Exploring the clinical potential of an automatic colonic polyp detection method based on the creation of energy maps
Open accessAbstract
BACKGROUND AND AIMS: Polyp miss-rate is a drawback of colonoscopy that increases significantly for small polyps. We explored the efficacy of an automatic computer-vision method for polyp detection. METHODS: Our method relies on a model that defines polyp boundaries as valleys of image intensity. Valley information is integrated into energy maps that represent the likelihood of the presence of a polyp. RESULTS: In 24 videos containing polyps from routine colonoscopies, all polyps were detected in at least one frame. The mean of the maximum values on the energy map was higher for frames with polyps than without (P < 0.001). Performance improved in high quality frames (AUC = 0.79 [95 %CI 0.70 - 0.87] vs. 0.75 [95 %CI 0.66 - 0.83]). With 3.75 set as the maximum threshold value, sensitivity and specificity for the detection of polyps were 70.4 % (95 %CI 60.3 % - 80.8 %) and 72.4 % (95 %CI 61.6 % - 84.6 %), respectively. CONCLUSION: Energy maps performed well for colonic polyp detection, indicating their potential applicability in clinical practice.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
Gastroenterologists use convolutional neural networks among other deep learning models to process images from endoscopy and ultrasound and detect abnormal structures such as colonic polyps.
“Gastroenterologists made use of convolutional neural networks among other deep learning models in order to process images from endoscopy and ultrasound (32) and detect abnormal structures such as colonic polyps (33).”
From Artificial Intelligence in Medicine: Today and Tomorrow (2020) · cites this paper as [33]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Still being checked.
Research works printed but not linked: 15
- [1]1. Levin B, Lieberman DA, McFarland B et al. Screening and surveillance for the early detection of colorectal cancer and adenomatous polyps, 2008: a joint guideline from the American Cancer Society, the US Multi-Society Task Force on Colorectal Cancer, and the American College of Radiology. Gastroenterology 2008;134:1570-95.
- [2]2. Hassan C, Quintero E, Dumonceau JM, et al. Post-polypectomy colonoscopy surveillance: ESGE Guideline. Endoscopy 2013;45:842-51.
- [3]3. van Rijn JC, Reitsma JB, Stoker J et al. Polyp miss rate determined by tandem colonoscopy: a systematic review. Am J Gastroenterol 2006;101:343-50.
- [4]4. Lee TJ, Rees CJ, Blanks RG et al. Colonoscopic factors associated with adenoma detection in a national colorectal cancer screening program. Endoscopy 2014;46:203- 11.
- [5]5. Belsey J, Crosta C, Epstein O, et al. Meta-analysis: the relative efficacy of oral bowel preparations for colonoscopy 1985-2010. Aliment Pharmacol Ther 2012;35:222-37.
- [6]6. ASGE Technology Committee, Konda VI, Chauhan SS, Dayyeh BKA et al. Endoscopes and devices to improve colon polyp detection. Gastrointest Endosc 2015;81:1122-9.
- [7]7. Tajbakhsh N, Gurudu SR, Liang J. Automatic polyp detection using global geometric constraints and local intensity variation patterns. Med Image Comput Assist Interv 2014;17:179-87.
- [8]8. Bernal J, Sánchez FJ, Vilariño F. Towards automatic polyp detection with a polyp appearance model. Pattern Recognition 2011; 45:3166-82.
- [9]9. Bernal J, Sánchez FJ, Fernández-Esparrach G et al. WM-DOVA maps for accurate polyp highlighting in colonoscopy: validation vs. saliency maps from physicians. Comput Med Imaging Graph 2015;43:99-111.
- [10]10. Endoscopic Classification Review Group. Update on the Paris classification of superficial neoplastic lesions in the digestive tract . Endoscopy 2005; 37: 570 -8.
- [11]11. Calderwood AH, Jacobson BC. Comprehensive validation of the Boston Bowel Preparation Scale. Gastrointest Endosc 2010;72:686-92.
- [12]12. Lebwohl B, Kastrinos F, Glick M et al. The impact of suboptimal bowel preparation on adenoma miss rates and the factors associated with early repeat colonoscopy. Gastrointest Endosc 2011;73:1207-14.
- [13]13. Regge D, Della Monica P, Galatola G et al. Efficacy of computer-aided detection as a second reader for 6-9 mm lesions at CT colonography. Radiology 2013;266:168-76.
