Optimizing cystoscopy and TURBT: enhanced imaging and artificial intelligence.
Journal
Nature reviews. Urology
ISSN: 1759-4820
Titre abrégé: Nat Rev Urol
Pays: England
ID NLM: 101500082
Informations de publication
Date de publication:
09 Jul 2024
09 Jul 2024
Historique:
accepted:
03
06
2024
medline:
10
7
2024
pubmed:
10
7
2024
entrez:
9
7
2024
Statut:
aheadofprint
Résumé
Diagnostic cystoscopy in combination with transurethral resection of the bladder tumour are the standard for the diagnosis, surgical treatment and surveillance of bladder cancer. The ability to inspect the bladder in its current form stems from a long chain of advances in imaging science and endoscopy. Despite these advances, bladder cancer recurrence and progression rates remain high after endoscopic resection. This stagnation is a result of the heterogeneity of cancer biology as well as limitations in surgical techniques and tools, as incomplete resection and provider-specific differences affect cancer persistence and early recurrence. An unmet clinical need remains for solutions that can improve tumour delineation and resection. Translational advances in enhanced cystoscopy technologies and artificial intelligence offer promising avenues to overcoming the progress plateau.
Identifiants
pubmed: 38982304
doi: 10.1038/s41585-024-00904-9
pii: 10.1038/s41585-024-00904-9
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.
Références
Herr, H. W. Role of repeat resection in non-muscle-invasive bladder cancer. J. Natl Compr. Cancer Netw. 13, 1041–1046 (2015).
doi: 10.6004/jnccn.2015.0123
Vianello, A. et al. Repeated white light transurethral resection of the bladder in nonmuscle-invasive urothelial bladder cancers: systematic review and meta-analysis. J. Endourol. 25, 1703–1712 (2011).
pubmed: 21936670
doi: 10.1089/end.2011.0081
Grimm, M.-O. et al. Effect of routine repeat transurethral resection for superficial bladder cancer: a long-term observational study. J. Urol. 170, 433–437 (2003).
pubmed: 12853793
doi: 10.1097/01.ju.0000070437.14275.e0
Mariappan, P. et al. Good quality white-light transurethral resection of bladder tumours (GQ-WLTURBT) with experienced surgeons performing complete resections and obtaining detrusor muscle reduces early recurrence in new non-muscle-invasive bladder cancer: validation across time and place and recommendation for benchmarking. BJU Int. 109, 1666–1673 (2012).
Samplaski, M. K. & Jones, J. S. Two centuries of cystoscopy: the development of imaging, instrumentation and synergistic technologies. BJU Int. 103, 154–158 (2009).
pubmed: 19076146
doi: 10.1111/j.1464-410X.2008.08244.x
Herr H. W. Max Nitze, the cystoscope and urology. J. Urol. 176, 1313–1316 (2006).
Howard, J. M., Woldu, S. L., Daneshmand, S. & Lotan, Y. Enhanced endoscopy with IMAGE1 S CHROMA improves detection of nonmuscle invasive bladder cancer during transurethral resection. J. Endourol. 35, 647–651 (2021).
pubmed: 33176470
doi: 10.1089/end.2020.0821
Shkolyar, E. et al. Augmented bladder tumor detection using deep learning. Eur. Urol. 76, 714–718 (2019).
pubmed: 31537407
pmcid: 6889816
doi: 10.1016/j.eururo.2019.08.032
Akand, M. et al. Quality control indicators for transurethral resection of non-muscle-invasive bladder cancer. Clin. Genitourin. Cancer 17, e784–e792 (2019).
pubmed: 31097388
doi: 10.1016/j.clgc.2019.04.014
Giacalone, N. J. et al. Long-term outcomes after bladder-preserving tri-modality therapy for patients with muscle-invasive bladder cancer: an updated analysis of the Massachusetts general hospital experience. Eur. Urol. 71, 952–960 (2017).
pubmed: 28081860
doi: 10.1016/j.eururo.2016.12.020
Brausi, M. et al. Variability in the recurrence rate at first follow-up cystoscopy after tur in stage Ta t1 transitional cell carcinoma of the bladder: a combined analysis of seven EORTC studies. Eur. Urol. 41, 523–531 (2002).
pubmed: 12074794
doi: 10.1016/S0302-2838(02)00068-4
Holzbeierlein, J. M. et al. Diagnosis and treatment of non-muscle invasive bladder cancer: AUA/SUO guideline: 2024 amendment. J. Urol. 211, 533–538 (2024).
