97% standard deviation, while the accuracy of QuanNet achieved a mean accuracy of 97.21% ± and a 2.61% standard deviation. Overall, the results of this study demonstrate the effectiveness of the proposed model and the advancement of the volumetric assessment of ascites on CT volume images. The proposed model is more efficient at detecting and quantifying ascites in patients than clinical experts. Thus, the proposed model can support the rapid grading of ascites on CT volume images and aid radiologists in clinical practice.

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ENGINEERING

2-s2.0-85152591999

Scopus

1.School of Computer Science,Hunan First Normal University,Changsha,410205,China
2.Gastroenterology Department of Xiangya Hospital,Central South University,Changsha,410008,China
3.Hunan Provincial Key Laboratory of Informationization Technology for Basic Education,Changsha,410205,China
4.Gastroenterology Department of the Second Xiangya Hospital,Central South University,Changsha,410011,China
5.Department of Dermatology,Shenzhen Peoples Hospital,The Second Clinical Medica College,Jinan University,The First Affiliated Hospital,Southern University of Science and Technology,Shenzhen,Guangdong,518020,China
6.Hunan International Scientific and Technological Cooperation Base of Artificial Intelligence. Computer Aided Diagnosis and Treatment for Digestive Disease,Changsha,410011,China
7.Candidate Branch of National Clinical Research Center for Skin Diseases,Shenzhen,Guangdong,518020,China
8.Department of Geriatrics,Shenzhen Peoples Hospital,The Second Clinical Medica College,Jinan University,The First Affiliated Hospital,Southern University of Science and Technology,Shenzhen,Guangdong,518020,China

Wang，Zheng,Xiao，Ying,Peng，Li,et al. Artificial intelligence-based detection and assessment of ascites on CT scans[J]. Expert Systems with Applications,2023,224.

Wang，Zheng.,Xiao，Ying.,Peng，Li.,Zhang，Zhuolin.,Li，Xiaojun.,...&Zhang，Jianglin.(2023).Artificial intelligence-based detection and assessment of ascites on CT scans.Expert Systems with Applications,224.

Wang，Zheng,et al."Artificial intelligence-based detection and assessment of ascites on CT scans".Expert Systems with Applications 224(2023).