論文

査読有り 国際誌
2019年5月10日

A convolutional neural network-based system to prevent patient misidentification in FDG-PET examinations.

Scientific reports
  • Keisuke Kawauchi
  • ,
  • Kenji Hirata
  • ,
  • Chietsugu Katoh
  • ,
  • Seiya Ichikawa
  • ,
  • Osamu Manabe
  • ,
  • Kentaro Kobayashi
  • ,
  • Shiro Watanabe
  • ,
  • Sho Furuya
  • ,
  • Tohru Shiga

9
1
開始ページ
7192
終了ページ
7192
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1038/s41598-019-43656-y

Patient misidentification in imaging examinations has become a serious problem in clinical settings. Such misidentification could be prevented if patient characteristics such as sex, age, and body weight could be predicted based on an image of the patient, with an alert issued when a mismatch between the predicted and actual patient characteristic is detected. Here, we tested a simple convolutional neural network (CNN)-based system that predicts patient sex from FDG PET-CT images. This retrospective study included 6,462 consecutive patients who underwent whole-body FDG PET-CT at our institute. The CNN system was used for classifying these patients by sex. Seventy percent of the randomly selected images were used to train and validate the system; the remaining 30% were used for testing. The training process was repeated five times to calculate the system's accuracy. When images for the testing were given to the learned CNN model, the sex of 99% of the patients was correctly categorized. We then performed an image-masking simulation to investigate the body parts that are significant for patient classification. The image-masking simulation indicated the pelvic region as the most important feature for classification. Finally, we showed that the system was also able to predict age and body weight. Our findings demonstrate that a CNN-based system would be effective to predict the sex of patients, with or without age and body weight prediction, and thereby prevent patient misidentification in clinical settings.

リンク情報
DOI
https://doi.org/10.1038/s41598-019-43656-y
PubMed
https://www.ncbi.nlm.nih.gov/pubmed/31076620
PubMed Central
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6510755
ID情報
  • DOI : 10.1038/s41598-019-43656-y
  • PubMed ID : 31076620
  • PubMed Central 記事ID : PMC6510755

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