pubmed: 38265030
doi: 10.1097/JU.0000000000003846
Cumberbatch, M. G. K. et al. Repeat transurethral resection in non-muscle-invasive bladder cancer: a systematic review. Eur. Urol. 73, 925–933 (2018).
pubmed: 29523366
doi: 10.1016/j.eururo.2018.02.014
Yanagisawa et al. Repeat transurethral resection for non-muscle-invasive bladder cancer: an updated systematic review and meta-analysis in the contemporary era. Eur. Urol. Focus 10, 41–56 (2023).
pubmed: 37495458
doi: 10.1016/j.euf.2023.07.002
Divrik, R. T., Şahin, A. F., Yildirim, Ü., Altok, M. & Zorlu, F. Impact of routine second transurethral resection on the long-term outcome of patients with newly diagnosed pT1 urothelial carcinoma with respect to recurrence, progression rate, and disease-specific survival: a prospective randomised clinical trial. Eur. Urol. 58, 185–190 (2010).
pubmed: 20303646
doi: 10.1016/j.eururo.2010.03.007
D’Andrea, D. et al. En bloc versus conventional resection of primary bladder tumor (eBLOC): a prospective, multicenter, open-label, phase 3 randomized controlled trial. Eur. Urol. Oncol. 6, 508–515 (2023).
pubmed: 37543464
doi: 10.1016/j.euo.2023.07.010
Suarez-Ibarrola, R. et al. Surgical checklist impact on recurrence-free survival of patients with non-muscle-invasive bladder cancer undergoing transurethral resection of bladder tumour. BJU Int. 123, 646–650 (2019).
pubmed: 30248235
doi: 10.1111/bju.14557
Mariappan, P. et al. Enhanced quality and effectiveness of transurethral resection of bladder tumour in non-muscle-invasive bladder cancer: a multicentre real-world experience from Scotland’s quality performance indicators programme. Eur. Urol. 78, 520–530 (2020).
pubmed: 32690321
doi: 10.1016/j.eururo.2020.06.051
Krieg, R. C., Messmann, H., Rauch, J., Seeger, S. & Knuechel, R. Metabolic characterization of tumor cell-specific protoporphyrin Ix accumulation after exposure to 5-aminolevulinic acid in human colonic cells. Photochem. Photobiol. 76, 518–525 (2007).
doi: 10.1562/0031-8655(2002)0760518MCOTCS2.0.CO2
Lange, N. et al. Photodetection of early human bladder cancer based on the fluorescence of 5-aminolaevulinic acid hexylester-induced protoporphyrin IX: a pilot study. Br. J. Cancer 80, 185–193 (1999).
pubmed: 10389995
pmcid: 2363006
doi: 10.1038/sj.bjc.6690338
Kriegmair, M. et al. Detection of early bladder cancer by 5-aminolevulinic acid induced porphyrin fluorescence. J. Urol. 155, 105–109 (1996).
pubmed: 7490803
doi: 10.1016/S0022-5347(01)66559-5
Burger, M. et al. Photodynamic diagnosis of non–muscle-invasive bladder cancer with hexaminolevulinate cystoscopy: a meta-analysis of detection and recurrence based on raw data. Eur. Urol. 64, 846–854 (2013).
pubmed: 23602406
doi: 10.1016/j.eururo.2013.03.059
Daneshmand, S. et al. Blue light cystoscopy for the diagnosis of bladder cancer: results from the US prospective multicenter registry. Urol. Oncol. 36, 361.e1–361.e6 (2018).
pubmed: 29859728
doi: 10.1016/j.urolonc.2018.04.013
Chappidi, M. R. et al. Utility of blue light cystoscopy for post-bacillus Calmette-Guérin bladder cancer recurrence detection: implications for clinical trial recruitment and study comparisons. J. Urol. 207, 534–540 (2022).
pubmed: 34694916
doi: 10.1097/JU.0000000000002308
Daneshmand, S. et al. Efficacy and safety of blue light flexible cystoscopy with hexaminolevulinate in the surveillance of bladder cancer: a phase III, comparative, multicenter study. J. Urol. 199, 1158–1165 (2018).
pubmed: 29203268
doi: 10.1016/j.juro.2017.11.096
Yuan, H. et al. Therapeutic outcome of fluorescence cystoscopy guided transurethral resection in patients with non-muscle invasive bladder cancer: a meta-analysis of randomized controlled trials. PLoS ONE 8, e74142 (2013).
