• Yukitaka Nimura, Takayuki Kitasaka, Hirotoshi Honma, Hirotsugu Takabatake, Masaki Mori, Hiroshi Natori, and Kensaku Mori.
• Information and Communications Headquarters, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi 464-8601, Japan. ynimura@mori.m.is.nagoya-u.ac.jp
• Int J Comput Assist Radiol Surg. 2013 May 1; 8 (3): 353-63.

PurposeChronic obstructive pulmonary disease (COPD) is characterized by airflow limitations. Physicians frequently assess the stage using pulmonary function tests and chest CT images. This paper describes a novel method to assess COPD severity by combining measurements of pulmonary function tests (PFT) and the results of chest CT image analysis.MethodsThe proposed method utilizes measurements from PFTs and chest CT scans to assess COPD severity. This method automatically classifies COPD severity into five stages, described in GOLD guidelines, by a multi-class AdaBoost classifier. The classifier utilizes 24 measurements as feature values, which include 18 measurements from PFTs and six measurements based on chest CT image analysis. A total of 3 normal and 46 abnormal (COPD) examinations performed in adults were evaluated using the proposed method to test its diagnostic capability.ResultsThe experimental results revealed that its accuracy rates were 100.0 % (resubstitution scheme) and 53.1 % (leave-one-out scheme). A total of 95.7 % of missed classifications were assigned in the neighboring severities.ConclusionsThese results demonstrate that the proposed method is a feasible means to assess COPD severity. A much larger sample size will be required to establish the limits of the method and provide clinical validation.

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