Cited by 119Colorectal Cancer Screening and DetectionAI in cancer detectionArtificial Intelligence in Healthcare and Education

The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.

Real-Time Artificial Intelligence–Based Optical Diagnosis of Neoplastic Polyps during Colonoscopy

Barua, Ishita, Wieszczy, Paulina, Kudo, Shin-ei, Misawa, Masashi, Holme, Øyvind, Gulati, Shraddha, Williams, Sophie, Mori, Kensaku, Itoh, Hayato, Takishima, Kazumi, Mochizuki, Kenichi, Miyata, Yuki, Mochida, Kentaro, Akimoto, Yoshika, Kuroki, Takanori, Morita, Yuriko, Shiina, Osamu, Kato, Shun, Nemoto, Tetsuo, Hayee, Bu, Patel, Mehul, Gunasingam, Nishmi, Kent, Alexandra, Emmanuel, Andrew, Munck, Carl, Nilsen, Jens Aksel, Hvattum, Stine Astrup, Frigstad, Svein Oskar, Tandberg, Petter, Løberg, Magnus, Kalager, Mette, Haji, Amyn, Bretthauer, Michael, Mori, Yuichi·2022·NEJM Evidence

DOI 10.1056/evidoa2200003

Open this paper in CoChat — free account

Abstract

BACKGROUND: Artificial intelligence using computer-aided diagnosis (CADx) in real time with images acquired during colonoscopy may help colonoscopists distinguish between neoplastic polyps requiring removal and nonneoplastic polyps not requiring removal. In this study, we tested whether CADx analyzed images helped in this decision-making process. METHODS: We performed a multicenter clinical study comparing a novel CADx-system that uses real-time ultra-magnifying polyp visualization during colonoscopy with standard visual inspection of small (≤5 mm in diameter) polyps in the sigmoid colon and the rectum for optical diagnosis of neoplastic histology. After committing to a diagnosis (i.e., neoplastic, uncertain, or nonneoplastic), all imaged polyps were removed. The primary end point was sensitivity for neoplastic polyps by CADx and visual inspection, compared with histopathology. Secondary end points were specificity and colonoscopist confidence level in unaided optical diagnosis. RESULTS: We assessed 1289 individuals for eligibility at colonoscopy centers in Norway, the United Kingdom, and Japan. We detected 892 eligible polyps in 518 patients and included them in analyses: 359 were neoplastic and 533 were nonneoplastic. Sensitivity for the diagnosis of neoplastic polyps with standard visual inspection was 88.4% (95% confidence interval [CI], 84.3 to 91.5) compared with 90.4% (95% CI, 86.8 to 93.1) with CADx (P=0.33). Specificity was 83.1% (95% CI, 79.2 to 86.4) with standard visual inspection and 85.9% (95% CI, 82.3 to 88.8) with CADx. The proportion of polyp assessment with high confidence was 74.2% (95% CI, 70.9 to 77.3) with standard visual inspection versus 92.6% (95% CI, 90.6 to 94.3) with CADx. CONCLUSIONS: Real-time polyp assessment with CADx did not significantly increase the diagnostic sensitivity of neoplastic polyps during a colonoscopy compared with optical evaluation without CADx. (Funded by the Research Council of Norway [Norges Forskningsråd], the Norwegian Cancer Society [Kreftforeningen], and the Japan Society for the Promotion of Science; UMIN number, UMIN000035213.)

