You are entering HCP content only

Please note you are entering now content intended only for Healthcare Professionals.

Are you a HCP?

Yes I am a HCP No I am not a HCP
placeUnited Kingdom
Contact & Support
Medtronic / Covidien Products / Gastrointestinal Artificial Intelligence / Computer-Aided Detection. Analysis. Prediction

GI Genius™ Intelligent Endoscopy Module

Computer-Aided
Detection. Analysis. Prediction.

The GI Genius™ intelligent endoscopy module offers a transformative solution — powered by AI — to address the challenges of detecting potential signs of colorectal cancer, early.1

Speak with a representative

Artificial Intelligence. Real Advantage.

Colorectal cancer is the second most common cause of cancer-related deaths in Europe.2

With GI Genius™ on your endoscopy team, you will be able to detect more life-threatening cancerous lesions. This market-leading endoscopy AI technology has been shown to increase endoscopists’ adenoma detection rates (ADRs) by up to 14%.1

So it’s a proven partner in helping you to protect patients more effectively from colorectal cancer (CRC).1,3

Working with you to find the hard-to-find

Some potentially adenomatous lesions can be hard to detect, due to variations in their size and morphology. Polyps can often be located at the periphery of the visual field and mixed with potential distractors which are similar in appearance4. 

But detecting them is worth the effort.  

Each 1% increase in ADR actually decreases the risk of interval cancer by 3%3 – which is reassuring to you and your patients.

Real Evidence

Real Results

Team Performance

  • GI GeniusTM helps standardise performance across your team by reducing variation in physician performance1
  • It has the potential to reach in every colonoscopy the same level of accuracy that can be met only by a few very experienced physicians10

Polyp Characterisation

  • In a recent study, GI GeniusTM CADx in standard white-light endoscopy showed a 97.6% negative predictive value7 
  • This greatly exceeds the requirements for Leave-in-Situ strategies7
  • GI GeniusTM supports endoscopists to reach high confidence optical diagnosis7

Polyp Detection

  • GI GeniusTM alerts the endoscopist to difficult-to-detect lesion types, including flat lesions and diminutive polyps
  • This has helped boost ADR detection rates by up to 14%1 and reduce missed lesions by almost 50%5

Real integration

GI GeniusTM works how you work, integrating easily into your current department set up.9

It dovetails with your procedural workflow, without the need for any changes.9

Simply add the GI GeniusTM Intelligent Endoscopy Module to your existing endoscopy tower - it has demonstrated full compatibility with all major brands of endoscopy equipment.9

Speak with a representative

Real Usability

  • GI GeniusTM engages automatically when a polyp is framed consistently, then actively follows the lesion around the image frame10
  • When you inspect the lesion, GI GeniusTM automatically transitions to optical diagnosis, to enable characterisation10
  • Optical diagnosis is carried out in the same white light setting as polyp detection10
  • GI GeniusTM does not extend procedure time1

 

Real Expertise

Leading gastroenterologists talk about their work with GI GeniusTM.

Learn more how to integrate GI GeniusTM in the clinical practice.

Watch our on-demand webinar session.

Artificial Intelligence in Lower GI: Can AI deliver real value to your clinical practice today?

Faculty: 

  • Dr. Giulio Antonelli – Gastroenterologist - Moderator and Speaker 
  • Prof. Markus Ellrichmann – Gastroenterologist - Speaker

Watch now

References:

