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Development of a novel artificial intelligence algorithm to detect pulmonary nodules on chest radiography

Mitsunori Higuchi, Takeshi Nagata, Kohei Iwabuchi, Akira Sano, Hidemasa Maekawa, Takayuki Idaka, Manabu Yamasaki, Chihiro Seko, Atsushi Sato, Junzo Suzuki, Yoshiyuki Anzai, Takashi Yabuki, Takuro Saito, Hiroyuki Suzuki

Author information

  • Mitsunori Higuchi

    Department of Thoracic Surgery, Aizu Medical Center, Fukushima Medical University

  • Takeshi Nagata

    University of Tsukuba School of Integrative and Global Majors
    Mizuho Research and Technologies, Ltd.

  • Kohei Iwabuchi

    Mizuho Research and Technologies, Ltd.

  • Akira Sano

    Mizuho Research and Technologies, Ltd.

  • Hidemasa Maekawa

    Mizuho Research and Technologies, Ltd.

  • Takayuki Idaka

    Mizuho Research and Technologies, Ltd.

  • Manabu Yamasaki

    Mizuho Research and Technologies, Ltd.

  • Chihiro Seko

    Mizuho Research and Technologies, Ltd.

  • Atsushi Sato

    Fukushima Preservative Service Association of Health

  • Junzo Suzuki

    Fukushima Preservative Service Association of Health

  • Yoshiyuki Anzai

    Aizuwakamatsu Medical Association

  • Takashi Yabuki

    Aizuwakamatsu Medical Association

  • Takuro Saito

    Department of Surgery, Aizu Medical Center, Fukushima Medical University

  • Hiroyuki Suzuki

    Department of Chest Surgery, Fukushima Medical University School of Medicine

Abstract

Background: In this study, we aimed to develop a novel artificial intelligence (AI) algorithm to support pulmonary nodule detection, which will enable physicians to efficiently interpret chest radiographs for lung cancer diagnosis.


Methods: We analyzed chest X-ray images obtained from a health examination center in Fukushima and the National Institutes of Health (NIH) Chest X-ray 14 dataset. We categorized these data into two types: type A included both Fukushima and NIH datasets, and type B included only the Fukushima dataset. We also demonstrated pulmonary nodules in the form of a heatmap display on each chest radiograph and calculated the positive probability score as an index value.


Results: Our novel AI algorithms had a receiver operating characteristic (ROC) area under the curve (AUC) of 0.74, a sensitivity of 0.75, and a specificity of 0.60 for the type A dataset. For the type B dataset, the respective values were 0.79, 0.72, and 0.74. The algorithms in both the type A and B datasets were superior to the accuracy of radiologists and similar to previous studies.


Conclusions: The proprietary AI algorithms had a similar accuracy for interpreting chest radiographs when compared with previous studies and radiologists. Especially, we could train a high quality AI algorithm, even with our small type B data set. However, further studies are needed to improve and further validate the accuracy of our AI algorithm.

The cintent of reseach paper

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