Integrity signals

Registry

Crossref · 10.1056/evidoa2200003

Cites

19 of 19 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries
Sung, H., Ferlay, J., Siegel, R. L., Laversanne, M., Soerjomataram, I., Jemal, A., & Bray, F. (2021) · CA: A Cancer Journal for Clinicians · doi:10.3322/caac.21660 · Cited by 108,699
Colonoscopic Polypectomy and Long-Term Prevention of Colorectal-Cancer Deaths
Zauber, A. G., Winawer, S. J., O’Brien, M. J., Lansdorp‐Vogelaar, I., van Ballegooijen, M., Hankey, B. F., Shi, W., Bond, J. H., Schapiro, M., Panish, J. F., et al. (2012) · New England Journal of Medicine · doi:10.1056/nejmoa1100370 · Cited by 3,185
The Paris endoscopic classification of superficial neoplastic lesions: esophagus, stomach, and colon
Workshop, P. I. T. P. (2003) · Gastrointestinal Endoscopy · doi:10.1016/s0016-5107(03)02159-x · Cited by 1,788
The Boston bowel preparation scale: a valid and reliable instrument for colonoscopy-oriented research
Lai, E. J., Calderwood, A. H., Doros, G. D., Fix, O. K., & Jacobson, B. C. (2009) · Gastrointestinal Endoscopy · doi:10.1016/j.gie.2008.05.057 · Cited by 1,322
The American Society for Gastrointestinal Endoscopy PIVI (Preservation and Incorporation of Valuable Endoscopic Innovations) on real-time endoscopic assessment of the histology of diminutive colorectal polyps
Rex, D. K., Kahi, C., O'Brien, M., Levin, T. R., Pohl, H., Rastogi, A., Burgart, L., Imperiale, T., Ladabaum, U., Cohen, J., et al. (2011) · Gastrointestinal Endoscopy · doi:10.1016/j.gie.2011.01.023 · Cited by 629
Validation of a Simple Classification System for Endoscopic Diagnosis of Small Colorectal Polyps Using Narrow-Band Imaging
Hewett, D. G., Kaltenbach, T. R., Sano, Y., Tanaka, S., Saunders, B. P., Ponchon, T., Soetikno, R. M., & Rex, D. K. (2012) · Gastroenterology · doi:10.1053/j.gastro.2012.05.006 · Cited by 589
Real-Time Use of Artificial Intelligence in Identification of Diminutive Polyps During Colonoscopy: A Prospective Study
Mori, Y., Kudo, S.-E., Misawa, M., Saito, Y., Ikematsu, H., Hotta, K., Ohtsuka, K., Urushibara, F., Kataoka, S., Ogawa, Y., et al. (2018) · Annals of Internal Medicine · doi:10.7326/m18-0249 · Cited by 509
Self-citation
ASGE Technology Committee systematic review and meta-analysis assessing the ASGE PIVI thresholds for adopting real-time endoscopic assessment of the histology of diminutive colorectal polyps
Abu Dayyeh, B. K., Thosani, N., Konda, V., Wallace, M. B., Rex, D. K., Chauhan, S. S., Hwang, J. H., Komanduri, S., Manfredi, M., Maple, J. T., et al. (2015) · Gastrointestinal Endoscopy · doi:10.1016/j.gie.2014.12.022 · Cited by 389
A Resect and Discard Strategy Would Improve Cost-Effectiveness of Colorectal Cancer Screening
Hassan, C., Pickhardt, P. J., & Rex, D. K. (2010) · Clinical Gastroenterology and Hepatology · doi:10.1016/j.cgh.2010.05.018 · Cited by 294
Development and validation of the WASP classification system for optical diagnosis of adenomas, hyperplastic polyps and sessile serrated adenomas/polyps
IJspeert, J. E. G., Bastiaansen, B. A. J., van Leerdam, M. E., Meijer, G. A., van Eeden, S., Sanduleanu, S., Schoon, E. J., Bisseling, T. M., Spaander, M. C. W., van Lelyveld, N., et al. (2015) · Gut · doi:10.1136/gutjnl-2014-308411 · Cited by 274

