Ming-Yue Wu1, Yong Li2, Bin-Jie Fu2, Guo-Shu Wang2, Zhi-Gang Chu2, Dan Deng1. 1. School of Public Health and Management, Chongqing Medical University, Chongqing, China. 2. Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Abstract
PURPOSE: This study aims to evaluate the performance of four artificial intelligence-aided diagnostic systems in identifying and measuring four types of pulmonary nodules. METHODS: Four types of nodules were implanted in a commercial lung phantom. The phantom was scanned with multislice spiral computed tomography, after which four systems (A, B, C, D) were used to identify the nodules and measure their volumes. RESULTS: The relative volume error (RVE) of system A was the lowest for all nodules, except for small ground glass nodules (SGGNs). System C had the smallest RVE for SGGNs, -0.13 (-0.56, 0.00). In the Bland-Altman test, only systems A and C passed the consistency test, P = 0.40. In terms of precision, the miss rate (MR) of system C was 0.00% for small solid nodules (SSNs), ground glass nodules (GGNs), and solid nodules (SNs) but 4.17% for SGGNs. The comparable system D MRs for SGGNs, SSNs, and GGNs were 71.30%, 25.93%, and 47.22%, respectively, the highest among all the systems. Receiver operating characteristic curve analysis indicated that system A had the best performance in recognizing SSNs and GGNs, with areas under the curve of 0.91 and 0.68. System C had the best performance for SGGNs (AUC = 0.91). CONCLUSION: Among four types nodules, SGGNs are the most difficult to recognize, indicating the need to improve higher accuracy and precision of artificial systems. System A most accurately measured nodule volume. System C was most precise in recognizing all four types of nodules, especially SGGN.
PURPOSE: This study aims to evaluate the performance of four artificial intelligence-aided diagnostic systems in identifying and measuring four types of pulmonary nodules. METHODS: Four types of nodules were implanted in a commercial lung phantom. The phantom was scanned with multislice spiral computed tomography, after which four systems (A, B, C, D) were used to identify the nodules and measure their volumes. RESULTS: The relative volume error (RVE) of system A was the lowest for all nodules, except for small ground glass nodules (SGGNs). System C had the smallest RVE for SGGNs, -0.13 (-0.56, 0.00). In the Bland-Altman test, only systems A and C passed the consistency test, P = 0.40. In terms of precision, the miss rate (MR) of system C was 0.00% for small solid nodules (SSNs), ground glass nodules (GGNs), and solid nodules (SNs) but 4.17% for SGGNs. The comparable system D MRs for SGGNs, SSNs, and GGNs were 71.30%, 25.93%, and 47.22%, respectively, the highest among all the systems. Receiver operating characteristic curve analysis indicated that system A had the best performance in recognizing SSNs and GGNs, with areas under the curve of 0.91 and 0.68. System C had the best performance for SGGNs (AUC = 0.91). CONCLUSION: Among four types nodules, SGGNs are the most difficult to recognize, indicating the need to improve higher accuracy and precision of artificial systems. System A most accurately measured nodule volume. System C was most precise in recognizing all four types of nodules, especially SGGN.
Authors: Patrick A Hein; Valentina C Romano; Patrik Rogalla; Christian Klessen; Alexander Lembcke; Lars Bornemann; Volker Dicken; Bernd Hamm; Hans-Christian Bauknecht Journal: J Digit Imaging Date: 2008-09-05 Impact factor: 4.056
Authors: Alexander A Bankier; Heber MacMahon; Jin Mo Goo; Geoffrey D Rubin; Cornelia M Schaefer-Prokop; David P Naidich Journal: Radiology Date: 2017-06-26 Impact factor: 11.105
Authors: Stefano Young; Hyun J Grace Kim; Moe Moe Ko; War War Ko; Carlos Flores; Michael F McNitt-Gray Journal: Med Phys Date: 2015-05 Impact factor: 4.071