- [14]14. Mang T, Bogoni L, Anand VX et al. CT colonography: effect of computer-aided detection of colonic polyps as a second and concurrent reader for general radiologists with moderate experience in CT colonography. Eur Radiol 2014;24:1466-76.
- [15]15. Halligan S, Mallett S, Altman DG et al. Incremental benefit of computer-aided detection when used as a second and concurrent reader of CT colonographic data: multiobserver study. Radiology 2011;258:469-476.
Cited by
Sorted by citations
Artificial Intelligence in Medicine: Today and Tomorrow
Briganti, G., & Le Moine, O. (2020) · Frontiers in Medicine · doi:10.3389/fmed.2020.00027 · Cited by 743 · see citing works
Wang, P., Xiao, X., Brown, J. R. G., Berzin, T. M., Tu, M., Xiong, F., Hu, X., Liu, P., Song, Y., Zhang, D., et al. (2018) · Nature Biomedical Engineering · doi:10.1038/s41551-018-0301-3 · Cited by 484 · see citing works
Artificial Intelligence-Assisted Polyp Detection for Colonoscopy: Initial Experience
Misawa, M., Kudo, S., Mori, Y., Cho, T., Kataoka, S., Yamauchi, A., Ogawa, Y., Maeda, Y., Takeda, K., Ichimasa, K., et al. (2018) · Gastroenterology · doi:10.1053/j.gastro.2018.04.003 · Cited by 395 · see citing works
Application of artificial intelligence in gastroenterology
Yang, Y. J., & Bang, C. S. (2019) · World Journal of Gastroenterology · doi:10.3748/wjg.v25.i14.1666 · Cited by 263 · see citing works
Artificial Intelligence in Colorectal Cancer Screening, Diagnosis and Treatment. A New Era
Mitsala, A., Tsalikidis, C., Pitiakoudis, M. S., Simopoulos, C. E., & Tsaroucha, A. K. (2021) · Current Oncology · doi:10.3390/curroncol28030149 · Cited by 281 · see citing works
Yamada, M., Saito, Y., Imaoka, H., Saiko, M., Yamada, S., Kondo, H., Takamaru, H., Sakamoto, T., Sese, J., Kuchiba, A., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-50567-5 · Cited by 263 · see citing works
Misawa, M., Kudo, S., Mori, Y., Hotta, K., Ohtsuka, K., Matsuda, T., Saito, S., Kudo, T., Baba, T., Ishida, F., et al. (2020) · Gastrointestinal Endoscopy · doi:10.1016/j.gie.2020.07.060 · Cited by 239 · see citing works
Ahmad, O. F., Soares, A. S., Mazomenos, E. B., Brandão, P., Vega, R., Seward, E. W., Stoyanov, D. V., Chand, M., & Lovat, L. B. (2018) · The Lancet. Gastroenterology & hepatology · doi:10.1016/s2468-1253(18)30282-6 · Cited by 215 · see citing works
Komeda, Y., Handa, H., Watanabe, T., Nomura, T., Kitahashi, M., Sakurai, T., Okamoto, A., Minami, T., Kono, M., Arizumi, T., et al. (2017) · Oncology · doi:10.1159/000481227 · Cited by 200 · see citing works
Study on detection rate of polyps and adenomas in artificial-intelligence-aided colonoscopy
Huang, J., Liu, W.-N., Zhang, Y.-Y., Bian, X.-Q., Wang, L.-J., Yang, Q., & Zhang, X.-D. (2020) · Saudi Journal of Gastroenterology · doi:10.4103/sjg.sjg_377_19 · Cited by 179 · see citing works
Artificial intelligence in gastroenterology: A state-of-the-art review
Kröner, P. T., Engels, M. M. L., Glicksberg, B. S., Johnson, K. W., Mzaik, O., van Hooft, J. E., Wallace, M. B., El‐Serag, H. B., & Krittanawong, C. (2021) · World Journal of Gastroenterology · doi:10.3748/wjg.v27.i40.6794 · Cited by 172 · see citing works
Artificial intelligence in gastrointestinal endoscopy: The future is almost here
Alagappan, M., Brown, J. R. G., Mori, Y., & Berzin, T. M. (2018) · World Journal of Gastrointestinal Endoscopy · doi:10.4253/wjge.v10.i10.239 · Cited by 163 · see citing works
Computer-aided diagnosis for colonoscopy
Mori, Y., Kudo, S., Berzin, T. M., Misawa, M., & Takeda, K. (2017) · Endoscopy · doi:10.1055/s-0043-109430 · Cited by 158 · see citing works
Artificial intelligence and colonoscopy: Current status and future perspectives