Show all 19 references

Sorted by citations

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, Antonelli, G., Awadie, H., Bernhofer, S., Carballal, S., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-18751-2 · Cited by 281
Expected value of artificial intelligence in gastrointestinal endoscopy: European Society of Gastrointestinal Endoscopy (ESGE) Position Statement
Messmann, H., Bisschops, R., Antonelli, G., Libânio, D., Sinonquel, P., Abdelrahim, M., Ahmad, O. F., Areia, M., Bergman, J. J. G. H. M., Bhandari, P., et al. (2022) · Endoscopy · doi:10.1055/a-1950-5694 · Cited by 189
Self-citation
Use of artificial intelligence improves colonoscopy performance in adenoma detection: a systematic review and meta-analysis
Makar, J., Abdelmalak, J., Con, D., Hafeez, B., & Garg, M. (2024) · Gastrointestinal Endoscopy · doi:10.1016/j.gie.2024.08.033 · Cited by 141
Autonomous Artificial Intelligence vs Artificial Intelligence–Assisted Human Optical Diagnosis of Colorectal Polyps: A Randomized Controlled Trial
Djinbachian, R., Haumesser, C., Taghiakbari, M., Pohl, H., Barkun, A., Sidani, S., Liu Chen Kiow, J., Panzini, B., Bouchard, S., Deslandres, E., et al. (2024) · Gastroenterology · doi:10.1053/j.gastro.2024.01.044 · Cited by 70
Real-world validation of a computer-aided diagnosis system for prediction of polyp histology in colonoscopy: a prospective multicenter study
Li, J. W., Wu, C. C. H., Lee, J. W. J., Liang, R., Soon, G. S. T., Wang, L. M., Koh, X. H., Koh, C. J., Chew, W. D., Lin, K. W., et al. (2023) · American Journal of Gastroenterology · doi:10.14309/ajg.0000000000002282 · Cited by 61
Benefits and challenges in implementation of artificial intelligence in colonoscopy: World Endoscopy Organization position statement
Mori, Y., East, J. E., Hassan, C., Halvorsen, N., Berzin, T. M., Byrne, M. F., Renteln, D. A. V., Hewett, D. G., Repici, A., Ramchandani, M. J., et al. (2023) · Digestive Endoscopy · doi:10.1111/den.14531 · Cited by 62
Self-citation
The Role of Artificial Intelligence in Colorectal Cancer Screening: Lesion Detection and Lesion Characterization
Young, E., Edwards, L., & Singh, R. (2023) · Cancers · doi:10.3390/cancers15215126 · Cited by 57
Computer-Aided Diagnosis for Leaving Colorectal Polyps In Situ: A Systematic Review and Meta-analysis
Hassan, C., Misawa, M., Rizkala, T., Mori, Y., Sultan, S., Facciorusso, A., Antonelli, G., Spadaccini, M., Houwen, B. B. S. L., Rondonotti, E., et al. (2024) · Annals of Internal Medicine · doi:10.7326/m23-2865 · Cited by 56
Self-citation
Artificial Intelligence for Real-Time Prediction of the Histology of Colorectal Polyps by General Endoscopists
Rex, D. K., Bhavsar‐Burke, I., Buckles, D. C., Burton, J. R., Cartee, A. K., Comar, K. M., Edwards, A. L., Fennimore, B. P., Fischer, M., Gerich, M. E., et al. (2024) · Annals of Internal Medicine · doi:10.7326/m24-0086 · Cited by 51
Self-citation
Training in basic gastrointestinal endoscopic procedures: a European Society of Gastrointestinal Endoscopy (ESGE) and European Society of Gastroenterology and Endoscopy Nurses and Associates (ESGENA) Position Statement
Antonelli, G., Voiosu, A. M., Pawlak, K. M., Gonçalves, T. C., Le, N. N. H., Bronswijk, M. J., Hollenbach, M., Elshaarawy, O., Beilenhoff, U., Mascagni, P., et al. (2023) · Endoscopy · doi:10.1055/a-2205-2613 · Cited by 53
Artificial intelligence in cancer: applications, challenges, and future perspectives
Cheng, H., & Shi, S.-S. (2025) · Molecular Cancer · doi:10.1186/s12943-025-02450-3 · Cited by 64
Computer-aided diagnosis for optical diagnosis of diminutive colorectal polyps including sessile serrated lesions: a real-time comparison with screening endoscopists
Houwen, B. B. S. L., Hazewinkel, Y., Giotis, I., Vleugels, J. L. A., Mostafavi, N. S., van Putten, P., Fockens, P., Dekker, E., & POLAR Study Group (2023) · Endoscopy · doi:10.1055/a-2009-3990 · Cited by 48
Comparative performance of artificial intelligence optical diagnosis systems for leaving in situ colorectal polyps
Hassan, C., Sharma, P., Mori, Y., Bretthauer, M., Rex, D. K., Spadaccini, M., Selvaggio, C., Antonelli, G., Khalaf, K., Rizkala, T., et al. (2023) · Gastroenterology · doi:10.1053/j.gastro.2022.10.021 · Cited by 46
Self-citation
Colorectal cancer screening: A review of current knowledge and progress in research
Lopes, S. R., Martins, C., Santos, I. C., Teixeira, M., Gamito, É., & Alves, A. L. (2024) · World Journal of Gastrointestinal Oncology · doi:10.4251/wjgo.v16.i4.1119 · Cited by 54
Artificial Intelligence-assisted Video Colonoscopy for Disease Monitoring of Ulcerative Colitis: A Prospective Study
Ogata, N., Maeda, Y., Misawa, M., Takenaka, K., Takabayashi, K., Iacucci, M., Kuroki, T., Takishima, K., Sasabe, K., Niimura, Y., et al. (2024) · Journal of Crohn s and Colitis · doi:10.1093/ecco-jcc/jjae080 · Cited by 35
Self-citation
Enhancing human-AI collaboration: The case of colonoscopy
Introzzi, L., Zonca, J., Cabitza, F., Cherubini, P., & Reverberi, C. (2024) · Digestive and Liver Disease · doi:10.1016/j.dld.2023.10.018 · Cited by 28
Current and future implications of artificial intelligence in colonoscopy
Antonelli, G. (2023) · Annals of Gastroenterology · doi:10.20524/aog.2023.0781 · Cited by 29
