GI Genius™ intelligent endoscopy module.
GI Genius™ is the first-to-market, deep learning, computer-aided polyp detection system, able to detect polyps of different shapes, sizes and morphologies in real time.
Medtronic is the sole distributor,
marketer and owner of GI Genius trademarks.
Harnessing AI to detect the undetected.
Powered by Artificial Intelligence, the GI Genius™ intelligent endoscopy module offers a transformative solution to address challenges in colorectal screening. Integrating seamlessly with existing endoscopy processes, it harnesses deep learning algorithms to assist in clinical decision-making in real time, using visual markers to alert physicians to the presence of lesions.
Relative increase in ADR. 1
Reduction in missed polyps. 2
More likely to detect polyps in the distal colon. 1
The genesis of genius: what makes GI Genius™ special?
Game-changing.
GI Genius™ was designed to assist physicians in a truly transformative way. That’s why it earned a place in the FORTUNE 2022 ‘Change the World’ list.
Science-backed.
The development of GI Genius™ was driven from the outset by scientific investigation, ensuring its effectiveness and facilitating regulatory approval.
Data-based.
The deep learning algorithms of the GI Genius™ module are trained on a robust data-set of 13 million images of polyp of multiple shapes and sizes.
Human-centred.
When AI and humans work together, they are more effective than either is alone. That is why GI Genius™ is designed to assist physicians, not replace them.
SVP Science, AI and Data, Cosmo IMD
Per-lesion sensitivity. 3
Faster polyp recognition than the endoscopist. 3
False activations. 3
Video Gallery
Genius Academy Dr. Douglas Rex
It's easy and straightforward for our brains to utilize, [...] and it gets at something that is so fundamental to colonoscopy into cancer prevention, which is detection.
Genius Academy Dr. Tyler Berzin
I am pretty certain I would have missed that one centimeter sessile serrated lesion, [...] and I walked out of the room knowing that I had helped the patient more effectively using this technology.
Seamless integration.
Universal compatibility.
01.
Your existing endoscopy tower and high-definition endoscope is all you need to integrate with the GI Genius™ intelligent endoscopy module.
02.
GI Genius™ intelligent endoscopy module can be easily integrated with existing major brands of endoscopic processors.
03.
GI Genius™ intelligent endoscopy module simply connects to the existing endoscope, video processor, and display monitor.
04.
Turn on GI Genius™ intelligent endoscopy module and immediately experience the benefits of AI, without changing any part of your procedure.
Hardware and software designed for today, and ready for tomorrow.
Artificial Intelligence is resource intensive, and its applicability may evolve over time. That’s why the GI GeniusTM hardware platform is built to be both powerful and flexible, and it’s why the software that operates on it can be easily updated and upscaled. So GI Genius™ is ready for whatever opportunities the future may bring.
Scientific evidence
Polyp detection with colonoscopy assisted by the GI Genius artificial intelligence endoscopy module compared with standard colonoscopy in routine colonoscopy practice (COLO-DETECT): a multicentre, open-label, parallel-arm, pragmatic randomised controlled trial.
Seager A, Sharp L, Neilson LJ, Brand A, Hampton JS, Lee TJW, Evans R, Vale L, Whelpton J, Bestwick N, Rees CJ; COLO-DETECT trial team. 2024
An artificial intelligence-assisted system versus white light endoscopy alone for adenoma detection in individuals with Lynch syndrome (TIMELY): an international, multicentre, randomised controlled trial.
Ortiz, O., Daca-Alvarez, M., Rivero-Sanchez, L., Gimeno-Garcia, A.Z., Carrillo-Palau, M., Alvarez, V., Ledo-Rodriguez, A., Ricciardiello, L., Pierantoni, C., Hüneburg, R., Nattermann, J., Bisschops, R., Tejpar, S., Huerta, A., Riu Pons, F., Alvarez-Urturi, C., López-Vicente, J., Repici, A., Hassan, C., Cid, L., Cavestro, G.M., Romero-Mascarell, C., Gordillo, J., Puig, I., Herraiz, M., Betes, M., Herrero, J., Jover, R., Balaguer, F., Pellisé, M., TIMELY study group, 2024.
Human–Artificial Intelligence Collaboration: Insights and Lessons from Colonoscopy Artificial Intelligence Integration
Cherubini, A., 2024
White light computer-aided optical diagnosis of diminutive colorectal polyps in routine clinical practice
Rondonotti, E., Bergna, I.M.B., Paggi, S., Amato, A., Andrealli, A., Scardino, G., Tamanini, G., Lenoci, N., Mandelli, G., Terreni, N., Rocchetto, S., Piagnani, A., Di Paolo, D., Bina, N., Filippi, E., Ambrosiani, L., Hassan, C., Correale, L., Radaelli, F., 2024
REAL-Colon: A dataset for developing real-world AI applications in colonoscopy.
Biffi, C., Antonelli, G., Bernhofer, S., Hassan, C., Hirata, D., Iwatate, M., Maieron, A., Salvagnini, P., Cherubini, A., 2024.