Kudo, S., Mori, Y., Misawa, M., Takeda, K., Kudo, T., Itoh, H., Oda, M., & Mori, K. (2019) · Digestive Endoscopy · doi:10.1111/den.13340 · Cited by 147 · see citing works
Quality assurance of computer-aided detection and diagnosis in colonoscopy
Vinsard, D. G., Mori, Y., Misawa, M., Kudo, S., Rastogi, A. N., Bağcı, U., Rex, D. K., & Wallace, M. B. (2019) · Gastrointestinal Endoscopy · doi:10.1016/j.gie.2019.03.019 · Cited by 141 · see citing works
Artificial Intelligence in Endoscopy
Okagawa, Y., Abe, S., Yamada, M., Oda, I., & Saito, Y. (2021) · Digestive Diseases and Sciences · doi:10.1007/s10620-021-07086-z · Cited by 126 · see citing works
Lee, J. Y., Jeong, J., Song, E. M., Ha, C., Lee, H., Koo, J. E., Yang, D., Kim, N., & Byeon, J. (2020) · Scientific Reports · doi:10.1038/s41598-020-65387-1 · Cited by 113 · see citing works
Artificial intelligence in gastrointestinal endoscopy: general overview
El Hajjar, A., & Rey, J.-F. (2020) · Chinese Medical Journal · doi:10.1097/cm9.0000000000000623 · Cited by 102 · see citing works
Artificial intelligence in gastrointestinal endoscopy
Pannala, R., Krishnan, K., Melson, J. E., Parsi, M. A., Schulman, A. R., Sullivan, S. A., Trikudanathan, G., Trindade, A. J., Watson, R. R., Maple, J. T., et al. (2020) · VideoGIE · doi:10.1016/j.vgie.2020.08.013 · Cited by 96 · see citing works
Nazarian, S., Glover, B. M., Ashrafian, H., Darzi, A. W., & TEARE, J. P. (2021) · Journal of Medical Internet Research · doi:10.2196/27370 · Cited by 93 · see citing works
Will Computer-Aided Detection and Diagnosis Revolutionize Colonoscopy?
Byrne, M. F., Shahidi, N. C., & Rex, D. K. (2017) · Gastroenterology · doi:10.1053/j.gastro.2017.10.026 · Cited by 76 · see citing works
Qiu, H., Ding, S., Liu, J., Wang, L., & Wang, X. (2022) · Current Oncology · doi:10.3390/curroncol29030146 · Cited by 80 · see citing works
Artificial Intelligence in Gastrointestinal Endoscopy
Abadir, A., Ali, M. F., Karnes, W. E., & Samarasena, J. (2020) · Clinical Endoscopy · doi:10.5946/ce.2020.038 · Cited by 74 · see citing works
Endomapper dataset of complete calibrated endoscopy procedures
Azagra, P., Sostres, C., Ferrández, Á., Riazuelo, L., Tomasini, C., Barbed, O. L., Morlana, J., Recasens, D., Batlle, V. M., Gómez-Rodríguez, J. J., et al. (2023) · Scientific Data · doi:10.1038/s41597-023-02564-7 · Cited by 80 · see citing works
Polyp Segmentation with Fully Convolutional Deep Neural Networks—Extended Evaluation Study
Guo, Y., Bernal, J., & Matuszewski, B. J. (2020) · Journal of Imaging · doi:10.3390/jimaging6070069 · Cited by 72 · see citing works
Self-citationArtificial intelligence-assisted colonoscopy: A review of current state of practice and research
Taghiakbari, M., Mori, Y., & Renteln, D. A. V. (2021) · World Journal of Gastroenterology · doi:10.3748/wjg.v27.i47.8103 · Cited by 71 · see citing works
Han, X., Liu, Q., Li, Y., Zhang, M., Liu, K., Kwok, L.-Y., Zhang, H., & Zhang, W. (2025) · Trends in Food Science & Technology · doi:10.1016/j.tifs.2025.104938 · Cited by 64 · see citing works
Mori, Y., Kudo, S., Misawa, M., & Mori, K. (2019) · VideoGIE · doi:10.1016/j.vgie.2018.10.006 · Cited by 64 · see citing works
Scope of Artificial Intelligence in Screening and Diagnosis of Colorectal Cancer
Goyal, H., Mann, R., Gandhi, Z., Perisetti, A., Ali, A. B., Ali, K. A., Sharma, N. R., Saligram, S., Tharian, B., & Inamdar, S. (2020) · Journal of Clinical Medicine · doi:10.3390/jcm9103313 · Cited by 65 · see citing works
Krenzer, A., Banck, M., Makowski, K., Hekalo, A., Fitting, D., Troya, J., Sudarevic, B., Zoller, W. G., Hann, A., & Puppe, F. (2023) · Journal of Imaging · doi:10.3390/jimaging9020026 · Cited by 62 · see citing works
Application of artificial intelligence to the diagnosis and therapy of colorectal cancer.
