Validation of deep learning-based computer-aided detection software use for interpretation of pulmonary abnormalities on chest radiographs and examination of factors that influence readers’ performance and final diagnosis

Naoki Toda, Masahiro Hashimoto, Yu Iwabuchi, Misa Nagasaka, Ryo Takeshita, Minoru Yamada, Yoshitake Yamada, Masahiro Jinzaki

Research output: Contribution to journalArticlepeer-review

Abstract

Purpose: To evaluate the performance of a deep learning-based computer-aided detection (CAD) software for detecting pulmonary nodules, masses, and consolidation on chest radiographs (CRs) and to examine the effect of readers’ experience and data characteristics on the sensitivity and final diagnosis. Materials and methods: The CRs of 453 patients were retrospectively selected from two institutions. Among these CRs, 60 images with abnormal findings (pulmonary nodules, masses, and consolidation) and 140 without abnormal findings were randomly selected for sequential observer-performance testing. In the test, 12 readers (three radiologists, three pulmonologists, three non-pulmonology physicians, and three junior residents) interpreted 200 images with and without CAD, and the findings were compared. Weighted alternative free-response receiver operating characteristic (wAFROC) figure of merit (FOM) was used to analyze observer performance. The lesions that readers initially missed but CAD detected were stratified by anatomic location and degree of subtlety, and the adoption rate was calculated. Fisher’s exact test was used for comparison. Results: The mean wAFROC FOM score of the 12 readers significantly improved from 0.746 to 0.810 with software assistance (P = 0.007). In the reader group with < 6 years of experience, the mean FOM score significantly improved from 0.680 to 0.779 (P = 0.011), while that in the reader group with ≥ 6 years of experience increased from 0.811 to 0.841 (P = 0.12). The sensitivity of the CAD software and the adoption rate for the lesions with subtlety level 2 or 3 (obscure) lesions were significantly lower than for level 4 or 5 (distinct) lesions (50% vs. 93%, P < 0.001; and 55% vs. 74%, P = 0.04, respectively). Conclusion: CAD software use improved doctors’ performance in detecting nodules/masses and consolidation on CRs, particularly for non-expert doctors, by preventing doctors from missing distinct lesions rather than helping them to detect obscure lesions.

Original languageEnglish
JournalJapanese Journal of Radiology
DOIs
Publication statusAccepted/In press - 2022

Keywords

  • Chest radiography
  • Computer-aided detection
  • Deep learning
  • Multicenter study
  • Pulmonary lesions

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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