A Review of the Technology, Training, and Assessment Methods for the First Real-Time AI-Enhanced Medical Device for Endoscopy
Cherubini, A., & Dinh, N. N. (2023) · Bioengineering · doi:10.3390/bioengineering10040404 · Cited by 26
Multimodal generative AI for interpreting 3D medical images and videos
Lee, J.-O., Zhou, H.-Y., Berzin, T. M., Sodickson, D. K., & Rajpurkar, P. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01649-4 · Cited by 27
Accuracy of polyp characterization by artificial intelligence and endoscopists: a prospective, non-randomized study in a tertiary endoscopy center
Baumer, S., Streicher, K., Alqahtani, S. A., Brookman-Amissah, D., Brunner, M., Federle, C., Muehlenberg, K., Pfeifer, L., Salzberger, A., Schorr, W., et al. (2023) · Endoscopy International Open · doi:10.1055/a-2096-2960 · Cited by 23
Artificial Intelligence–Assisted Optical Diagnosis: A Comprehensive Review of Its Role in Leave-In-Situ and Resect-and-Discard Strategies in Colonoscopy
Zoghbi, M. E., Shaukat, A., Hassan, C., Anderson, J. C., Repici, A., & Gross, S. A. (2023) · Clinical and Translational Gastroenterology · doi:10.14309/ctg.0000000000000640 · Cited by 22
AGA Clinical Practice Update on the Role of Artificial Intelligence in Colon Polyp Diagnosis and Management: Commentary
Samarasena, J., Yang, D. J., & Berzin, T. M. (2023) · Gastroenterology · doi:10.1053/j.gastro.2023.07.010 · Cited by 21
Updates in narrow‐band imaging for colorectal polyps: Narrow‐band imaging generations, detection, diagnosis, and artificial intelligence
Teramoto, A., Hamada, S., Ogino, B., Yasuda, I., & Sano, Y. (2022) · Digestive Endoscopy · doi:10.1111/den.14489 · Cited by 20
Artificial intelligence in endoscopy: Overview, applications, and future directions
Arif, A. A., Jiang, S. X., & Byrne, M. F. (2023) · Saudi Journal of Gastroenterology · doi:10.4103/sjg.sjg_286_23 · Cited by 21
Accuracy of Computer-aided Diagnosis in Colonoscopy Varies According to Polyp Location: A Systematic Review and Meta-analysis
Rizkala, T., Hassan, C., Mori, Y., Spadaccini, M., Antonelli, G., Dekker, E., Houwen, B. B. S. L., Pech, O., Baumer, S., Rondonotti, E., et al. (2024) · Clinical Gastroenterology and Hepatology · doi:10.1016/j.cgh.2024.08.021 · Cited by 20
Self-citation
Enhancing artificial intelligence-doctor collaboration for computer-aided diagnosis in colonoscopy through improved digital literacy
Mori, Y., Jin, E. H., & Lee, D. (2023) · Digestive and Liver Disease · doi:10.1016/j.dld.2023.11.033 · Cited by 19
Self-citation
Clinical trial designs for artificial intelligence in gastrointestinal endoscopy
Mori, Y., Kaminski, M. F., Hassan, C., & Bretthauer, M. (2022) · The Lancet Gastroenterology & Hepatology · doi:10.1016/s2468-1253(22)00232-1 · Cited by 18
Self-citation
The Role of Artificial Intelligence in Prospective Real-Time Histological Prediction of Colorectal Lesions during Colonoscopy: A Systematic Review and Meta-Analysis
Vadhwana, B., Tarazi, M., & Patel, V. (2023) · Diagnostics · doi:10.3390/diagnostics13203267 · Cited by 18
Colon Cancer Screening, Surveillance, and Treatment: Novel Artificial Intelligence Driving Strategies in the Management of Colon Lesions
Hassan, C., Bisschops, R., Sharma, P., & Mori, Y. (2025) · Gastroenterology · doi:10.1053/j.gastro.2025.02.021 · Cited by 20
Self-citation
Current Status of Artificial Intelligence Use in Colonoscopy
Misawa, M., & Kudo, S.-E. (2024) · Digestion · doi:10.1159/000543345 · Cited by 19
Self-citation
Artificial intelligence-assisted colonoscopy to identify histologic remission and predict the outcomes of patients with ulcerative colitis: A systematic review
Maeda, Y., Kudo, S.-E., Santacroce, G., Ogata, N., Misawa, M., & Iacucci, M. (2024) · Digestive and Liver Disease · doi:10.1016/j.dld.2024.04.005 · Cited by 16
Self-citation
Artificial Intelligence in Gastrointestinal Endoscopy: The Japan Gastroenterological Endoscopy Society Position Statements
Mori, Y., Ishihara, R., Ogata, H., Kutsumi, H., Saito, Y., Sumiyama, K., Sekiguchi, M., Tajiri, H., Fujishiro, M., Matsuda, K., et al. (2025) · Digestive Endoscopy · doi:10.1111/den.15075 · Cited by 15
Self-citation
Artificial Intelligence in Colonoscopy
Mansour, N. (2023) · Current Gastroenterology Reports · doi:10.1007/s11894-023-00872-x · Cited by 15
Artificial intelligence for characterization of colorectal polyps: Prospective multicenter study
Lange, G. D., Prouvost, V., Rahmi, G., Vanbiervliet, G., Berre, C. L., Mack, S., Koessler, T., & Coron, E. (2024) · Endoscopy International Open · doi:10.1055/a-2261-2711 · Cited by 13
Artificial intelligence-enabled advanced endoscopic imaging to assess deep healing in inflammatory bowel disease
Maeda, Y., Ditonno, I., Puga‐Tejada, M., Zammarchi, I., Santacroce, G., Ghosh, S., & Iacucci, M. (2024) · eGastroenterology · doi:10.1136/egastro-2024-100090 · Cited by 13
Impact of computer‐aided characterization for diagnosis of colorectal lesions, including sessile serrated lesions: Multireader, multicase study
Kato, S., Kudo, S., Minegishi, Y., Miyata, Y., Maeda, Y., Kuroki, T., Takashina, Y., Mochizuki, K., Tamura, E., Abe, M., et al. (2023) · Digestive Endoscopy · doi:10.1111/den.14612 · Cited by 13
Self-citation
The path from task-specific to general purpose artificial intelligence for medical diagnostics: A bibliometric analysis