Maisch, P. et al. Blue vs white light for transurethral resection of non‐muscle‐invasive bladder cancer: an abridged Cochrane Review. BJU Int. 130, 730–740 (2022).
pubmed: 35238145
doi: 10.1111/bju.15723
Veeratterapillay, R. et al. Time to turn on the blue lights: a systematic review and meta-analysis of photodynamic diagnosis for bladder cancer. Eur. Urol. Open. Sci. 31, 17–27 (2021).
pubmed: 34467237
pmcid: 8385287
doi: 10.1016/j.euros.2021.06.011
Heer, R. et al. A randomized trial of PHOTOdynamic surgery in non-muscle-invasive bladder cancer. NEJM Evid. 1, EVIDoa2200092 (2022).
pubmed: 38319866
doi: 10.1056/EVIDoa2200092
de Angelis, M., Briganti, A., Montorsi, F. & Moschini, M. Re: a randomized trial of PHOTOdynamic surgery in non-muscle-invasive bladder cancer. Eur. Urol. 83, 477–478 (2023).
pubmed: 36806362
doi: 10.1016/j.eururo.2023.02.001
St-Laurent, M. P., Suderman, J. & Black, P. C. Re: a randomized trial of PHOTOdynamic surgery in non-muscle-invasive bladder cancer. Eur. Urol. 83, 298–299 (2023).
pubmed: 36604273
doi: 10.1016/j.eururo.2022.12.018
Chang, S. S. et al. Diagnosis and treatment of non-muscle invasive bladder cancer: AUA/SUO Guideline. J. Urol. 196, 1021–1029 (2016).
Babjuk, M. et al. European Association of Urology guidelines on non-muscle-invasive bladder cancer (TaT1 and carcinoma in situ) — 2019 update. Eur. Urol. 76, 639–657 (2019).
pubmed: 31443960
doi: 10.1016/j.eururo.2019.08.016
Flaig, T. W. et al. Bladder cancer, version 3.2020, NCCN clinical practice guidelines in oncology. J. Natl Compr. Canc. Netw. 18, 329–354 (2020).
pubmed: 32135513
doi: 10.6004/jnccn.2020.0011
Klaassen, Z. et al. Contemporary cost-consequence analysis of blue light cystoscopy with hexaminolevulinate in non-muscle-invasive bladder cancer. Can. Urol. Assoc. J. 11, 173 (2017).
pubmed: 28652875
pmcid: 5472462
doi: 10.5489/cuaj.4568
Garfield, S. S., Gavaghan, M. B., Armstrong, S. O. & Jones, J. S. The cost-effectiveness of blue light cystoscopy in bladder cancer detection: United States projections based on clinical data showing 4.5 years of follow up after a single hexaminolevulinate hydrochloride instillation. Can. J. Urol. 20, 6682–6689 (2013).
pubmed: 23587507
Williams, S. B., Gavaghan, M. B., Fernandez, A., Daneshmand, S. & Kamat, A. M. Macro and microeconomics of blue light cystoscopy with CYSVIEW® in non-muscle invasive bladder cancer. Urol. Oncol. Semin. Orig. Investig. 40, 10.e7–10.e12 (2022).
Gono, K. et al. Appearance of enhanced tissue features in narrow-band endoscopic imaging. J. Biomed. Opt. 9, 568 (2004).
pubmed: 15189095
doi: 10.1117/1.1695563
Li, K., Lin, T., Fan, X., Duan, Y. & Huang, J. Diagnosis of narrow-band imaging in non-muscle-invasive bladder cancer: a systematic review and meta-analysis: diagnosis of NMIBC by NBI. Int. J. Urol. 20, 602–609 (2013).
pubmed: 23113702
doi: 10.1111/j.1442-2042.2012.03211.x
Naselli, A. et al. A randomized prospective trial to assess the impact of transurethral resection in narrow band imaging modality on non-muscle-invasive bladder cancer recurrence. Eur. Urol. 61, 908–913 (2012).
pubmed: 22280855
doi: 10.1016/j.eururo.2012.01.018
Naito, S. et al. The Clinical Research Office of the Endourological Society (CROES) multicentre randomised trial of narrow band imaging-assisted transurethral resection of bladder tumour (TURBT) versus conventional white light imaging-assisted TURBT in primary non-muscle-invasive bladder cancer patients: trial protocol and 1-year results. Eur. Urol. 70, 506–515 (2016).
pubmed: 27117749
doi: 10.1016/j.eururo.2016.03.053
Kang, W. et al. Narrow band imaging-assisted transurethral resection reduces the recurrence risk of non-muscle invasive bladder cancer: a systematic review and meta-analysis. Oncotarget 8, 23880–23890 (2017).
pubmed: 27823975
doi: 10.18632/oncotarget.13054
Xiong, Y. et al. A meta-analysis of narrow band imaging for the diagnosis and therapeutic outcome of non-muscle invasive bladder cancer. PLoS ONE 12, e0170819 (2017).
