Artificial intelligence in colorectal cancer: a review

Artificial intelligence in colorectal cancer: a review

Автор: Gurjeet Singh

Журнал: Сибирский онкологический журнал @siboncoj

Рубрика: Обзоры

Статья в выпуске: 3 т.22, 2023 года.

Бесплатный доступ

The study objective: the study objective is to examine the use of artificial intelligence (AI) in the diagnosis, treatment, and prognosis of Colorectal Cancer (CRC) and discuss the future potential of AI in CRC. Material and Methods. The Web of Science, Scopus, PubMed, Medline, and eLIBRARY databases were used to search for the publications. A study on the application of Artificial Intelligence (AI) to the diagnosis, treatment, and prognosis of Colorectal Cancer (CRC) was discovered in more than 100 sources. In the review, data from 83 articles were incorporated. Results. The review article explores the use of artificial intelligence (AI) in medicine, specifically focusing on its applications in colorectal cancer (CRC). It discusses the stages of AI development for CRC, including molecular understanding, image-based diagnosis, drug design, and individualized treatment. The benefits of AI in medical image analysis are highlighted, improving diagnosis accuracy and inspection quality. Challenges in AI development are addressed, such as data standardization and the interpretability of machine learning algorithms. The potential of AI in treatment decision support, precision medicine, and prognosis prediction is discussed, emphasizing the role of AI in selecting optimal treatments and improving surgical precision. Ethical and regulatory considerations in integrating AI are mentioned, including patient trust, data security, and liability in AI-assisted surgeries. The review emphasizes the importance of an AI standard system, dataset standardization, and integrating clinical knowledge into AI algorithms. Overall, the article provides an overview of the current research on AI in CRC diagnosis, treatment, and prognosis, discussing its benefits, challenges, and future prospects in improving medical outcomes.

Еще

Artificial intelligence (ai), deep learning (dl), machine learning (ml), diagnosis, treatment, crc

