Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images.

Toshiaki Hirasawa, Kazuharu Aoyama, Tetsuya Tanimoto, Soichiro Ishihara, Satoki Shichijo, Tsuyoshi Ozawa, Tatsuya Ohnishi, Mitsuhiro Fujishiro, Keigo Matsuo, Junko Fujisaki, Tomohiro Tada

July 2018 Gastric Cancer

Synopsis of Social media discussions

Discussions show strong agreement and high interest, with mentions of CNN applications for gastric cancer detection and improvements in diagnostic efficiency, using words like 'potential' and 'innovative.' Participants explore both technological methods and clinical implications, indicating meaningful engagement.

A
Agreement
 Moderate agreement

Most discussions support the article's findings, emphasizing the effectiveness of AI in medical diagnosis.

I
Interest
 High level of interest

Users demonstrate high interest by referencing specific techniques like CNNs and their clinical implications.

E
Engagement
 Moderate level of engagement

Participants engage with details of the methods and potential clinical impact, indicating a moderate depth of discussion.

I
Impact
 Moderate level of impact

The overall tone suggests the publication could influence future medical practices, highlighting its relevance.

Social Mentions

YouTube

6 Videos

Twitter

10 Posts

News

2 Articles

Metrics

Video Views

8,526

Total Likes

60

Extended Reach

52,334

Social Features

18

Timeline: Posts about article

Top Social Media Posts

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  • AI Medical Service Inc. / 株式会社AIメディカルサービス
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    Application of artificial intelligence using a convolutional neural network for detecting cholesteatoma in endoscopic enhanced images https://t.co/fhObBJ5cav #DL #AI #ML #DeepLearning #ArtificialIntelligence #MachineLearning #ComputerVision #AutonomousVehicles #NeuroMorphic
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    RT @MlOncology: Application of artificial intelligence using a convolutional neural network for diagnosis of early gastric cancer based on…
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    Application of artificial intelligence using a convolutional neural network for diagnosis of early gastric cancer based on magnifying endoscopy with narrow-band imaging https://t.co/haiIWWHfl9
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    July 19, 2020

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Abstract Synopsis

  • The study developed a convolutional neural network (CNN) based on the Single Shot MultiBox Detector architecture to automatically identify gastric cancer in endoscopic images, training it with over 13,500 images and testing it on nearly 2,300 images from patients.
  • The CNN achieved a high sensitivity of 92.2%, correctly detecting 70 out of 77 gastric cancer lesions, especially larger and invasive cancers, but also produced some false positives, mainly misidentifying gastritis as cancer.
  • The system processed images quickly (about 0.47 seconds for the test set) and demonstrated potential for clinical use by assisting endoscopists in diagnosing gastric cancer efficiently, thus possibly reducing their workload.