Wang, Y., He, X., Nie, H., Zhou, J., Cao, P., & Ou, C. (2020) · PubMed Central · Cited by 58 · see citing works
Young, E., Edwards, L., & Singh, R. (2023) · Cancers · doi:10.3390/cancers15215126 · Cited by 57 · see citing works
Quan, S. Y., Wei, M. T., Lee, J., Mohi-Ud-Din, R., Mostaghim, R., Sachdev, R. M., Siegel, D., Friedlander, Y., & Friedland, S. (2022) · Scientific Reports · doi:10.1038/s41598-022-10597-y · Cited by 55 · see citing works
Current status of artificial intelligence analysis for endoscopic ultrasonography
Kuwahara, T., Hara, K., Mizuno, N., Haba, S., Okuno, N., Koda, H., Miyano, A., & Fumihara, D. (2020) · Digestive Endoscopy · doi:10.1111/den.13880 · Cited by 54 · see citing works
COTR: Convolution in Transformer Network for End to End Polyp Detection
Shen, Z., Fu, R., Lin, C., & Zheng, S. (2021) · IEEE International Conference on Computer and Communications (ICCC) · doi:10.1109/iccc54389.2021.9674267 · Cited by 45 · see citing works
Artificial intelligence in gastroenterology and hepatology: Status and challenges
Cao, J., Lu, Z., Chen, M., Zhang, B., Juengpanich, S., Hu, J., Li, S., Topatana, W., Zhou, X.-Y., Xu, F., et al. (2021) · World Journal of Gastroenterology · doi:10.3748/wjg.v27.i16.1664 · Cited by 49 · see citing works
Figueiredo, P., Figueiredo, I. N., Pinto, L., Kumar, S., Tsai, Y. R., & Mamonov, A. (2019) · Endoscopy International Open · doi:10.1055/a-0808-4456 · Cited by 44 · see citing works
Current status and future perspective on artificial intelligence for lower endoscopy
Misawa, M., Kudo, S., Mori, Y., Maeda, Y., Ogawa, Y., Ichimasa, K., Kudo, T., Wakamura, K., Hayashi, T., Miyachi, H., et al. (2020) · Digestive Endoscopy · doi:10.1111/den.13847 · Cited by 42 · see citing works
Multiclassification of Endoscopic Colonoscopy Images Based on Deep Transfer Learning
Wang, Y., Feng, Z., Song, L., Liu, X., & Liu, S. (2021) · Computational and Mathematical Methods in Medicine · doi:10.1155/2021/2485934 · Cited by 42 · see citing works
Artificial Intelligence Applied to Gastrointestinal Diagnostics
Patel, V., Khan, M. N., Shrivastava, A., Sadiq, K., Ali, A., Moore, S. R., Brown, D. E., & Syed, S. (2019) · Journal of Pediatric Gastroenterology and Nutrition · doi:10.1097/mpg.0000000000002507 · Cited by 44 · see citing works
Predicting Colorectal Cancer Occurrence in IBD
Yalchin, M., Baker, A., Graham, T. A., & Hart, A. L. (2021) · Cancers · doi:10.3390/cancers13122908 · Cited by 42 · see citing works
Artificial intelligence in intestinal polyp and colorectal cancer prediction
Sharma, A., Kumar, R., Yadav, G., & Garg, P. (2023) · Cancer Letters · doi:10.1016/j.canlet.2023.216238 · Cited by 42 · see citing works
Xu, J., Zhao, R., Yu, Y., Zhang, Q., Bian, X., Wang, J., Ge, Z., & Qian, D. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102503 · Cited by 39 · see citing works
Sánchez, F. J., Bernal, J., Sánchez-Montes, C., de Miguel, C. R., & Fernández‐Esparrach, G. (2017) · Machine Vision and Applications · doi:10.1007/s00138-017-0864-0 · Cited by 38 · see citing works