The Efficacy of Real-Time Computer-Aided Detection of Colonic Neoplasia In Community Practice: A Pragmatic Randomized Controlled Trial.
Thiruvengadam, N.R., Solaimani, P., Shrestha, M., Buller, S., Carson, R., Reyes-Garcia, B., Gnass, R.D., Wang, B., Albasha, N., Leonor, P., Saumoy, M., Coimbra, R., Tabuenca, A., Srikureja, W., Serrao, S., 2024
A Computer-Aided Detection (CADe) System Significantly Improves Polyp Detection in Routine Practice.
Keswani, R.N., Thakkar, U., Sals, A., Pandolfino, J.E., 2023
A Review of the Technology, Training, and Assessment Methods for the First Real-Time AI-Enhanced Medical Device for Endoscopy. Bioengineering. 10(4) 404.
Cherubini A., Ngo Dinh, N. 2023
Effect of real-time computer-aided detection of colorectal adenoma in routine colonoscopy (COLO-GENIUS): a single-centre randomised controlled trial
Karsenti, D., Tharsis, G., Perrot, B., Cattan, P., Percie du Sert, A., Venezia, F., Zrihen, E., Gillet, A., Lab, J.P., Tordjman, G., Cavicchi, M. 2023
Role of Artificial Intelligence in Colonoscopy Detection of Advanced Neoplasias
Mangas-Sanjuan, C., de-Castro, L., Cubiella, J., Díez-Redondo, P., Suárez, A., Pellisé, M., Fernández, N., Zarraquiños, S., Núñez-Rodríguez, H., Álvarez-García, V., Ortiz, O., Sala-Miquel, N., Zapater, P., Jover, R.; CADILLAC study investigators*, 2023
Direct comparison of multiple computer-aided polyp detection systems
Troya, J., Sudarevic, B., Krenzer, A., Banck, M., Brand, M., Walter, B.M., Puppe, F., Zoller, W.G., Meining, A., Hann, A., 2023
Gorilla in the room: Even experts can miss polyps at colonoscopy and how AI helps complex visual perception tasks
Cherubini, A. and East, J.E. 2023
Cost-effectiveness of Artificial Intelligence-Aided Colonoscopy for Adenoma Detection in Colon Cancer Screening
Barkun, A.N., von Renteln, D., Sadri, H. 2023
Combination of mucosa-exposure device and computer-aided detection for Adenoma Detection during Colonoscopy: a randomized trial
Spadaccini, M., Hassan, C., Rondonotti, E., Antonelli, G., Andrisani, G., Lollo, G., Auriemma, F., Iacopini, F., Facciorusso, A., Maselli, R., Fugazza, A., Bergna, I.M.B., Cereatti, F., Mangiavillano, B., Radaelli, F., Di Matteo, F., Gross, S.A., Sharma, P., Mori, Y., Bretthauer, M., Rex, D.K., Repici, A. 2023
The brave new world of artificial intelligence: dawn of a new era
Di Napoli, G., Lee, L. S., 2023
Cost-utility analysis of real-time artificial intelligent-assisted colonoscopy in Italy
Hassan, C., Povero, M., Pradelli, L., Spadaccini, M., Repici, A., 2023
Knowledge, perceptions and behaviours of endoscopists towards the use of artificial intelligence-aided colonoscopy.
Tham, S., Koh, F.H., Teo, E.K., Lin, C.L., Foo, F.J., 2023
An Evaluation of Critical Factors for the Cost-Effectiveness of Real-Time Computer-Aided Detection: Sensitivity and Threshold Analyses Using a Microsimulation Model
Thiruvengadam, N.R., Cote, G., Gupta, S., Rodrigues, M., Schneider, Y., Arain, M.A., Solaimani, P., Serrao, S., Kochman, M.L., Saumoy, M., 2023
Impact of Artificial Intelligence on Miss Rate of Colorectal Neoplasia
Wallace, M.B., Sharma, P., Bhandari, P., East, J., Antonelli, G., Lorenzetti, R., Vieth, M., Speranza, I., Spadaccini, M., Desai, M., Lukens, F.J., Babameto, G., Batista, D., Singh, D., Palmer, W., Ramirez, F., Palmer, R., Lunsford, T., Ruff, K., Bird-Liebermann, E., Ciofoaia, V., Arndtz, S., Cangemi, D., Puddick, K., Derfus, G., Johal, A.S., Barawi, M., Longo, L., Moro, L., Repici, A., Hassan, C., 2022
A novel AI device for real-time optical characterization of colorectal polyps
Biffi, C., Salvagnini, P., Dinh, N.N., Hassan, C., Sharma, P., GI Genius CADx Study Group, Cherubini, A., 2022
Artificial Intelligence Allows Leaving-In-Situ Colorectal Polyps. Clin. Gastroenterol. Hepatol. Off. Clin. Pract. J. Am. Gastroenterol
Hassan, C., Balsamo, G., Lorenzetti, R., Zullo, A., Antonelli, G., 2022