Kamphuis, G. M. et al. Storz professional image enhancement system: a new technique to improve endoscopic bladder imaging. J. Cancer Sci. Ther. https://doi.org/10.4172/1948-5956.1000394 (2016).
Sonn, G. A. et al. Optical biopsy of human bladder neoplasia with in vivo confocal laser endomicroscopy. J. Urol. 182, 1299–1305 (2009).
pubmed: 19683270
doi: 10.1016/j.juro.2009.06.039
Naselli, A., Guarneri, A. & Pirola, G. M. Confocal laser endomicroscopy for bladder cancer detection: where do we stand? Appl. Sci. 12, 9990 (2022).
doi: 10.3390/app12199990
Huang, D. et al. Optical coherence tomography. Science 254, 1178–1181 (1991).
pubmed: 1957169
pmcid: 4638169
doi: 10.1126/science.1957169
Xiong, Y. Q. et al. Diagnostic accuracy of optical coherence tomography for bladder cancer: a systematic review and meta-analysis. Photodiagnosis Photodyn. Ther. 27, 298–304 (2019).
pubmed: 31185324
doi: 10.1016/j.pdpdt.2019.06.006
Draga, R. O. P. et al. In vivo bladder cancer diagnosis by high-volume Raman spectroscopy. Anal. Chem. 82, 5993–5999 (2010).
pubmed: 20524627
doi: 10.1021/ac100448p
Movasaghi, Z., Rehman, S. & Rehman, I. U. Raman spectroscopy of biological tissues. Appl. Spectrosc. Rev. 42, 493–541 (2007).
doi: 10.1080/05704920701551530
Castellino, R. A. Computer aided detection (CAD): an overview. Cancer Imaging 5, 17–19 (2005).
pubmed: 16154813
pmcid: 1665219
doi: 10.1102/1470-7330.2005.0018
Koh, D. M. et al. Artificial intelligence and machine learning in cancer imaging. Commun. Med. 2, 1–14 (2022).
doi: 10.1038/s43856-022-00199-0
Laurie, M. A. et al. Bladder cancer and artificial intelligence: emerging applications. Urol. Clin. North. Am. 51, 63–75 (2024).
pubmed: 37945103
doi: 10.1016/j.ucl.2023.07.002
Chang, T. C. et al. Real-time detection of bladder cancer using augmented cystoscopy with deep learning: a pilot study. J. Endourol. https://doi.org/10.1089/end.2023.0056 (2023).
Kriegmair, M. C. et al. Digital mapping of the urinary bladder: potential for standardized cystoscopy reports. Urology 104, 235–241 (2017).
pubmed: 28214573
doi: 10.1016/j.urology.2017.02.019
Hackner, R. et al. Panoramic imaging assessment of different bladder phantoms — an evaluation study. Urology 156, e103–e110 (2021)
pubmed: 34087314
doi: 10.1016/j.urology.2021.05.036
Soper, T. D., Porter, M. P. & Seibel, E. J. Surface mosaics of the bladder reconstructed from endoscopic video for automated surveillance. IEEE Trans. Biomed. Eng. 59, 1670–1680 (2012).
pubmed: 22481800
doi: 10.1109/TBME.2012.2191783
Lurie, K. L., Angst, R., Zlatev, D. V., Liao, J. C. & Ellerbee Bowden, A. K. 3D reconstruction of cystoscopy videos for comprehensive bladder records. Biomed. Opt. Express 8, 2106 (2017).
pubmed: 28736658
pmcid: 5516821
doi: 10.1364/BOE.8.002106
Falcon, N. et al. Innovative computer vision approach to 3D bladder model reconstruction from flexible cystoscopy. in Proc. SPIE 10852, Therapeutics and Diagnostics in Urology (SPIE, 2019).