Chang, C., Shi, W., Wang, Y., Zhang, Z., Huang, X., & Jiao, Y. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108258 · Cited by 15
Emerge of colorectal cancer in Lynch syndrome despite colonoscopy surveillance: A challenge of hide and seek
Helderman, N. C., van Leerdam, M. E., Kloor, M., Ahadova, A., & Nielsen, M. (2024) · Critical Reviews in Oncology/Hematology · doi:10.1016/j.critrevonc.2024.104331 · Cited by 11
Effectiveness of artificial intelligence-assisted colonoscopy in detecting and diagnosing colorectal tumors: a systematic review and network meta-analysis
Tan, S., Zeng, P., Liu, S., Yang, Y., Chen, S., Zhang, W. Y., Li, X., Liu, D., Li, Y., & Chen, X. (2025) · International Journal of Colorectal Disease · doi:10.1007/s00384-025-04949-z · Cited by 11
Incorporating Artificial Intelligence Into Gastroenterology Practices
Hassan, C., Repici, A., & Sharma, P. (2023) · Clinical Gastroenterology and Hepatology · doi:10.1016/j.cgh.2023.02.008 · Cited by 10
Artificial intelligence for disease diagnosis: the criterion standard challenge
Mori, Y., Misawa, M., Bernal, J., Bretthauer, M., Kudo, S., Rastogi, A. N., & Fernández‐Esparrach, G. (2022) · Gastrointestinal Endoscopy · doi:10.1016/j.gie.2022.04.057 · Cited by 10
Self-citation
Leaving colorectal polyps in situ with endocytoscopy assisted by computer-aided diagnosis: a cost-effectiveness study
Halvorsen, N., Barua, I., Bretthauer, M., Mori, Y., Kudo, S.-E., Gulati, S., Misawa, M., Mori, K., Hayee, B., Olabintan, O., et al. (2025) · Endoscopy · doi:10.1055/a-2532-9282 · Cited by 9
Self-citation
Artificial Intelligence‐assisted Endoscopy and Examiner Confidence: A Study on Human–Artificial Intelligence Interaction in Barrett's Esophagus (With Video)
Roser, D., Meinikheim, M., Muzalyova, A., Mendel, R., Palm, C., Probst, A., Nagl, S., Scheppach, M. W., Römmele, C., Schnoy, E., et al. (2025) · DEN Open · doi:10.1002/deo2.70150 · Cited by 10
Development of deep learning-based narrow-band imaging endocytoscopic classification for predicting colorectal lesions from a retrospective study
Wang, J., Liu, M., Liao, H., Fan, J., Zhu, H., Ma, T., Yang, D., Ni, F., Zhang, F., Jin, G., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-63812-5 · Cited by 10
Automated Endoscopic Diagnosis in IBD
Maeda, Y., Kudo, S., Kuroki, T., & Iacucci, M. (2024) · Gastrointestinal Endoscopy Clinics of North America · doi:10.1016/j.giec.2024.04.012 · Cited by 8
Self-citation
Safety of artificial intelligence-assisted optical diagnosis for leaving colorectal polyps in situ during colonoscopy (PRACTICE): a non-inferiority, randomised controlled trial
Antonelli, G., Desideri, F., Scarozza, P., Andrisani, G., Zerboni, G., Furnari, M., Bevilacqua, N., Cossignani, M., Fonzo, M. D., Cereatti, F., et al. (2025) · The Lancet. Gastroenterology & hepatology · doi:10.1016/s2468-1253(25)00140-2 · Cited by 8
Self-citation
Advances in artificial intelligence and computer science for computer-aided diagnosis of colorectal polyps: current status
van Bokhorst, Q. N., Houwen, B. B., Hazewinkel, Y., Fockens, P., & Dekker, E. (2023) · Endoscopy International Open · doi:10.1055/a-2098-1999 · Cited by 8
PolySeg Plus: Polyp Segmentation Using Deep Learning with Cost Effective Active Learning
Saad, A. I., Maghraby, F. A., & Badawy, O. M. (2023) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-023-00330-6 · Cited by 7
Exploiting artificial intelligence in precision oncology: an updated comprehensive review
Goda, R. Y., & Abdel‐Aziz, A. K. (2025) · Journal of Translational Medicine · doi:10.1186/s12967-025-07308-2 · Cited by 9
Diagnostic accuracy of endocytoscopy via artificial intelligence in colorectal lesions: A systematic review and meta‑analysis
Zhang, H., Yang, X., Tao, Y., Zhang, X., & Huang, X. (2023) · PLoS ONE · doi:10.1371/journal.pone.0294930 · Cited by 8
Role of Artificial Intelligence for Colon Polyp Detection and Diagnosis and Colon Cancer
Rizkala, T., Menini, M., Massimi, D., & Repici, A. (2024) · Gastrointestinal Endoscopy Clinics of North America · doi:10.1016/j.giec.2024.10.005 · Cited by 7
Cost-Effectiveness for Artificial Intelligence in Colonoscopy
Halvorsen, N., & Mori, Y. (2024) · Gastrointestinal Endoscopy Clinics of North America · doi:10.1016/j.giec.2024.10.008 · Cited by 7
Self-citation
From data to artificial intelligence: evaluating the readiness of gastrointestinal endoscopy datasets
Elamin, S., Johri, S., Rajpurkar, P., Geisler, E., & Berzin, T. M. (2025) · Journal of the Canadian Association of Gastroenterology · doi:10.1093/jcag/gwae041 · Cited by 7
Computer-aided polyp characterization in colonoscopy: sufficient performance or not?
Halvorsen, N., & Mori, Y. (2024) · Clinical Endoscopy · doi:10.5946/ce.2023.092 · Cited by 7
Self-citation
Implementing discard strategies for diminutive polyps using autonomous CADx in clinical practice
Taghiakbari, M., Rex, D. K., Pohl, H., Hassan, C., Djinbachian, R., Huang, F., & Renteln, D. A. V. (2025) · Gut · doi:10.1136/gutjnl-2025-335441 · Cited by 6
Cardiac Amyloidosis: Tribulations and New Frontiers
Nguyen, D., Ramazani, N., Sodhi, G., & Tak, T. (2025) · Journal of Personalized Medicine · doi:10.3390/jpm15100472 · Cited by 6
