Kidney Week

Abstract: SA-PO726

Artificial Intelligence Is Useful for Quantitative Analyses of Human Kidney Biopsy Images

Session Information

• Pathology and Lab Medicine: Clinical
  November 09, 2019 | Location: Exhibit Hall, Walter E. Washington Convention Center
  Abstract Time: 10:00 AM - 12:00 PM

Category: Pathology and Lab Medicine

• 1602 Pathology and Lab Medicine: Clinical

Authors

• Matsumoto, Ayumi, Osaka University Graduate School of Medicine, Suita, OSAKA-FU, Japan

Ayumi Matsumoto,

• Matsui, Isao, Osaka University Graduate School of Medicine, Suita, OSAKA-FU, Japan

Isao Matsui,

• Shimada, Karin, Graduate School of Medicine, Osaka University, Osaka, Osaka-Fu, Japan

Karin Shimada,

• Hashimoto, Nobuhiro, Osaka University Graduate School of Medicine, Suita, OSAKA-FU, Japan

Nobuhiro Hashimoto,

• Yasuda, Seiichi, Osaka University Graduate School of Medicine, Suita, OSAKA-FU, Japan

Seiichi Yasuda,

• Kajimoto, Sachio, Osaka university, Osaka, Japan

Sachio Kajimoto,

• Doi, Yohei, Osaka University Graduate School of Medicine, Suita, OSAKA-FU, Japan

Yohei Doi,

• Yamaguchi, Satoshi, Osaka University Graduate School of Medicine, Suita, OSAKA-FU, Japan

Satoshi Yamaguchi,

• Oka, Tatsufumi, Osaka University Graduate School of Medicine, Suita, OSAKA-FU, Japan

Tatsufumi Oka,

• Kubota, Keiichi, Osaka University Graduate School of Medicine, Suita, OSAKA-FU, Japan

Keiichi Kubota,

• Sakaguchi, Yusuke, Osaka University Graduate School of Medicine, Suita, OSAKA-FU, Japan

Yusuke Sakaguchi,

• Namba, Tomoko, Osaka University Graduate School of Medicine, Suita, OSAKA-FU, Japan

Tomoko Namba,

• Inoue, Kazunori, Osaka University Graduate School of Medicine, Suita, OSAKA-FU, Japan

Kazunori Inoue,

• Hamano, Takayuki, Osaka University Graduate School of Medicine, Suita, OSAKA-FU, Japan

Takayuki Hamano,

• Isaka, Yoshitaka, Osaka University Graduate School of Medicine, Suita, OSAKA-FU, Japan

Yoshitaka Isaka,

Background

The kidney biopsy is a valuable clinical examination that provides profound insight into the diagnosis, prognosis, and treatment of kidney diseases. However, appropriate quantitative analyses of the kidney biopsy images require trained nephrologists and pathologists. Artificial intelligence, an emerging technology in the field of computer science, may help analyses of pathology images.

Methods

PAS, PAM, or Masson-stained human kidney biopsy samples were obtained from four hospitals in Japan. The kidney biopsy images were digitalized by virtual slide system. Convolutional neural network (CNN), a deep neural network for computer vision, was trained to segment the kidney biopsy images.

Results

The CNN was almost well trained to identify glomerular and global sclerosis regions in various kidney diseases. However, Kimmelstiel-Wilson nodules were tended to be identified as global sclerosis. The CNN could also segment kidney biopsy images into intact tubulointerstitium, mild expansion of interstitial matrix, severe expansion of interstitial matrix, inflammation, arteries, and capsule of the kidney. Because PAS, PAM, and Masson-staining techniques were slightly different among the four hospitals, the training of the CNN required data from all four hospitals to obtain better accuracy of segmentation.

Conclusion

The artificial intelligence could segment human kidney biopsy images. The segmentation by artificial intelligence can provide novel quantitative methods for the analyses of human kidney biopsy images.

Segmentation of kidney biopsy images by artificial intelligence