Self-citationDetecting colorectal polyps via machine learning
Mori, Y., & Kudo, S. (2018) · Nature Biomedical Engineering · doi:10.1038/s41551-018-0308-9 · Cited by 39 · see citing works
Evaluation of novel LCI CAD EYE system for real time detection of colon polyps
Neumann, H., Kreft, A., Sivanathan, V., Rahman, F., & Galle, P. R. (2021) · PLoS ONE · doi:10.1371/journal.pone.0255955 · Cited by 39 · see citing works
Milluzzo, S. M., Cesaro, P., Grazioli, L. M., Olivari, N., & Spada, C. (2021) · Clinical Endoscopy · doi:10.5946/ce.2020.082 · Cited by 36 · see citing works
Automatic Polyp Segmentation Using Modified Recurrent Residual Unet Network
Tran, S.-T., Nguyen, H. M., Dang, H.-P., & Nguyen, T.-T. (2022) · IEEE Access · doi:10.1109/access.2022.3184773 · Cited by 34 · see citing works
Hoogenboom, S. A. M., Bağcı, U., & Wallace, M. B. (2019) · Techniques and Innovations in Gastrointestinal Endoscopy · doi:10.1016/j.tgie.2019.150634 · Cited by 32 · see citing works
Artificial intelligence technologies for the detection of colorectal lesions: The future is now
Attardo, S., Chandrasekar, V. T., Spadaccini, M., Maselli, R., Patel, H. K., Desai, M., Capogreco, A., Badalamenti, M., Galtieri, P. A., Pellegatta, G., et al. (2020) · World Journal of Gastroenterology · doi:10.3748/wjg.v26.i37.5606 · Cited by 32 · see citing works
Hurvitz, N., & Ilan, Y. (2023) · Clinics and Practice · doi:10.3390/clinpract13040089 · Cited by 32 · see citing works
Becq, A., Chandnani, M., Bharadwaj, S., Baran, B., Ernest-Suárez, K., Gabr, M. M., Brown, J. R. G., Sawhney, M., Pleskow, D. K., & Berzin, T. M. (2019) · Journal of Clinical Gastroenterology · doi:10.1097/mcg.0000000000001272 · Cited by 31 · see citing works
Emerging use of artificial intelligence in inflammatory bowel disease
Kohli, A., Holzwanger, E. A., & Levy, A. N. (2020) · World Journal of Gastroenterology · doi:10.3748/wjg.v26.i44.6923 · Cited by 32 · see citing works
Applications of Artificial Intelligence for the Diagnosis of Gastrointestinal Diseases
Pecere, S., Milluzzo, S. M., Esposito, G., Dilaghi, E., Telese, A., & Eusebi, L. H. (2021) · Diagnostics · doi:10.3390/diagnostics11091575 · Cited by 31 · see citing works
Houwen, B. B. S. L., Nass, K. J., Vleugels, J. L. A., Fockens, P., Hazewinkel, Y., & Dekker, E. (2022) · Gastrointestinal Endoscopy · doi:10.1016/j.gie.2022.08.043 · Cited by 32 · see citing works
Ethical Dilemmas of Using Artificial Intelligence in Medicine
Astărăstoae, V., Rogozea, L. M., Leașu, F., & Ioan, B. (2024) · American Journal of Therapeutics · doi:10.1097/mjt.0000000000001693 · Cited by 23 · see citing works
An end-to-end tracking method for polyp detectors in colonoscopy videos
Yu, T., Lin, N., Zhang, X., Pan, Y., Hu, H., Zheng, W., Liu, J., Hu, W., Duan, H., & Si, J. (2022) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2022.102363 · Cited by 26 · see citing works
Applications of Artificial Intelligence in Medicine