  1. Repici A, Badalamenti M, Maselli R, et al. Efficacy of Real-Time Computer-Aided Detection of Colorectal Neoplasia in a Randomized Trial. Gastroenterology (2020). https://doi.org/10.1053/j.gastro.2020.04.062
  2. Colorectal cancer factsheet in 2020 for EU-27 countries European cancer information system 2020. https://ecis.jrc.ec.europa.eu/pdf/factsheets/Colorectal_cancer_en-Mar_2021.pdf
  3. Corley DA, Jenson CD, Marks AR, et al. Adenoma Detection Rate and Risk of Colorectal Cancer and Death. NEJM 2014;370:1298-306.
  4. A. Cherubini and J.E. East, Gorilla in the room: Even experts can miss polyps at colonoscopy and how AI helps complex visual perception tasks, Digestive and Liver Disease, https://doi.org/10.1016/j.dld.2022.10.004
  5. Wallace M, Sharma P, Bhandari P, et al. Impact of Artificial Intelligence on Miss Rate of Colorectal Neoplasia Gastroenterology (2022).https://doi.org/10.1053/j.gastro.2022.03.007Clinical Evaluation Report. CB-17-08 System LQ20052607.
  6. Clinical Evaluation Report. CB-17-08 System LQ20052607.
  7. Hassan C, Balsamo G, Lorenzetti R, et al. Artificial intelligence for leaving in situ colorectal polyps – results of a realtime clinical trial, ClinicalGastroenterology and Hepatology (2022), doi: https://doi.org/10.1016/j.cgh.2022.04.045.
  8. Houwen B, Hassan C. Definition of competence standards for optical diagnosis of diminutive colorectal polyps: European Society of Gastrointestinal Endoscopy (ESGE) Position Statement, Endoscopy 2022; 54: 88–99.
  9. V and V data on file/usability engineering file.
  10. Biffi C, Salvagnini P, Dinh NN, Hassan C, Sharma P; GI Genius CADx Study Group, Cherubini A. A novel AI device for real-time optical characterization of colorectal polyps. NPJ Digit Med. 2022 Jun 30;5(1):84. doi: 10.1038/s41746-022-00633-6. Erratum in: NPJ Digit Med. 2022Aug 16;5(1):114. PMID: 35773468; PMCID: PMC9247164.
  11. Hassan, Cesare, et al. "New artificial intelligence system: first validation study versus experienced endoscopists for colorectal polyp detection." Gut 69.5 (2020): 799-800.
  12. Repici, A., Spadaccini, M., Antonelli, G., Correale, L., Maselli, R., Galtieri, P. A., ... & Hassan, C. (2022). Artificial intelligence and colonoscopy experience: lessons from two randomised trials. Gut, 71(4), 757-765.
  13. Troya, Joel, et al. "The influence of computer-aided polyp detection systems on reaction time for polyp detection and eye gaze." Endoscopy AAM (2022).
  14. Biscaglia, Giuseppe, et al. "Real-time, computer-aided, detection-assisted colonoscopy eliminates differences in adenoma detection rate between trainee and experienced endoscopists." Endoscopy International Open 10.05 (2022): E616-E62
  15. Zippelius, Carolin, et al. "Diagnostic accuracy of a novel artificial intelligence system for adenoma detection in daily practice: a prospective nonrandomized comparative study." Endoscopy 54.05 (2022): 465-472.
  16. Hassan, Cesare, et al. "Computer-aided detection-assisted colonoscopy: classification and relevance of false positives." Gastrointestinal endoscopy 92.4 (2020): 900-904.
  17. Hassan, C., Spadaccini, M., ... & Repici, A. (2021). Performance of artificial intelligence in colonoscopy for adenoma and polyp detection: a systematic review and metaanalysis. Gastrointestinal endoscopy, 93(1), 77-85.
  18. Spadaccini, M., Iannone, A., ... & Repici, A. (2021). Computer-aided detection versus advanced imaging for detection of colorectal neoplasia: a systematic review and network meta-analysis. The Lancet Gastroenterology & Hepatology, 6(10), 793-802.
  19. Deliwala, Smit S., et al. "Artificial intelligence (AI) real-time detection vs. routine colonoscopy for colorectal neoplasia: a meta-analysis and trial sequential analysis." International Journal of Colorectal Disease 36.11 (2021): 2291-230
  20. Reverberi, C., Rigon, T., Solari, A. et al. Experimental evidence of effective human–AI collaboration in medical decision-making. Sci Rep 12, 14952 (2022). https://doi.org/10.1038/s41598-022-18751-2
  21. Hassan C, Sharma P, Mori Y, Bretthauer M, Rex DK, COMBO Study Group, Comparative Performance of Artificial Intelligence Optical Diagnosis Systems for Leaving-In-Situ Colorectal Polyps, Gastroenterology (2022), doi: https://doi.org/10.1053/j.gastro.2022.10.021