Короткий адрес: https://sciup.org/140300186

IDR: 140300186   |   DOI: 10.21294/1814-4861-2023-22-3-99-107

Список литературы Artificial intelligence in colorectal cancer: a review

  • Acs B., Rantalainen M., Hartman J. Artificial intelligence as the next step towards precision pathology. J Intern Med. 2020; 288(1): 62-81. https://doi.org/10.1111/joim.13030.
  • El Hajjar A., Rey J.F. Artificial intelligence in gastrointestinal endoscopy: general overview. Chin Med J (Engl). 2020; 133(3): 326-34. https://doi.org/10.1097/CM9.0000000000000623.
  • Min J.K., Kwak M.S., Cha J.M. Overview of Deep Learning in Gastrointestinal Endoscopy. Gut Liver. 2019; 13(4): 388-93. https://doi.org/10.5009/gnl18384.
  • Onder D., Sarioglu S., Karacali B. Automated labelling of cancer textures in colorectal histopathology slides using quasi-supervised learning. Micron. 2013; 47: 33-42. https://doi.org/10.1016/j.micron.2013.01.003.
  • Roadknight C., Aickelin U., Qiu G., Scholefield J., Durrant L. Supervised learning and anti-learning of colorectal cancer classes and survival rates from cellular biology parameters. Proceedings 2012 IEEE international conference on systems, man, and cybernetics. 2012: 797-802. https://doi.org/10.1109/icsmc.2012.6377825.
  • Chen Y., Carroll R.J., Hinz E.R., Shah A., Eyler A.E., Denny J.C., Xu H. Applying active learning to high-throughput phenotyping algorithms for electronic health records data. J Am Med Inform Assoc. 2013; 20(2): 253-9. https://doi.org/10.1136/amiajnl-2013-001945.
  • Le Berre C., Sandborn W.J., Aridhi S., Devignes M.D., Fournier L., Smail-TabboneM., Danese S., Peyrin-BirouletL. Application of Artificial Intelligence to Gastroenterology and Hepatology. Gastroenterology. 2020; 158(1): 76-94. https://doi.org/10.1053/j.gastro.2019.08.058.
  • Jagga Z., Gupta D. Machine learning for biomarker identification in cancer research - developments toward its clinical application. Per Med. 2015; 12(4): 371-87. https://doi.org/10.2217/pme.15.5.
  • Low S.K., Nakamura Y. The road map of cancer precision medicine with the innovation of advanced cancer detection technology and personalized immunotherapy. Jpn J Clin Oncol. 2019; 49(7): 596-603. https://doi.org/10.1093/jjco/hyz073.
  • Singh G., Nager P. A case Study on Nutek India Limited Regarding Deep Falling in Share Price. Researchers World--Journal of Arts, Science & Commerce. 2012; 3(2): 64-8.
  • Nager P., Singh G. An Analysis of Outliers For Fraud Detection in Indian Stock Market. Researchers World--Journal of Arts, Science & Commerce. 2012; 3(4): 10-5.
  • Nagar P., Issar G.S. Detection of outliers in stock market using regression analysis. 2021. https://doi.org/10.5281/zenodo.6047417.
  • Singh G. Machine Learning Models in Stock Market Prediction. arXiv e-prints, arXiv-2202. 2022.
  • Shi M., Zhang B. Semi-supervised learning improves gene expression-based prediction of cancer recurrence. Bioinformatics. 2011; 27(21): 3017-23. https://doi.org/10.1093/bioinformatics/btr502.
  • Gulati S., Patel M., Emmanuel A., Haji A., Hayee B., Neumann H. The future of endoscopy: Advances in endoscopic image innovations. Dig Endosc. 2020; 32(4): 512-22. https://doi.org/10.1111/den.13481.
  • Wang P., Berzin T.M., Glissen Brown J.R., Bharadwaj S., Becq A., Xiao X., Liu P., Li L., Song Y., Zhang D., Li Y., Xu G., Tu M., Liu X. Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study. Gut. 2019; 68(10): 1813-9. https://doi.org/10.1136/gutjnl-2018-317500.
  • Kang J., Gwak J. Ensemble of instance segmentation models for polyp segmentation in colonoscopy images. IEEE Access. 2019. 7: 26440-7. https://doi.org/10.1109/access.2019.2900672.
  • Eisner R., Greiner R., Tso V., Wang H., Fedorak R.N. A machine-learned predictor of colonic polyps based on urinary metabolomics. Biomed Res Int. 2013. https://doi.org/10.1155/2013/303982.
  • Köküer M., Naguib R.N., Jancovic P, Younghusband H.B., Green R.C. Cancer risk analysis in families with hereditary nonpolyposis colorectal cancer. IEEE Trans Inf Technol Biomed. 2006; 10(3): 581-7. https://doi.org/10.1109/titb.2006.872054.