Ben-Hamadou A., Daul C., Soussen C. Construction of extended 3D field of views of the internal bladder wall surface: a proof of concept. 3D Res. https://doi.org/10.1007/s13319-016-0095-6 (2016).
Lewis, A. et al. Real time localization of cystoscope angulation in 2D bladder phantom for telecystoscopy. in 2021 International Symposium on Medical Robotics, ISMR 2021 (IEEE, 2021).
Groenhuis, V., de Groot, A. G., Cornel, E. B., Stramigioli, S. & Siepel, F. J. 3-D and 2-D reconstruction of bladders for the assessment of inter-session detection of tissue changes: a proof of concept. Int. J. Comput. Assist. Radiol. Surg. https://doi.org/10.1007/s11548-023-02900-7 (2023).
doi: 10.1007/s11548-023-02900-7
pubmed: 37085675
pmcid: 10497453
Suarez-Ibarrola, R. et al. A novel endoimaging system for endoscopic 3D reconstruction in bladder cancer patients. Minim. Invasive Ther. Allied Technol. 31, 34–41 (2022).
pubmed: 32491933
doi: 10.1080/13645706.2020.1761833
Chen, P. et al. Real-time flexible endoscope navigation within bladder phantom having sparse non-distinct features is enhanced with robotic control. in Proc. SPIE 12034, Medical Imaging 2022: Image-Guided Procedures, Robotic Interventions, and Modeling (SPIE, 2022).
Zhou Y., Eimen R. L., Seibel E. J., Bowden A. K. Cost-efficient video synthesis and evaluation for development of virtual 3D endoscopy. IEEE J. Transl. Eng. Health Med. https://doi.org/10.1109/JTEHM.2021.3132193 (2021).
Bhambhvani, H. P. et al. Development of robust artificial neural networks for prediction of 5-year survival in bladder cancer. Urol. Oncol. 39, 193.e7–193.e12 (2021).
pubmed: 32593506
doi: 10.1016/j.urolonc.2020.05.009
Eimen R., Pillai, M., Scarpato, K., Bowden, A. A metric to predict the utility of cystoscopy frames in 3D bladder reconstructions. In: 35th EUS Annual Meeting (2022).
Eminaga, O. et al. An efficient framework for video documentation of bladder lesions for cystoscopy: a proof-of-concept study. J Med. Syst. https://doi.org/10.1007/s10916-022-01862-8 (2022).
Eminaga, O. et al. Conceptual framework and documentation standards of cystoscopic media content for artificial intelligence. J. Biomed. Inf. 142, 104369 (2023).
doi: 10.1016/j.jbi.2023.104369
Shore, N. D. & Gavaghan, M. B. Clinical and economic impact of blue light cystoscopy in the management of NMIBC at US ambulatory surgical centers: what is the site-of-service disparity? Urol. Oncol. 41, 207.e9–207.e16 (2023).
pubmed: 36564259
doi: 10.1016/j.urolonc.2022.11.014
Ali, S. Where do we stand in AI for endoscopic image analysis? Deciphering gaps and future directions. NPJ Digit. Med. 5, 184 (2022).
pubmed: 36539473
pmcid: 9767933
doi: 10.1038/s41746-022-00733-3
Chang, T. C. et al. Current trends in artificial intelligence application for endourology and robotic surgery. Urol. Clin. North. Am. 48, 151–160 (2021).
pubmed: 33218590
doi: 10.1016/j.ucl.2020.09.004
Teoh, J. Y. C. et al. A newly developed computer-aided endoscopic diagnostic system for bladder cancer detection. Eur. Urol. Open. Sci. 19, e1364–e1365 (2020).
doi: 10.1016/S2666-1683(20)33498-4
Gosnell, M. E., Polikarpov, D. M., Goldys, E. M., Zvyagin, A. V. & Gillatt, D. A. Computer-assisted cystoscopy diagnosis of bladder cancer. Urol. Oncol. 36, 8.e9–8.e15 (2018).
pubmed: 28958822
doi: 10.1016/j.urolonc.2017.08.026
Eminaga, O., Eminaga, N., Semjonow, A. & Breil, B. Diagnostic classification of cystoscopic images using deep convolutional neural networks. JCO Clin. Cancer Inf. 2, 1–8 (2018).
Lorencin, I., Anđelić, N., Španjol, J. & Car, Z. Using multi-layer perceptron with Laplacian edge detector for bladder cancer diagnosis. Artif. Intell. Med. 102, 101746 (2020).
pubmed: 31980088
doi: 10.1016/j.artmed.2019.101746
Ikeda, A. et al. Support system of cystoscopic diagnosis for bladder cancer based on artificial intelligence. J. Endourol. 34, 352–358 (2020).
pubmed: 31808367
pmcid: 7099426
doi: 10.1089/end.2019.0509
Yang, R. et al. Automatic recognition of bladder tumours using deep learning technology and its clinical application. Int. J. Med. Robot 17, e2194 (2021).
pubmed: 33119212
doi: 10.1002/rcs.2194
Du, Y. et al. A deep learning network-assisted bladder tumour recognition under cystoscopy based on Caffe deep learning framework and EasyDL platform. Int. J. Med. Robot. 17, 1–8 (2021).
pubmed: 32947648
doi: 10.1002/rcs.2169
Ali, N. et al. Deep learning-based classification of blue light cystoscopy imaging during transurethral resection of bladder tumors. Sci. Rep. 11, 1–10 (2021).
doi: 10.1038/s41598-021-91081-x
Ikeda, A. et al. Cystoscopic imaging for bladder cancer detection based on stepwise organic transfer learning with a pretrained convolutional neural network. J. Endourol. 35, 1030–1035 (2021).
pubmed: 33148020
doi: 10.1089/end.2020.0919
Ikeda, A. et al. PD26-02 real-time bladder tumor detection at clinics in flexible cystoscopy with white light and narrow band imaging using deep learning. J. Urol. 207, 487–488 (2022).
doi: 10.1097/JU.0000000000002574.02
Laurie, M. et al. Sequential modeling for cystoscopic image classification. in Proc. SPIE 12353, Advanced Photonics in Urology 123530B (SPIE, 2023).
Jia, X. et al. Tumor detection under cystoscopy with transformer-augmented deep learning algorithm. Phys. Med. Biol. 68, 165013 (2023).
doi: 10.1088/1361-6560/ace499
Wu, S. et al. An artificial intelligence system for the detection of bladder cancer via cystoscopy: a multicenter diagnostic study. J. Natl Cancer Inst. 114, 220–227 (2022).
pubmed: 34473310
doi: 10.1093/jnci/djab179
Negassi, M., Suarez-Ibarrola, R., Hein, S., Miernik, A. & Reiterer, A. Application of artificial neural networks for automated analysis of cystoscopic images: a review of the current status and future prospects. World J. Urol. 38, 2349–2358 (2020).
pubmed: 31925551
pmcid: 7508959
doi: 10.1007/s00345-019-03059-0
Yoo, J. W. et al. Deep learning diagnostics for bladder tumor identification and grade prediction using RGB method. Sci. Rep. 12, 1–8 (2022).
doi: 10.1038/s41598-022-22797-7
Mutaguchi, J. et al. Artificial intelligence for segmentation of bladder tumor cystoscopic images performed by U-net with dilated convolution. J. Endourol. 36, 827–834 (2022).
pubmed: 35018828
doi: 10.1089/end.2021.0483
Varnyú, D., Szirmay-Kalos, L. A comparative study of deep neural networks for real-time semantic segmentation during the transurethral resection of bladder tumors. Diagnostics https://doi.org/10.3390/diagnostics12112849 (2022).
Zhang, Q. et al. A comparative study of attention mechanism based deep learning methods for bladder tumor segmentation. Int. J. Med. Inf. 171, 104984 (2023).
doi: 10.1016/j.ijmedinf.2023.104984
Chang, S. et al. Bringing blue light cystoscopy to the office: digital staining on matched white and blue light cystoscopy videos. in Proc. SPIE PC12368, Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XXI PC123680P (SPIE, 2023).
Jia, X. et al. Flat lesion detection of white light cystoscopy with deep learning. in Proc. SPIE 12353, Advanced Photonics in Urology 123530D (SPIE, 2023).
Dilmaghani, S. & Coelho-Prabhu, N. Role of artificial intelligence in colonoscopy: a literature review of the past, present, and future directions. Tech. Innov. Gastrointest. Endosc. 25, 399–412 (2023).
doi: 10.1016/j.tige.2023.03.002
Chadebecq, F., Lovat, L. B. & Stoyanov, D. Artificial intelligence and automation in endoscopy and surgery. Nat. Rev. Gastroenterol. Hepatol. 20, 171–182 (2023).
pubmed: 36352158
doi: 10.1038/s41575-022-00701-y