Srivastava, R. (2023) · Exploratory Research and Hypothesis in Medicine · doi:10.14218/erhm.2023.00048 · Cited by 33 · see citing works
Application of Artificial Intelligence in Gastrointestinal Endoscopy
Wu, J., Chen, J., & Cai, J. (2020) · Journal of Clinical Gastroenterology · doi:10.1097/mcg.0000000000001423 · Cited by 23 · see citing works
Computer-Aided Detection False Positives in Colonoscopy
Hsieh, Y., Tang, C.-P., Tseng, C., Lin, T., & Leung, F. W. (2021) · Diagnostics · doi:10.3390/diagnostics11061113 · Cited by 21 · see citing works
Scope of Artificial Intelligence in Gastrointestinal Oncology
Goyal, H., Sherazi, S. A. A., Mann, R., Gandhi, Z., Perisetti, A., Aziz, M., Chandan, S., Kopel, J. J., Tharian, B., Sharma, N. R., et al. (2021) · Cancers · doi:10.3390/cancers13215494 · Cited by 22 · see citing works
Spadaccini, M., Hassan, C., Alfarone, L., Rio, L. D., Maselli, R., Carrara, S., Galtieri, P. A., Pellegatta, G., Fugazza, A., Koleth, G., et al. (2022) · Gastrointestinal Endoscopy · doi:10.1016/j.gie.2021.12.031 · Cited by 21 · see citing works
Artificial Intelligence-assisted colonoscopy and colorectal cancer screening: Where are we going?
Spadaccini, M., Troya, J., Khalaf, K., Facciorusso, A., Maselli, R., Hann, A., & Repici, A. (2024) · Digestive and Liver Disease · doi:10.1016/j.dld.2024.01.203 · Cited by 20 · see citing works
Colonoscopic Image Synthesis For Polyp Detector Enhancement Via Gan And Adversarial Training
He, F., Chen, S., Li, S., Zhou, L., Zhang, H., Peng, H., & Huang, X. (2021) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi48211.2021.9434050 · Cited by 20 · see citing works
Sundaram, S., Choden, T., Mattar, M. C., Desai, S. H., & Desai, M. (2021) · Therapeutic Advances in Gastrointestinal Endoscopy · doi:10.1177/26317745211017809 · Cited by 19 · see citing works
Tang, C.-P., Chen, K., & Lin, T. (2021) · Sensors · doi:10.3390/s21165315 · Cited by 18 · see citing works
Wang, Y., Jheng, Y.-C., Sung, K., Lin, H.-E., Hsin, I., Chen, P., Chu, Y.-C., Lu, D., Wang, Y., Hou, M., et al. (2022) · Diagnostics · doi:10.3390/diagnostics12030613 · Cited by 17 · see citing works
Syed, S., & Stidham, R. W. (2020) · Inflammatory Bowel Diseases · doi:10.1093/ibd/izaa211 · Cited by 16 · see citing works
DAN-PD: Domain adaptive network with parallel decoder for polyp segmentation
Hu, J., Xu, Y., & Tang, Z. (2022) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2022.102124 · Cited by 16 · see citing works
Artificial Intelligence—The Rising Star in the Field of Gastroenterology and Hepatology
Stan‐Ilie, M., Şandru, V., Constantinescu, G., Plotogea, O., Rinja, E. M., Ţincu, I. F., Jichitu, A., Carasel, A. E., Butuc, A., & Popa, B. V. (2023) · Diagnostics · doi:10.3390/diagnostics13040662 · Cited by 17 · see citing works
Application of OCT in the Gastrointestinal Tract
Samel, N. S., & Mashimo, H. L. (2019) · Applied Sciences · doi:10.3390/app9152991 · Cited by 16 · see citing works
Optimizing Screening Colonoscopy: Strategies and Alternatives
