An Efficient Framework for Video Documentation of Bladder Lesions for Cystoscopy: A Proof-of-Concept Study.
Cystoscopy atlas
Cystoscopy documentation
Data distribution
Data management
Video for cystoscopy
Video-based documentation
Journal
Journal of medical systems
ISSN: 1573-689X
Titre abrégé: J Med Syst
Pays: United States
ID NLM: 7806056
Informations de publication
Date de publication:
03 Oct 2022
03 Oct 2022
Historique:
received:
14
04
2022
accepted:
07
09
2022
entrez:
3
10
2022
pubmed:
4
10
2022
medline:
6
10
2022
Statut:
epublish
Résumé
Processing full-length cystoscopy videos is challenging for documentation and research purposes. We therefore designed a surgeon-guided framework to extract short video clips with bladder lesions for more efficient content navigation and extraction. Screenshots of bladder lesions were captured during transurethral resection of bladder tumor, then manually labeled according to case identification, date, lesion location, imaging modality, and pathology. The framework used the screenshot to search for and extract a corresponding 10-seconds video clip. Each video clip included a one-second space holder with a QR barcode informing the video content. The success of the framework was measured by the secondary use of these short clips and the reduction of storage volume required for video materials. From 86 cases, the framework successfully generated 249 video clips from 230 screenshots, with 14 erroneous video clips from 8 screenshots excluded. The HIPPA-compliant barcodes provided information of video contents with a 100% data completeness. A web-based educational gallery was curated with various diagnostic categories and annotated frame sequences. Compared with the unedited videos, the informative short video clips reduced the storage volume by 99.5%. In conclusion, our framework expedites the generation of visual contents with surgeon's instruction for cystoscopy and potential incorporation of video data towards applications including clinical documentation, education, and research.
Identifiants
pubmed: 36190581
doi: 10.1007/s10916-022-01862-8
pii: 10.1007/s10916-022-01862-8
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
73Subventions
Organisme : NCI NIH HHS
ID : R01 CA260426
Pays : United States
Organisme : NIH HHS
ID : R01 CA260426
Pays : United States
Informations de copyright
© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Références
Witjes JA, Bruins HM, Cathomas R, Comperat EM, Cowan NC, Gakis G, et al. European Association of Urology Guidelines on Muscle-invasive and Metastatic Bladder Cancer: Summary of the 2020 Guidelines. Eur Urol. 2021;79(1):82-104. https://doi.org/10.1016/j.eururo.2020.03.055 .
doi: 10.1016/j.eururo.2020.03.055
pubmed: 32360052
Engelsgjerd JS, Deibert CM. Cystoscopy. StatPearls [Internet]. 2020.
Douglas-Moore J, Lewis R, Patrick J. The importance of clinical documentation. The Bulletin of the Royal College of Surgeons of England. 2015.
Lenherr SM, Crosby EC, Cameron AP. Cystoscopic findings: a video tutorial. Int Urogynecol J. 2015;26(6):921-3. https://doi.org/10.1007/s00192-014-2614-4 .
doi: 10.1007/s00192-014-2614-4
pubmed: 25619539
pmcid: 4936533
Ronstrom C, Lai HH. Presenting an atlas of Hunner lesions in interstitial cystitis which can be identified with office cystoscopy. Neurourol Urodyn. 2020;39(8):2394-400. https://doi.org/10.1002/nau.24500 .
doi: 10.1002/nau.24500
pubmed: 32902893
Eminaga O, Eminaga N, Semjonow A, Breil B. Diagnostic Classification of Cystoscopic Images Using Deep Convolutional Neural Networks. JCO Clin Cancer Inform. 2018;2:1-8. https://doi.org/10.1200/CCI.17.00126 .
doi: 10.1200/CCI.17.00126
pubmed: 30652604
Ikeda A, Nosato H, Kochi Y, Kojima T, Kawai K, Sakanashi H, et al. Support System of Cystoscopic Diagnosis for Bladder Cancer Based on Artificial Intelligence. J Endourol. 2020;34(3):352-8. https://doi.org/10.1089/end.2019.0509 .
doi: 10.1089/end.2019.0509
pubmed: 31808367
pmcid: 7099426
Ikeda A, Nosato H, Kochi Y, Negoro H, Kojima T, Sakanashi H, et al. Cystoscopic Imaging for Bladder Cancer Detection Based on Stepwise Organic Transfer Learning with a Pretrained Convolutional Neural Network. J Endourol. 2021;35(7):1030-5. https://doi.org/10.1089/end.2020.0919 .
doi: 10.1089/end.2020.0919
pubmed: 33148020
Mutaguchi J, Morooka KI, Kobayashi S, Umehara A, Miyauchi S, Kinoshita F, et al. Artificial intelligence for segmentation of bladder tumor cystoscopic images performed by U-Net with dilated convolution. J Endourol. 2022. https://doi.org/10.1089/end.2021.0483 .
doi: 10.1089/end.2021.0483
pubmed: 35018828
Shkolyar E, Jia X, Chang TC, Trivedi D, Mach KE, Meng MQ, et al. Augmented Bladder Tumor Detection Using Deep Learning. Eur Urol. 2019;76(6):714-8. https://doi.org/10.1016/j.eururo.2019.08.032 .
doi: 10.1016/j.eururo.2019.08.032
pubmed: 31537407
pmcid: 6889816
Suarez-Ibarrola R, Kriegmair M, Waldbillig F, Grune B, Negassi M, Parupalli U, et al. A novel endoimaging system for endoscopic 3D reconstruction in bladder cancer patients. Minim Invasive Ther Allied Technol. 2020:1–8. https://doi.org/10.1080/13645706.2020.1761833 .
O'Leary DE. Artificial intelligence and big data. IEEE intelligent systems. 2013;28(2):96-9.
doi: 10.1109/MIS.2013.39
Danciu I, Cowan JD, Basford M, Wang X, Saip A, Osgood S, et al. Secondary use of clinical data: the Vanderbilt approach. Journal of biomedical informatics. 2014;52:28-35.
doi: 10.1016/j.jbi.2014.02.003
Mingers J. The paucity of multimethod research: a review of the information systems literature. Information systems journal. 2003;13(3):233-49.
doi: 10.1046/j.1365-2575.2003.00143.x
Coleman JF, Hansel DE. Benign Diseases of the Bladder. Surg Pathol Clin. 2008;1(1):129-58. https://doi.org/10.1016/j.path.2008.07.001 .
doi: 10.1016/j.path.2008.07.001
pubmed: 26837905
Salvadores M, Alexander PR, Musen MA, Noy NF. BioPortal as a Dataset of Linked Biomedical Ontologies and Terminologies in RDF. Semant Web. 2013;4(3):277-84.
doi: 10.3233/SW-2012-0086
Edge SB, Compton CC. The American Joint Committee on Cancer: the 7th edition of the AJCC cancer staging manual and the future of TNM. Ann Surg Oncol. 2010;17(6):1471-4. https://doi.org/10.1245/s10434-010-0985-4 .
doi: 10.1245/s10434-010-0985-4
pubmed: 20180029
Comperat EM, Burger M, Gontero P, Mostafid AH, Palou J, Roupret M, et al. Grading of Urothelial Carcinoma and The New "World Health Organisation Classification of Tumours of the Urinary System and Male Genital Organs 2016". Eur Urol Focus. 2019;5(3):457-66. https://doi.org/10.1016/j.euf.2018.01.003 .
doi: 10.1016/j.euf.2018.01.003
pubmed: 29366854
Daneshmand S, Bazargani ST, Bivalacqua TJ, Holzbeierlein JM, Willard B, Taylor JM, et al. Blue light cystoscopy for the diagnosis of bladder cancer: Results from the US prospective multicenter registry. Urol Oncol. 2018;36(8):361 e1- e6. https://doi.org/10.1016/j.urolonc.2018.04.013 .
doi: 10.1016/j.urolonc.2018.04.013
Amin MB, Greene FL, Edge SB, Compton CC, Gershenwald JE, Brookland RK, et al. The Eighth Edition AJCC Cancer Staging Manual: Continuing to build a bridge from a population-based to a more "personalized" approach to cancer staging. CA Cancer J Clin. 2017;67(2):93-9. https://doi.org/10.3322/caac.21388 .
doi: 10.3322/caac.21388
pubmed: 28094848
Patridge EF, Bardyn TP. Research Electronic Data Capture (REDCap). J Med Libr Assoc. 2018;106(1):142-4. https://doi.org/10.5195/jmla.2018.319 .
doi: 10.5195/jmla.2018.319
pmcid: 5764586
Palubinskas G. Image similarity/distance measures: what is really behind MSE and SSIM? International Journal of Image and Data Fusion. 2017;8(1):32-53.
doi: 10.1080/19479832.2016.1273259
Palubinskas G. Mystery behind similarity measures MSE and SSIM. 2014 IEEE International Conference on Image Processing (ICIP): IEEE; 2014. p. 575–9.
Stark MM. QR Codes: The Technical Guide. A. K. Peters, Ltd.; 2013.
Carbon CC. Understanding human perception by human-made illusions. Front Hum Neurosci. 2014;8:566. https://doi.org/10.3389/fnhum.2014.00566 .
doi: 10.3389/fnhum.2014.00566
pubmed: 25132816
pmcid: 4116780
Pearson J, Naselaris T, Holmes EA, Kosslyn SM. Mental Imagery: Functional Mechanisms and Clinical Applications. Trends Cogn Sci. 2015;19(10):590-602. https://doi.org/10.1016/j.tics.2015.08.003 .
doi: 10.1016/j.tics.2015.08.003
pubmed: 26412097
pmcid: 4595480
Noorman S, Neville DA, Simanova I. Words affect visual perception by activating object shape representations. Sci Rep. 2018;8(1):14156. https://doi.org/10.1038/s41598-018-32483-2 .
doi: 10.1038/s41598-018-32483-2
pubmed: 30237542
pmcid: 6148044
Ayoade G, Karande V, Khan L, Hamlen K. Decentralized IoT data management using blockchain and trusted execution environment. 2018 IEEE International Conference on Information Reuse and Integration (IRI): IEEE; 2018. p. 15–22.
Norvell DC. Study types and bias-Don't judge a study by the abstract's conclusion alone. Evid Based Spine Care J. 2010;1(2):7-10. https://doi.org/10.1055/s-0028-1100908 .
doi: 10.1055/s-0028-1100908
pubmed: 23637661
pmcid: 3623096
Liu F, Hernandez-Cabronero M, Sanchez V, Marcellin MW, Bilgin A. The current role of image compression standards in medical imaging. Information. 2017;8(4):131.
doi: 10.3390/info8040131
Pianykh OS. Digital imaging and communications in medicine (DICOM): a practical introduction and survival guide. Springer; 2012.