Experimental evidence of effective human-AI collaboration in medical decision-making
Reverberi, C., Rigon, T., Solari, A., Hassan, C., Cherubini, P., GI Genius CADx Study Group, Cherubini, A., 2022
Strengths and Weaknesses of an Artificial Intelligence Polyp Detection Program as Assessed by a High-Detecting Endoscopist
Rex, D.K., Mori, Y., Sharma, P., Lahr, R.E., Vemulapalli, K.C., Hassan, C., 2022
Artificial intelligence and colonoscopy experience: lessons from two randomised trials
Repici, A., Spadaccini, M., Antonelli, G., Correale, L., Maselli, R., Galtieri, P.A., Pellegatta, G., Capogreco, A., Milluzzo, S.M., Lollo, G., Di Paolo, D., Badalamenti, M., Ferrara, E., Fugazza, A., Carrara, S., Anderloni, A., Rondonotti, E., Amato, A., De Gottardi, A., Spada, C., Radaelli, F., Savevski, V., Wallace, M.B., Sharma, P., Rösch, T., Hassan, C., 2022
Evaluation of a real-time computer-aided polyp detection system during screening colonoscopy: AI-DETECT study
Ahmad, A., Wilson, A., Haycock, A., Humphries, A., Monahan, K., Suzuki, N., Thomas-Gibson, S., Vance, M., Bassett, P., Thiruvilangam, K., Dhillon, A., Saunders, B.P., 2022
Artificial Intelligence-Aided Colonoscopy Does Not Increase Adenoma Detection Rate in Routine Clinical Practice
Levy, I., Bruckmayer, L., Klang, E., Ben-Horin, S., Kopylov, U., 2022
Computer-aided Detection of Polyps Does Not Improve Colonoscopist Performance in a Pragmatic Implementation Trial
Ladabaum, U., Shepard, J., Weng, Y., Desai, M., Singer, S.J., Mannalithara, A., 2022
Prospective study of computer-aided detection of colorectal adenomas in hospitalized patients
Engelke, C., Graf, M., Maass, C., Tews, H.C., Kraus, M., Ewers, T., Sayk, F., Solbach, P., Zimpel, C., Tharun, L., Marquardt, J.U., Kirstein, M.M., 2022
One-year review of real-time artificial intelligence (AI)-aided endoscopy performance
Chin, S.E., Wan, F.T., Ladlad, J., Chue, K.M., SKH, E.C., Teo, E.K., Lin, C.L., Foo, F.J., Koh, F.H., 2022
Real-time artificial intelligence (AI)-aided endoscopy improves adenoma detection rates even in experienced endoscopists: a cohort study in Singapore
Koh, F.H., Ladlad, J., SKH Endoscopy Centre, Foo, F.-J., Tan, W.J., Sivarajah, S.S., Ho, L.M.L., Ng, J.-L., Koh, F.H., Chong, C., Aw, D., Kam, J.-H., Tan, A.Y.H., Tan, C.-C., Yeung, B.P.M., Wong, W.-K., Toh, B.-C., Ladlad, J., Barco, J., Chue, K.-M., Leong, F., Kong, C., Lin, C.-L., Teo, E.-K., Ng, Y.-K., Tey, T.-T., De-Roza, M.A., Lum, J., Li, X., Li, J., Mohd-Nor, N.B., Ng, S.-P., Teo, E.-K., Lin, C.-L., Foo, F.-J., 2022
Discovering the first US FDA-approved computer-aided polyp detection system
Spadaccini, M., Marco, A.D., Franchellucci, G., Sharma, P., Hassan, C., Repici, A., 2022
Computer-aided detection-assisted colonoscopy: classification and relevance of false positives
Hassan, C., Badalamenti, M., Maselli, R., Correale, L., Iannone, A., Radaelli, F., Rondonotti, E., Ferrara, E., Spadaccini, M., Alkandari, A., Fugazza, A., Anderloni, A., Galtieri, P.A., Pellegatta, G., Carrara, S., Di Leo, M., Craviotto, V., Lamonaca, L., Lorenzetti, R., Andrealli, A., Antonelli, G., Wallace, M., Sharma, P., Rösch, T., Repici, A., 2020
New artificial intelligence system: first validation study versus experienced endoscopists for colorectal polyp detection
Hassan, C., Wallace, M.B., Sharma, P., Maselli, R., Craviotto, V., Spadaccini, M., Repici, A., 2020
Efficacy of Real-Time Computer-Aided Detection of Colorectal Neoplasia in a Randomized Trial
Repici, A., Badalamenti, M., Maselli, R., Correale, L., Radaelli, F., Rondonotti, E., Ferrara, E., Spadaccini, M., Alkandari, A., Fugazza, A., Anderloni, A., Galtieri, P.A., Pellegatta, G., Carrara, S., Di Leo, M., Craviotto, V., Lamonaca, L., Lorenzetti, R., Andrealli, A., Antonelli, G., Wallace, M., Sharma, P., Rosch, T., Hassan, C., 2020
GI Genius™ is distributed
by Medtronic.
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