Allescher, H., & Weingart, V. (2019) · Visceral Medicine · doi:10.1159/000501835 · Cited by 15 · see citing works
Iwatsubo, T., Uedo, N., Yamasaki, Y., Takeuchi, Y., & Ando, Y. (2017) · VideoGIE · doi:10.1016/j.vgie.2017.08.012 · Cited by 14 · see citing works
Hsieh, Y., & Leung, F. W. (2019) · Expert Review of Gastroenterology & Hepatology · doi:10.1080/17474124.2019.1694903 · Cited by 12 · see citing works
Mazumdar, S., Sinha, S., Jha, S., & Jagtap, B. (2023) · Indian Journal of Gastroenterology · doi:10.1007/s12664-022-01331-7 · Cited by 12 · see citing works
Evaluating false‐positive detection in a computer‐aided detection system for colonoscopy
Okumura, T., Imai, K., Misawa, M., Kudo, S., Hotta, K., Ito, S., Kishida, Y., Takada, K., Kawata, N., Maeda, Y. K., et al. (2024) · Journal of Gastroenterology and Hepatology · doi:10.1111/jgh.16491 · Cited by 11 · see citing works
Parkash, O., Siddiqui, A. T. S., Jiwani, U., Rind, F., Padhani, Z. A., Rizvi, A., Hoodbhoy, Z. A., & Das, J. K. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.1018937 · Cited by 12 · see citing works
Sánchez-Montes, C., Bernal, J., García‐Rodríguez, A., Córdova, H., & Fernández‐Esparrach, G. (2020) · Gastroenterología y Hepatología (English Edition) · doi:10.1016/j.gastre.2019.11.003 · Cited by 11 · see citing works
Self-citationAplicaciones de la inteligencia artificial en la medicina
Raraz-Vidal, J., & Raraz-Vidal, O. (2022) · Revista Peruana de Investigación en Salud · doi:10.35839/repis.6.3.1559 · Cited by 11 · see citing works
Kayser, M., Soberanis-Mukul, R. D., Zvereva, A.-M., Klare, P., Navab, N., & Albarqouni, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.02883 · Cited by 11 · see citing works
Wang, C., Xu, R., Xu, S., Meng, W., & Zhang, X. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106168 · Cited by 10 · see citing works
Sánchez-Montes, C., Bernal, J., García‐Rodríguez, A., Córdova, H., & Fernández‐Esparrach, G. (2020) · Gastroenterología y Hepatología · doi:10.1016/j.gastrohep.2019.11.004 · Cited by 10 · see citing works
Self-citationRole of Artificial Intelligence in Detecting Colonic Polyps during Intestinal Endoscopy
Modi, A., Kishore, B., Shetty, D. K., Sharma, V. P., Ibrahim, S., Hunain, R., Usman, N., Nayak, S. G., Kumar, S., & Paul, R. (2022) · Engineered Science · doi:10.30919/es8d697 · Cited by 11 · see citing works
Artificial Intelligence for Colorectal Polyp Detection and Characterization
Mori, Y., Kudo, S., Misawa, M., Takeda, K., Kudo, T., Itoh, H., Oda, M., & Mori, K. (2020) · Current Treatment Options in Gastroenterology · doi:10.1007/s11938-020-00287-x · Cited by 10 · see citing works
Artificial Intelligence in Colorectal Polyp Detection and Characterization
Le, A., Salifu, M. O., & McFarlane, I. M. (2021) · International Journal of Clinical Research & Trials · doi:10.15344/2456-8007/2021/157 · Cited by 10 · see citing works
Artificial Intelligence for Understanding Imaging, Text, and Data in Gastroenterology.