  • Bell C.S., Puerto G.A., Mariottini G.L., Valdastri P. Six DOF motion estimation for teleoperated flexible endoscopes using optical flow: A comparative study. 2014 IEEE international conference on robotics and automation (ICRA). 2014: 5386-92. https://doi.org/10.1109/icra.2014.6907651.
  • Liu Z., Wang S., Dong D., Wei J., Fang C., Zhou X., Sun K., Li L., Li B., Wang M., Tian J. The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges. Theranostics. 2019; 9(5): 1303-22. https://doi.org/10.7150/thno.30309.
  • Yang T., Liang N., Li J., Yang Y., Li Y., Huang Q., Li R., He X., Zhang H. Intelligent imaging technology in diagnosis of colorectal cancer using deep learning. IEEE Access 2019; 7: 178839-47. https://doi.org/10.1109/access.2019.2958124.
  • Dalca A., Danagoulian G., Kikinis R., Schmidt E., Golland P. Sparse classification for computer aided diagnosis using learned dictionaries. Medical Image Computing and Computer-Assisted Intervention. 2011; 537-45.
  • Regge D., Halligan S. CAD: how it works, how to use it, performance. Eur J Radiol. 2013; 82(8): 1171-6. https://doi.org/10.1016/j.ejrad.2012.04.022.
  • Summers R.M., Handwerker L.R., Pickhardt P.J., Van Uitert R.L., Deshpande K.K., Yeshwant S., Yao J., Franaszek M. Performance of a previously validated CT colonography computer-aided detection system in a new patient population. AJR Am J Roentgenol. 2008; 191(1): 168-74. https://doi.org/10.2214/AJR.07.3354.
  • Chowdhury T.A., Whelan P.F., Ghita O. A fully automatic CAD-CTC system based on curvature analysis for standard and low-dose CT data. IEEE Trans Biomed Eng. 2008; 55(3): 888-901. https://doi.org/10.1109/TBME.2007.909506.
  • Nappi J.J., Hironaka T., Yoshida H. Detection of colorectal masses in CT colonography: Application of deep residual networks for differentiating masses from normal colon anatomy. Medical imaging 2018: Computer-aided diagnosis. Bellingham: Spie-Int Soc Optical Engineering. https://doi.org/10.1117/12.2293848.
  • Taylor S.A., Iinuma G., Saito Y., Zhang J., Halligan S. CT colonography: computer-aided detection of morphologically flat T1 colonic carcinoma. Eur Radiol. 2008; 18(8): 1666-73. https://doi.org/10.1007/s00330-008-0936-7.
  • Summers R.M. Current concepts in computer-aided detection for CT colonography. 2010 7th IEEE international symposium on biomedical imaging: From nano to macro. 2010: 269-72. https://doi.org/10.1109/isbi.2010.5490363.
  • Lee J.G., Hyo Kim J., Hyung Kim S., Sun Park H., Ihn Choi B. A straightforward approach to computer-aided polyp detection using a polypspecific volumetric feature in CT colonography. Comput Biol Med. 2011; 41(9): 790-801. https://doi.org/10.1016/j.compbiomed.2011.06.015.
  • Nappi J.J., Hironaka T., Regge D., Yoshida H. Deep transfer learning of virtual endoluminal views for the detection of polyps in CT colonography. Medical imaging 2016: Computer-aided diagnosis. Bellingham: Spie-Int Soc Optical Engineering, 2015. https://doi.org/10.1117/12.2217260.
  • Näppi J., Frimmel H., Yoshida H. Virtual endoscopic visualization of the colon by shape-scale signatures. IEEE Trans Inf Technol Biomed. 2005; 9(1): 120-31. https://doi.org/10.1109/titb.2004.837834.
  • van Wijk C., van Ravesteijn V.F., Vos F.M., van Vliet L.J. Detection and segmentation of colonic polyps on implicit isosurfaces by second principal curvature flow. IEEE Trans Med Imaging. 2010; 29(3): 688-98. https://doi.org/10.1109/TMI.2009.2031323.
  • Kim S.H., Lee J.M., Lee J.G., Kim J.H., Lefere P.A., Han J.K., Choi B.I. Computer-aided detection of colonic polyps at CT colonography using a Hessian matrix-based algorithm: preliminary study. AJR Am J Roentgenol. 2007; 189(1): 41-51. https://doi.org/10.2214/AJR.07.2072.
  • Nappi J.J., Pickhardt P., Kim D.H., Hironaka T., Yoshida H. Deep learning of contrast-coated serrated polyps for computer-aided detection in CT colonography. Medical imaging 2017: Computer-aided diagnosis. 2017. https://doi.org/10.1117/12.2255634.
  • Ma J., Dercle L., Lichtenstein P., Wang D., Chen A., Zhu J., Piessevaux H., Zhao J., Schwartz L.H., Lu L., Zhao B. Automated Identification of Optimal Portal Venous Phase Timing with Convolutional Neural Networks. Acad Radiol. 2020; 27(2): 10-18. https://doi.org/10.1016/j.acra.2019.02.024.