Efficacy of an artificial intelligence system for lesion detection and characterization ( CADe and CADx ) during colonoscopy following positive faecal immunochemical test in a colorectal cancer screening programme: A randomized clinical trial
de la Osa, D. R., Fernández, J. S., Urra, C. P., Pinedo, P. E., Labrador, C. B. B., Ibáñez, C. M., de Castro, E. G., Citores, L. P., Moretón, Á. M. M., Santamarta, F. S., et al. (2026) · Colorectal Disease · doi:10.1111/codi.70426 · Cited by 8
AI and endoscopy/histology in UC: the rise of machine
Nardone, O. M., Maeda, Y., & Iacucci, M. (2024) · Therapeutic Advances in Gastroenterology · doi:10.1177/17562848241275294 · Cited by 5
Computer-aided diagnosis of colorectal polyps: assisted or autonomous?
Mori, Y., & Hassan, C. (2025) · Clinical Endoscopy · doi:10.5946/ce.2024.338 · Cited by 6
Self-citation
Multicentre study to assess the performance of an artificial intelligence instrument to support qualitative diagnosis of colorectal polyps
Sato, K., Kuramochi, M., Tsuchiya, A., Yamaguchi, A., Hosoda, Y., Yamaguchi, N., Nakamura, N., Itoi, Y., Hashimoto, Y., Kasuga, K., et al. (2024) · BMJ Open Gastroenterology · doi:10.1136/bmjgast-2024-001553 · Cited by 5
The Pros and Cons of Artificial Intelligence in Endoscopy
Hassan, C., Mori, Y., & Sharma, P. (2023) · The American Journal of Gastroenterology · doi:10.14309/ajg.0000000000002287 · Cited by 5
Self-citation
Role of CADx in colonoscopy: lessons from real-life studies
Bustamante, M. (2025) · Best Practice & Research Clinical Gastroenterology · doi:10.1016/j.bpg.2025.102020 · Cited by 5
A novel approach to overcome black box of AI for optical diagnosis in colonoscopy
Shin, Y., Bae, J. H., Kim, J., Choi, J., & Kim, Y.-G. (2025) · Scientific Reports · doi:10.1038/s41598-025-04770-2 · Cited by 4
Advancements in real-time oncology diagnosis: harnessing AI and image fusion techniques
Bagheriye, L., & Kwisthout, J. (2025) · Frontiers in Oncology · doi:10.3389/fonc.2025.1468753 · Cited by 6
Optical Polyp Diagnosis in the Era of Artificial Intelligence
Djinbachian, R., Rex, D. K., & Renteln, D. A. V. (2024) · The American Journal of Gastroenterology · doi:10.14309/ajg.0000000000003195 · Cited by 5
Artificial intelligence enabled histological scoring in ulcerative colitis: Are we ready yet?
Iacucci, M., Maeda, Y., & Ghosh, S. K. (2024) · United European Gastroenterology Journal · doi:10.1002/ueg2.12579 · Cited by 4
Environmental impact of computer-aided diagnosis in colonoscopy: a carbon footprint assessment of a prospective study cohort
Olabintan, O., Halvorsen, N., Baddeley, R., Kudo, S.-E., Barua, I., Misawa, M., Mori, K., Hunt, C., Nilsen, J. A., Frigstad, S. O., et al. (2025) · Endoscopy · doi:10.1055/a-2717-8365 · Cited by 3
Self-citation
White Light, Magnifying Endoscopy, Endocytoscopy, and Artificial Intelligence in Diagnosis of Early Colorectal Cancer: A Comparative Study
Tamura, E., Kudo, S., Iwasaki, S., Semba, S., Shibuya, T., Kato, S., Kuroki, T., Sato, Y., Sakurai, T., Ogawa, Y., et al. (2025) · DEN Open · doi:10.1002/deo2.70240 · Cited by 5
Self-citation
Artificial intelligence-based image recognition in bronchoscopy: software development and randomized controlled trial for training evaluation in intensive care residents
Brunoni, B., Zadek, F., Pampurini, F., Vettorello, M., Baccoli, F., Cabitza, F., Fumagalli, R., & Langer, T. (2026) · BMC Medical Education · doi:10.1186/s12909-026-08817-4 · Cited by 3
A critical review of technical progress, clinical heterogeneity, and implementation challenges of artificial intelligence in digestive endoscopy
Gadour, E., Raheem, B., & Facciorusso, A. (2026) · Expert Review of Gastroenterology & Hepatology · doi:10.1080/17474124.2026.2646299 · Cited by 3
Artificial Intelligence in Gastrointestinal Endoscopy and Hemostatic Decision-Making: Current Evidence, Clinical Implications and Implementation Barriers
Brusnic, O., Boicean, A., Ichim, C., Anderco, P., & Onișor, D. M. (2026) · Life · doi:10.3390/life16050845 · Cited by 4
Colonoscopy Quality, Innovation, and the Assessment of New Technology
Gadi, S. R. V., Muralidharan, S. S., & Brown, J. R. G. (2024) · Techniques and Innovations in Gastrointestinal Endoscopy · doi:10.1016/j.tige.2024.03.001 · Cited by 3
Research ethics for AI in healthcare: how, when and who
Pifarré-Esquerda, F., Esquerda, M., & Garcia-Cuyas, F. (2025) · AI & Society · doi:10.1007/s00146-025-02198-w · Cited by 5
Asia-Pacific consensus on the use of artificial intelligence in colorectal cancer screening and surveillance
Koh, F. H.-X., Li, J. W., Wong, S. H., Lee, J., John, S., Chong, V. H., Wu, K., Lui, R. N.-S., Ng, S. S. M., Lam, T. Y., et al. (2025) · iGIE · doi:10.1016/j.igie.2025.04.001 · Cited by 3
Development and deployment of Computer-aided Real-Time feedback for improving quality of colonoscopy in a Multi-Center clinical trial
Tavanapong, W., Pratt, J., Oh, J., Khaleel, M., Wong, J. S. K., & de Groen, P. C. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.104609 · Cited by 4
Artificial Intelligence in Colorectal Cancer Screening
Ramprasad, C., Uche-Anya, E. N., & Berzin, T. M. (2023) · Current Treatment Options in Gastroenterology · doi:10.1007/s11938-023-00426-0 · Cited by 3
Translation of Artificial Intelligence in Colonoscopy
