The results of Olympus’ ongoing joint research program1 to create an AI-based pathology gastric cancer diagnostic tool with the potential to streamline pathologists’ workloads were announced at the recent Japan Society of Digital Pathology Study annual meeting. The diagnostic tool achieved 100% sensitivity2 and 50% or more specificity3 for all gastric biopsy pathology specimens analyzed.
Diagnostic Gastric Cancer Tool Demand
The ongoing shortage of pathologists has led to a demand for AI-based pathology diagnostic tools. Olympus, through its Office of Innovation*, began a collaboration with the Kure Medical Center and Chugoku Cancer Center in Japan in 2017 to develop an AI-based pathology diagnostic tool. In the initial testing phase4 , the AI was trained using 368 gastric cancer biopsy pathology slide images.
The second phase of research5 began in November 2020, where the diagnostic tool was expanded to six hospitals in Japan, with the aim of verifying the versatility and improving the accuracy of the AI tool. Specifically, it was important to test whether the tool works correctly on pathology slides that vary in thickness and color.
The goal of this program is to deliver AI pathology diagnosis software that can assist pathologists by 2023.
The AI-based pathology tool uses a convolutional neural network6 (CNN) optimized to analyze pathology images. This technology enables the tool to identify adenocarcinoma7 versus non-adenocarcinoma tissue in an image. Once the AI was trained, it was tested using 1200 pathology whole slide images from the six institutions participating in the study. The AI classified each image as either adenocarcinoma or non-adenocarcinoma. The AI tool was able to achieve 100% sensitivity and 50% or higher specificity for slides from all six facilities. The robustness of the results will enable Olympus to pursue commercialization of the AI tool in the future.
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Featured Image: Areas with a high probability of adenocarcinoma tissue are displayed in red, and areas with a low probability of adenocarcinoma tissue are displayed in blue. Photo: Olympus
1National Hospital Organization Kure Medical Center and Chugoku Cancer Center, National Hospital Organization Osaka National Hospital, National Hospital Organization Shikoku Cancer Center, National Hospital Organization Nagasaki Medical Center, Hiroshima Memorial Hospital, and Kure Medical Association Hospital.
2Percentage of positive samples diagnosed as positive.
3Percentage of negative samples diagnosed as negative.
4Learn more about the first phase https://www.olympus-global.com/news/2018/nr00869.html
5Learn more about the second phase https://www.olympus-global.com/news/2020/nr01948.html
6A method widely used as a deep-learning technology for image analysis. The features to be analyzed can be learned efficiently.
7One of the cancers. Most cancers in gastric biopsy are said to be adenocarcinoma.
*Collaboration with companies and academic organizations with advanced technologies under our X (cross) INNOVATION program.