Stidham, R. W. (2020) · PubMed Central · Cited by 8 · see citing works
Kalahasty, R., Yerrapragada, G., Lee, J., Gopalakrishnan, K., Kaur, A., Muddaloor, P., Sood, D., Parikh, C., Gohri, J., Panjwani, G. A. R., et al. (2025) · Sensors · doi:10.3390/s25154735 · Cited by 7 · see citing works
An Overview on Computer Processing for Endoscopy and Colonoscopy Videos
Luca, M., Barbu, T., & Ciobanu, A. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-52190-5_1 · Cited by 7 · see citing works
Gan, P., Li, P., Xia, H., Zhou, X., & Tang, X. (2022) · Gastroenterología y Hepatología · doi:10.1016/j.gastrohep.2022.03.009 · Cited by 7 · see citing works
Lamotkin, A. I., Иванович, К. Д., & Анатольевич, Л. И. (2024) · FARMAKOEKONOMIKA Modern Pharmacoeconomics and Pharmacoepidemiology · doi:10.17749/2070-4909/farmakoekonomika.2024.267 · Cited by 6 · see citing works
A review of water exchange and artificial intelligence in improving adenoma detection
Tang, C.-P., Shao, P., Hsieh, Y., & Leung, F. W. (2020) · Tzu Chi Medical Journal · doi:10.4103/tcmj.tcmj_88_20 · Cited by 6 · see citing works
Artificial intelligence in colorectal cancer management
Cianci, P., & Restini, E. (2021) · WArtificial Intelligence in Cancer · doi:10.35713/aic.v2.i6.79 · Cited by 6 · see citing works
Emerging role of artificial intelligence in gastroenterology and hepatology
Shrestha, U. K. (2025) · World Journal of Gastroenterology · doi:10.3748/wjg.v31.i39.111495 · Cited by 8 · see citing works
Polyp Detection in Colonoscopy Images using Improved Deformable DETR
Mohammad, F., Sharma, V., & Das, P. K. (2022) · IEEE Region 10 Conference (TENCON) · doi:10.1109/tencon55691.2022.9977600 · Cited by 5 · see citing works
Attention augmented residual autoencoder for efficient polyp segmentation
Sushama, G., & Menon, G. C. (2022) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22814 · Cited by 5 · see citing works
Improving U-Net with Attention Mechanism for Medical Image Segmentation Applications
Honorato, E. S., Uchida, M. A. S., Traina, A. J. M., & Wolf, D. F. (2025) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms65348.2025.00060 · Cited by 4 · see citing works
Khanyile, R., Marima, R. M., Mbeje, M., Mutambirwa, S. B. A., Montwedi, D., & Dlamini, Z. (2023) · Springer eBooks · doi:10.1007/978-3-031-21506-3_15 · Cited by 4 · see citing works
A local region proposals approach to instance segmentation for intestinal polyp detection
Hwang, M., Qian, Y., Wu, C., Jiang, W., Wang, D., Wei, J., Ding, K., & Hwang, K. (2022) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-022-01714-4 · Cited by 4 · see citing works
Mizukami, K., Fushimi, E., Sagami, R., Abe, T., Sato, T., Terashi, S., Fukuda, M., Nishikiori, H., Nagai, T., Kodama, M., et al. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12196332 · Cited by 4 · see citing works
Artificial intelligence in ophthalmology clinical trials: a narrative review
Rahmanipour, E., Afazel, S., Ashrafi, S., Sadeghi, E., Ghorbani, M. M., Zarranz‐Ventura, J., & Chhablani, J. K. (2025) · Graefe s Archive for Clinical and Experimental Ophthalmology · doi:10.1007/s00417-025-06997-1 · Cited by 3 · see citing works
- Authors
- Fernández-Esparrach, G., Bernal, J., López-Cerón, M., Córdova, H., Sánchez-Montes, C., Rodríguez de Miguel, C., & Sánchez, F.
- Affiliations
- Endoscopy Unit, Gastroenterology Department, Hospital Clínic, IDIBAPS, CIBEREHD, University of Barcelona, Barcelona, Spain
- Universitat Autònoma de Barcelona (ES)
- Hospital Clínic de Barcelona (ES)
- Venue
- Endoscopy
- Publisher
- Georg Thieme Verlag KG
- Published
- 2016-06-10
- Volume
- 48
- Issue
- 09
- Pages
- 837-842
- ISSN
- 0013-726X (ISSN-L)
- 1438-8812
- OpenAlex
- W2410282112
- Cited by
- 146
- References
- 0
- Citations by year
- 2026: 1 · 2025: 17 · 2024: 8 · 2023: 18 · 2022: 21 · 2021: 33 · 2020: 24 · 2019: 13 · 2018: 6 · 2017: 5
- FWCI
- 4.31
- Type
- journal article
- Language
- en
- Open access
- green
- ddd.uab.cat
- Licence
- public-domain
- Locations
- Endoscopy (publishedVersion)
- PubMed (publishedVersion)
- Dipòsit Digital de Documents de la UAB (Universitat Autònoma de Barcelona) (submittedVersion, public-domain)
- Funders
- Generalitat de Catalunya (2014SGR135, 2014-SGR-1470)
- Topics
- Health Sciences › Medicine › Oncology › Colorectal Cancer Screening and Detection
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- colorectal polyp detection, energy map, computer-aided detection, colonoscopy, polyp miss rate, small polyps
- MeSH terms
- Colonic Polyps, Colonoscopy, Humans, Image Interpretation, Computer-Assisted, Image Processing, Computer-Assisted, Video Recording
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