Ahmed, J. F., El-Sayed, A., & Kader, R. (2025) · Digestion · doi:10.1159/000548823 · Cited by 2
Research progress on the application of artificial intelligence in colonoscopy
Bo, J., Liu, Z., wang, Y., & Luo, Q. (2025) · Gastroenterology & Endoscopy · doi:10.1016/j.gande.2025.10.003 · Cited by 3
Computer‐Aided Diagnosis of Colorectal Polyps: Clinical Usefulness and Limitations
Lin, K. W., Fock, K. M., & Li, J. W. (2025) · Digestive Endoscopy · doi:10.1111/den.70056 · Cited by 2
Colorectal cancer worldwide: epidemiological trends, economic burden, and the promise of AI-driven solutions
Le, D. T. P., & Pham, T. D. (2025) · Exploration of Medicine · doi:10.37349/emed.2025.1001371 · Cited by 4
Role of artificial intelligence in the detection and characterization of gastrointestinal premalignant and early malignant lesions
Asmar, N. E., Baydoun, M., Mrad, J., & Barada, K. (2025) · World Journal of Gastroenterology · doi:10.3748/wjg.v31.i44.111160 · Cited by 4
Artificial intelligence in gastroenterology: where are we and where are we going?
Lovat, L. B. (2024) · Gastrointestinal Nursing · doi:10.12968/gasn.2024.22.sup3.s6 · Cited by 2
Comparison of the diagnostic performance of narrow-band imaging endocytoscopy and staining-based endocytoscopy for colorectal lesions
Liu, M., Li, Y., Fan, J., Zhu, H., Ni, F., Ma, T., Cui, E., Zhang, N., & Xu, H. (2025) · Digestive and Liver Disease · doi:10.1016/j.dld.2025.08.012 · Cited by 2
Effectiveness of artificial intelligence in improving colonoscopy quality
Gadour, E., Hassan, Z. A., Hashim, A., Miutescu, B. P., & Okasha, H. (2024) · The Egyptian Journal of Internal Medicine · doi:10.1186/s43162-024-00371-z · Cited by 1
Endoscopic approaches to reduce the incidence of interval colon cancer
Chandan, S. (2025) · Gastrointestinal Endoscopy · doi:10.1016/j.gie.2025.05.021 · Cited by 1
Adjunct Tools for Colonoscopy Enhancement: A Comprehensive Review
Dei, N. N., Mazomenos, E. B., Zhang, S., Bano, S., Montiel, J. M. M., Stoyanov, D. V., & Ciuti, G. (2025) · IEEE Transactions on Medical Robotics and Bionics · doi:10.1109/tmrb.2025.3573024 · Cited by 1
Radiomics and artificial intelligence-based prediction of tumor response in digestive system neoplasm: a systematic review and meta-analysis
Yu, S., Gong, M., Wang, H., Liu, H., & Deng, M. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1795060 · Cited by 1
Artificial intelligence in predicting colorectal polyp histology: Systematic review and meta-analysis of diagnostic accuracy in real-time procedures
Curlej, P., & Soldera, J. (2026) · World Journal of Gastrointestinal Endoscopy · doi:10.4253/wjge.v18.i3.116381 · Cited by 1
Computer-assisted and unassisted optical diagnosis of colorectal polyps: results from a prospective pragmatic implementation study
Medawar, E., Oleksiw, M., Rex, D. K., Hassan, C., Djinbachian, R., Michal, V., Battat, R. J., Deslandres, É., Bouin, M., Kiow, J. L. C., et al. (2026) · Endoscopy · doi:10.1055/a-2882-7665 · Cited by 1
Current Status of Diagnosis and Treatment of Colorectal Cancer in Asian Countries: A Questionnaire Survey
Tanaka, H., Oka, S., Shiotani, A., Sugimoto, M., Suzuki, H., Naito, Y., Handa, O., Hisamatsu, T., Fukudo, S., Fujishiro, M., et al. (2023) · Digestion · doi:10.1159/000531706 · Cited by 1
Artificial Intelligence (AI) in Colonoscopy
Misawa, M., & Kudo, S. (2024) · Springer eBooks · doi:10.1007/978-981-97-2732-2_4 · Cited by 1
Self-citation
Diagnostic performance and agreement of auditors for evaluation of computer-aided optical polyp diagnosis: Prospective study
Huang, F., Dimbu, T. I., Rex, D. K., Pohl, H., Hassan, C., Djinbachian, R., Michal, V., Kim, D. H., Amani, B., Haddouch, N., et al. (2025) · Endoscopy International Open · doi:10.1055/a-2631-8030 · Cited by 1
Big Brother Is Watching: Artificial Intelligence in Endoscopy — Hype or Reality?
Cheema, H. I., & Tharian, B. (2022) · NEJM Evidence · doi:10.1056/evide2200098 · Cited by 1
Progenda 2000: programas genéricos para el desarrollo de aptitudes básicas para el aprendizaje
Ruiz, C. U., & Pérez, E. M. G. (2005) · Dialnet (Universidad de la Rioja)
Artificial intelligence for polyp characterization: easy as ABC
Ahmad, O. F. (2022) · Endoscopy · doi:10.1055/a-1931-4332
Applying AutoML to Colorectal Lesion Classification: A Comparison of Three Commercial PlatformsPreprint
Mahajan, N., Muralidharan, S. S., Holzwanger, E. A., Berzin, T. M., & Brown, J. R. G. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4458023
Artificial Intelligence in Gastroenterology: Current Status
Misawa, M., & Kudo, S. (2023) · Journal of Japan Society of Computer Aided Surgery · doi:10.5759/jscas.25.65
Self-citation
One Flew Over… AI‘s Nest
Cintezǎ, M. (2023) · MAEDICA – a Journal of Clinical Medicine · doi:10.26574/maedica.2023.18.3.387
Current Status and Challenges of Gastrointestinal Endoscopy Diagnosis with AI
Okamoto, Y., Ozawa, T., Shibata, J., Yoshio, T., Hirasawa, T., Fujisaki, J., Gotouda, T., & Tada, T. (2024) · Health Evaluation and Promotion · doi:10.7143/jhep.51.215
Applications of Artificial Intelligence in Gastroenterology and Hepatology
Sood, N., Chirayath, S. G., Bahirwani, J. T., Patel, H., Kim, E. S., Reddy‐Patel, N., Lin, H., & Martins, N. B. (2024) · Artificial intelligence · doi:10.5772/intechopen.115047

