Arrow
use cases

Predict Gastric Cancer

Gastric cancer prediction with Artificial Intelligence to improve accuracy and efficiency in early detection and diagnosis of gastric cancer.

Problem

Gastric cancer, also known as stomach cancer, persists as one of the main culprits of cancer-related mortality worldwide. Recognition often occurs in the later stages because early symptoms are confusing, along with the challenges inherent in identifying precancerous lesions using conventional diagnostic approaches. The pervasiveness of this serious health problem was evident in 2020, with an estimated incidence of 1,089,103 new cases along with 768,793 deaths attributable to gastric cancer (1)(2). Adding to this concern is the remarkably high incidence of precancerous conditions in people with known risk factors, requiring the development and implementation of more effective and generally accessible diagnostic techniques (3)(4).

Why it matters

  • Gastric cancer is often recognized in later stages due to confusing early symptoms and challenges in identifying precancerous lesions.
  • In 2020, there were an estimated 1,089,103 new cases and 768,793 deaths from gastric cancer.
  • There is a high incidence of precancerous conditions in people with known risk factors, necessitating better diagnostic techniques.

Solution

GastricHealthAI, tool to help doctors by providing real-time advice for gastric cancer based on current research and clinical guidelines.

User person: Gastroenterologists, Surgical Oncologists, Medical Oncologists, General Surgeons, Gastrointestinal Specialists, Endoscopists.

Discover more and interact with our AI!

Datasources

GastricHealthAI uses a clinical guideline from the National Comprehensive Cancer Network (NCCN) (5). This guideline provide evidence-based recommendations that inform the attendee's risk assessments and diagnostic suggestions.

Citations

  1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021 May;71(3):209-249. doi: 10.3322/caac.21660. Epub 2021 Feb 4. PMID: 33538338.
  2. Pannala R, Krishnan K, Melson J, Parsi MA, Schulman AR, Sullivan S, Trikudanathan G, Trindade AJ, Watson RR, Maple JT, Lichtenstein DR. Artificial intelligence in gastrointestinal endoscopy. VideoGIE. 2020 Nov 9;5(12):598-613. doi: 10.1016/j.vgie.2020.08.013. PMID: 33319126; PMCID: PMC7732722.
  3. Lee J, Lee H, Chung JW. The Role of Artificial Intelligence in Gastric Cancer: Surgical and Therapeutic Perspectives: A Comprehensive Review. J Gastric Cancer. 2023 Jul;23(3):375-387. doi: 10.5230/jgc.2023.23.e31. PMID: 37553126; PMCID: PMC10412973.
  4. Zhu SL, Dong J, Zhang C, Huang YB, Pan W. Application of machine learning in the diagnosis of gastric cancer based on noninvasive characteristics. PLoS One. 2020 Dec 31;15(12):e0244869. doi: 10.1371/journal.pone.0244869. PMID: 33382829; PMCID: PMC7775073.
  5. Ajani, J. A., D'Amico, T. A., Almhanna, K., Bentrem, D. J., Chao, J., Das, P., Denlinger, C. S., Fanta, P., Farjah, F., Fuchs, C. S., Gerdes, H., Gibson, M., Glasgow, R. E., Hayman, J. A., Hochwald, S. N., Hofstetter, W. L., Ilson, D. H., Jaroszewski, D., Johung, K. L., Keswani, R. N., ... Sundar, H. (2016). Gastric Cancer, Version 3.2016, NCCN Clinical Practice Guidelines in Oncology. Journal of the National Comprehensive Cancer Network, 14(10), 1286-1312. doi: 10.6004/jnccn.2016.0137

Book a Free Consultation

Trusted by the world's top healthcare institutions