  • Soomro M.H., De Cola G., Conforto S., Schmid M., Giunta G., Guidi E., Neri E., Caruso D., Ciolina M., Laghi A. Automatic segmentation of colorectal cancer in 3D MRI by combining deep learning and 3D level-set algorithm-a preliminary study. 2018 IEEE 4th middle east conference on biomedical engineering. 2018; 198-203. https://doi.org/10.1109/mecbme.2018.8402433.
  • Soomro M.H., Coppotelli M., Conforto S., Schmid M., Giunta G., Del Secco L., Neri E., Caruso D., Rengo M., Laghi A. Automated Segmentation of Colorectal Tumor in 3D MRI Using 3D Multiscale Densely Connected Convolutional Neural Network. J Healthc Eng. 2019. https://doi.org/10.1155/2019/1075434.
  • Wang D., Xu J., Zhang Z., Li S., Zhang X., Zhou Y., Zhang X., Lu Y. Evaluation of Rectal Cancer Circumferential Resection Margin Using Faster Region-Based Convolutional Neural Network in High-Resolution Magnetic Resonance Images. Dis Colon Rectum. 2020; 63(2): 143-51. https://doi.org/10.1097/DCR.0000000000001519.
  • Wu Q.Y., Liu S.L., Sun P., Li Y., Liu G.W., Liu S.S., Hu J.L., Niu T.Y., Lu Y. Establishment and clinical application value of an automatic diagnosis platform for rectal cancer T-staging based on a deep neural network. Chin Med J (Engl). 2021; 134(7): 821-8. https://doi.org/10.1097/CM9.0000000000001401.
  • Joshi N., Bond S., Brady M. The segmentation of colorectal MRI images. Med Image Anal. 2010; 14(4): 494-509. https://doi.org/10.1016/j.media.2010.03.002.
  • Dabass M., Vashisth S., Vig R. Review of classification techniques using deep learning for colorectal cancer imaging modalities. 2019 6th International Conference on Signal Processing and Integrated Networks. 2019; 105-10. https://doi.org/10.1109/spin.2019.8711776.
  • Shiraishi T., Shinto E., Nearchou I.P., Tsuda H., Kajiwara Y., Einama T., Caie P.D., Kishi Y., Ueno H. Prognostic significance of mesothelin expression in colorectal cancer disclosed by area-specific four-point tissue microarrays. Virchows Arch. 2020; 477(3): 409-20. https://doi.org/10.1007/s00428-020-02775-y.
  • Pham T.D. Scaling of texture in training autoencoders for classification of histological images of colorectal cancer. Advances in neural networks. 2017; 524-32. https://doi.org/10.1007/978-3-319-59081-3_61.
  • Tiwari S. An analysis in tissue classification for colorectal cancer histology using convolution neural network and colour models. IJISMD. 2018; 9: 1-19. https://doi.org/10.4018/ijismd.2018100101.
  • Sirinukunwattana K., Ahmed Raza S.E., Tsang Y.W., Snead D.R., Cree I.A., Rajpoot N.M. Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images. IEEE Trans Med Imaging. 2016; 35(5): 1196-206. https://doi.org/10.1109/TMI.2016.2525803.
  • Koohababni N.A., Jahanifar M., Gooya A., Rajpoot N. Nuclei detection using mixture density networks. Machine learning in medical imaging. 2018; 241-8. https://doi.org/10.1007/978-3-030-00919-9_28.
  • Zhang X., Chen G., Saruta K., Terata Y. An end-to-end cells detection approach for colon cancer histology images. 10th international conference on digital image processing. 2018. https://doi.org/10.1117/12.2503067.
  • Xu J., Luo X., Wang G., Gilmore H., Madabhushi A. A Deep Convolutional Neural Network for segmenting and classifying epithelial and stromal regions in histopathological images. Neurocomputing. 2016; 191: 214-23. https://doi.org/10.1016/j.neucom.2016.01.034.
  • Chen H., Qi X., Yu L., Dou Q., Qin J., Heng P.A. DCAN: Deep contour-aware networks for object instance segmentation from histology images. Med Image Anal. 2017; 36: 135-46. https://doi.org/10.1016/j.media.2016.11.004.
  • Yoshida H., Yamashita Y., Shimazu T., Cosatto E., Kiyuna T., Taniguchi H., Sekine S., Ochiai A. Automated histological classification of whole slide images of colorectal biopsy specimens. Oncotarget. 2017; 8(53): 90719-29. https://doi.org/10.18632/oncotarget.21819.
  • Saito A., Cosatto E., Kiyuna T., Sakamoto M. Dawn of the digital diagnosis assisting system, can it open a new age for pathology? Medical imaging. Digital pathology. 2013. https://doi.org/10.1117/12.2008967.
  • Jin Y., Zhou C., Teng X., Ji J., Wu H., Liao J. Pai-wsit: An AI service platform with support for storing and sharing whole-slide images with metadata and annotations. IEEE Access. 2019; 7: 54780-6. https://doi.org/10.1109/access.2019.2913255.
  • Qaiser T., Tsang Y.W., Taniyama D., Sakamoto N., Nakane K., Epstein D., Rajpoot N. Fast and accurate tumor segmentation of histology images using persistent homology and deep convolutional features. Med Image Anal. 2019; 55: 1-14. https://doi.org/10.1016/j.media.2019.03.014.
  • Chao W.L., Manickavasagan H., Krishna S.G. Application of Artificial Intelligence in the Detection and Differentiation of Colon Polyps: A Technical Review for Physicians. Diagnostics (Basel). 2019; 9(3): 99. https://doi.org/10.3390/diagnostics9030099.
  • Zhou J., Wu L., Wan X., Shen L., Liu J., Zhang J., Jiang X., Wang Z., Yu S., Kang J., Li M., Hu S., Hu X., Gong D., Chen D., Yao L., Zhu Y., Yu H. A novel artificial intelligence system for the assessment of bowel preparation (with video). Gastrointest Endosc. 2020; 91(2): 428-35. https://doi.org/10.1016/j.gie.2019.11.026.
  • de Almeida Thomaz V., Sierra-Franco C.A., Raposo A.B. Training data enhancements for robust polyp segmentation in colonoscopy images. 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS). 2019; 192-7. https://doi.org/10.1109/cbms.2019.00047.
  • Azer S.A. Challenges Facing the Detection of Colonic Polyps: What Can Deep Learning Do? Medicina (Kaunas). 2019; 55(8): 473. https://doi.org/10.3390/medicina55080473.
  • Taha B., Dias J., Werghi N. Convolutional neural network as a feature extractor for automatic polyp detection. 2017 24th IEEE international conference on image processing. 2017; 2060-4. https://doi.org/10.1109/icip.2017.8296644.
  • Yao H., Stidham R.W., Soroushmehr R., Gryak J., Najarian K. Automated Detection of Non-Informative Frames for Colonoscopy Through a Combination of Deep Learning and Feature Extraction. Annu Int Conf IEEE Eng Med Biol Soc. 2019; 2402-6. https://doi.org/10.1109/EMBC.2019.8856625.
  • McNeil M.B., Gross S.A. Siri here, cecum reached, but please wash that fold: Will artificial intelligence improve gastroenterology? Gastrointest Endosc. 2020; 91(2): 425-7. https://doi.org/10.1016/j.gie.2019.10.027. Erratum in: Gastrointest Endosc. 2021; 93(2): 538.
  • Bravo D., Ruano J., Gomez M., Romero E. Automatic detection of colorectal polyps larger than 5 mm during colonoscopy procedures using visual descriptors. 14th international symposium on medical information processing and analysis. 2018. https://doi.org/10.1117/12.2511577.
  • de Lange T., Halvorsen P., Riegler M. Methodology to develop machine learning algorithms to improve performance in gastrointestinal endoscopy. World J Gastroenterol. 2018; 24(45): 5057-62. https://doi.org/10.3748/wjg.v24.i45.5057.
  • Mahmood F., Durr N.J. Deep learning-based depth estimation from a synthetic endoscopy image training set. Medical imaging 2018: Image processing. Bellingham: Spie-Int Soc Optical Engineering. 2018. https://doi.org/10.1117/12.2293785.
  • Mo X., Tao K., Wang Q., Wang G. An efficient approach for polyps detection in endoscopic videos based on faster R-CNN. 2018 24th international conference on pattern recognition. 2018; 3929-34. https://doi.org/10.1109/icpr.2018.8545174.
  • Zhu H., Fan Y., Lu H., Liang Z. Improving initial polyp candidate extraction for CT colonography. Phys Med Biol. 2010; 55(7): 2087-102. https://doi.org/10.1088/0031-9155/55/7/019.
  • Komeda Y., Handa H., Watanabe T., Nomura T., Kitahashi M., Sakurai T., Okamoto A., Minami T., Kono M., Arizumi T., Takenaka M., Hagiwara S., Matsui S., Nishida N., Kashida H., Kudo M. Computer-Aided Diagnosis Based on Convolutional Neural Network System for Colorectal Polyp Classification: Preliminary Experience. Oncology. 2017; 93s1: 30-4. https://doi.org/10.1159/000481227.
  • Zhang R., Zheng Y., Poon C.C.Y., Shen D., Lau J.Y.W. Polyp detection during colonoscopy using a regression-based convolutional neural network with a tracker. Pattern Recognit. 2018; 83: 209-19. https://doi.org/10.1016/j.patcog.2018.05.026.
  • Zhu X., Nemoto D., Mizuno T., Nakajima Y., Utano K., Aizawa M., Takezawa T., Sagara Y., Hayashi Y., Katsuki S., Yamamoto H., Hewett D.G., Togashi K. Identification of deeply invasive colorectal cancer on nonmagnified endoscopic images using artificial intelligence. Gastrointest Endosc. 2019.
  • Akbari M., Mohrekesh M., Nasr-Esfahani E., Soroushmehr S.M.R., Karimi N., Samavi S., Najarian K. Polyp Segmentation in Colonoscopy Images Using Fully Convolutional Network. Annu Int Conf IEEE Eng Med Biol Soc. 2018; 69-72. https://doi.org/10.1109/EMBC.2018.8512197.
  • Yu L., Chen H., Dou Q., Qin J., Heng P.A. Integrating Online and Offline Three-Dimensional Deep Learning for Automated Polyp Detection in Colonoscopy Videos. IEEE J Biomed Health Inform. 2017; 21(1): 65-75. https://doi.org/10.1109/JBHI.2016.2637004.
  • Yamada M., Saito Y., Imaoka H., Saiko M., Yamada S., Kondo H., Takamaru H., Sakamoto T., Sese J., Kuchiba A., Shibata T., Hamamoto R. Development of a real-time endoscopic image diagnosis support system using deep learning technology in colonoscopy. Sci Rep. 2019; 9(1): 14465. https://doi.org/10.1038/s41598-019-50567-5.
  • Allescher H.D., Weingart V. Optimizing Screening Colonoscopy: Strategies and Alternatives. Visc Med. 2019; 35(4): 215-25. https://doi.org/10.1159/000501835.
  • Lund Henriksen F., Jensen R., Kvale Stensland H., Johansen D., Riegler M.A., Halvorsen P. Performance of data enhancements and training optimization for neural network: A polyp detection case study. 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS), 2019. 287-93. https://doi.org/10.1109/cbms.2019.00067.
  • Ahmad O.F., Soares A.S., Mazomenos E., Brandao P., Vega R., Seward E., Stoyanov D., Chand M., Lovat L.B. Artificial intelligence and computer-aided diagnosis in colonoscopy: current evidence and future directions. Lancet Gastroenterol Hepatol. 2019; 4(1): 71-80. https://doi.org/10.1016/S2468-1253(18)30282-6.
  • Takamaru H., Wu S.Y.S., Saito Y. Endocytoscopy: technology and clinical application in the lower GI tract. Transl Gastroenterol Hepatol. 2020; 5: 40. https://doi.org/10.21037/tgh.2019.12.04.
  • Rath T., Morgenstern N., Vitali F., Atreya R., Neurath M.F. Advanced Endoscopic Imaging in Colonic Neoplasia. Visc Med. 2020; 36(1): 48-59. https://doi.org/10.1159/000505411.
  • Shahidi N., Rex D.K., Kaltenbach T., Rastogi A., Ghalehjegh S.H., Byrne M.F. Use of Endoscopic Impression, Artificial Intelligence, and Pathologist Interpretation to Resolve Discrepancies Between Endoscopy and Pathology Analyses of Diminutive Colorectal Polyps. Gastroenterology. 2020; 158(3): 783-5. https://doi.org/10.1053/j.gastro.2019.10.024.
  • Djinbachian R., Dube AJ., von Rentein D. Optical Diagnosis of Colorectal Polyps: Recent Developments. Curr Treat Options Gastroenterol. 2019; 17(1): 99-114. https://doi.org/10.1007/s11938-019-00220-x.
  • Kudo S.E., Misawa M., Mori Y., Hotta K., Ohtsuka K., Ikematsu H., Saito Y., Takeda K., Nakamura H., Ichimasa K., Ishigaki T., Toyoshima N., Kudo T., Hayashi T., Wakamura K., Baba T., Ishida F., Inoue H., Itoh H., Oda M., Mori K. Artificial Intelligence-assisted System Improves Endoscopic Identification of Colorectal Neoplasms. Clin Gastroenterol Hepatol. 2020; 18(8): 1874-81. https://doi.org/10.1016/j.cgh.2019.09.009.
  • Wang Y., He X., Nie H., Zhou J., Cao P., Ou C. Application of artificial intelligence to the diagnosis and therapy of colorectal cancer. Am J Cancer Res. 2020; 10(11): 3575-98.
  • O'Sullivan S., Nevejans N., Allen C., Blyth A., Leonard S., Pagallo U., Holzinger K., Holzinger A., Sajid M.I., Ashrafian H. Legal, regulatory, and ethical frameworks for development of standards in artificial intelligence (AI) and autonomous robotic surgery. Int J Med Robot. 2019; 15(1). https://doi.org/10.1002/rcs.1968.
  • Felfoul O., Mohammadi M., Taherkhani S., de Lanauze D., Zhong Xu Y., Loghin D., Essa S., Jancik S., Houle D., Lafleur M., Gabou-ry L., Tabrizian M., Kaou N., Atkin M., Vuong T., Batist G., Beauchemin N., Radzioch D., Martel S. Magneto-aerotactic bacteria deliver drug-containing nanoliposomes to tumour hypoxic regions. Nat Nanotechnol. 2016; 11(11): 941-7. https://doi.org/10.1038/nnano.2016.137.
Еще
Статья научная
SciUp 2024 © 2024 ©