Authors: Barua, I., Wieszczy, P., Kudo, S.-E., Misawa, M., Holme, Ø., Gulati, S., Williams, S., Mori, K., Itoh, H., Takishima, K., Mochizuki, K., Miyata, Y., Mochida, K., Akimoto, Y., Kuroki, T., Morita, Y., Shiina, O., Kato, S., Nemoto, T., Hayee, B., Patel, M., Gunasingam, N., Kent, A., Emmanuel, A., Munck, C., Nilsen, J. A., Hvattum, S. A., Frigstad, S. O., Tandberg, P., Løberg, M., Kalager, M., Haji, A., Bretthauer, M., & Mori, Y.
Affiliations: Oslo University Hospital (NO)
SHOWA Medical University Northern Yokohama Hospital (JP)
King's College Hospital NHS Foundation Trust (GB)
Nagoya University (JP)
Vestre Viken Hospital Trust (NO)
ORCID: Barua, Ishita
Wieszczy, Paulina
Kudo, Shin-ei
Misawa, Masashi
Holme, Øyvind
Gulati, Shraddha
Williams, Sophie
Mori, Kensaku
Itoh, Hayato
Mochizuki, Kenichi
Akimoto, Yoshika
Kuroki, Takanori
Hayee, Bu
Patel, Mehul
Gunasingam, Nishmi
Kent, Alexandra
Emmanuel, Andrew
Nilsen, Jens Aksel
Frigstad, Svein Oskar
Løberg, Magnus
Kalager, Mette
Haji, Amyn
Bretthauer, Michael
Mori, Yuichi
Venue: NEJM Evidence
Publisher: Massachusetts Medical Society
Published: 2022-05-24
Volume: 1
Issue: 6
Pages: EVIDoa2200003-EVIDoa2200003
ISSN: 2766-5526 (ISSN-L)
DOI: 10.1056/evidoa2200003
OpenAlex: W4223989602
Cited by: 119
References: 19
Citations by year: 2026: 16 · 2025: 36 · 2024: 30 · 2023: 28 · 2022: 8
FWCI: 10.67
Type: journal article
Language: en
Open access: closed
Locations: NEJM Evidence (publishedVersion)
PubMed (publishedVersion)
Topics: Health Sciences › Medicine › Oncology › Colorectal Cancer Screening and Detection
Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords: neoplastic polyps, computer-aided diagnosis, colonoscopy, polyp histology, sensitivity and specificity, small polyps, sigmoid colon, rectum, artificial intelligence in endoscopy, histopathology comparison, colorectal cancer screening
MeSH terms: Adult, Aged, Artificial Intelligence, Colonic Neoplasms, Colonic Polyps, Colonoscopy, Diagnosis, Computer-Assisted, Female, Humans, Male, Middle Aged, Sensitivity and Specificity

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Real-Time Artificial Intelligence–Based Optical Diagnosis of Neoplastic Polyps during Colonoscopy

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks