Deep learning-based classification of blue light cystoscopy imaging during transurethral resection of bladder tumors

Abstract Bladder cancer is one of the top 10 frequently occurring cancers and leads to most cancer deaths worldwide. Recently, blue light (BL) cystoscopy-based photodynamic diagnosis was introduced as a unique technology to enhance the detection of bladder cancer, particularly for the detection of f...

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Autores principales:	Nairveen Ali, Christian Bolenz, Tilman Todenhöfer, Arnulf Stenzel, Peer Deetmar, Martin Kriegmair, Thomas Knoll, Stefan Porubsky, Arndt Hartmann, Jürgen Popp, Maximilian C. Kriegmair, Thomas Bocklitz
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/bd4c6a671406465dae84b54dbf9d938c
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spelling	oai:doaj.org-article:bd4c6a671406465dae84b54dbf9d938c2021-12-02T15:56:41ZDeep learning-based classification of blue light cystoscopy imaging during transurethral resection of bladder tumors10.1038/s41598-021-91081-x2045-2322https://doaj.org/article/bd4c6a671406465dae84b54dbf9d938c2021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-91081-xhttps://doaj.org/toc/2045-2322Abstract Bladder cancer is one of the top 10 frequently occurring cancers and leads to most cancer deaths worldwide. Recently, blue light (BL) cystoscopy-based photodynamic diagnosis was introduced as a unique technology to enhance the detection of bladder cancer, particularly for the detection of flat and small lesions. Here, we aim to demonstrate a BL image-based artificial intelligence (AI) diagnostic platform using 216 BL images, that were acquired in four different urological departments and pathologically identified with respect to cancer malignancy, invasiveness, and grading. Thereafter, four pre-trained convolution neural networks were utilized to predict image malignancy, invasiveness, and grading. The results indicated that the classification sensitivity and specificity of malignant lesions are 95.77% and 87.84%, while the mean sensitivity and mean specificity of tumor invasiveness are 88% and 96.56%, respectively. This small multicenter clinical study clearly shows the potential of AI based classification of BL images allowing for better treatment decisions and potentially higher detection rates.Nairveen AliChristian BolenzTilman TodenhöferArnulf StenzelPeer DeetmarMartin KriegmairThomas KnollStefan PorubskyArndt HartmannJürgen PoppMaximilian C. KriegmairThomas BocklitzNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Nairveen Ali Christian Bolenz Tilman Todenhöfer Arnulf Stenzel Peer Deetmar Martin Kriegmair Thomas Knoll Stefan Porubsky Arndt Hartmann Jürgen Popp Maximilian C. Kriegmair Thomas Bocklitz Deep learning-based classification of blue light cystoscopy imaging during transurethral resection of bladder tumors
description	Abstract Bladder cancer is one of the top 10 frequently occurring cancers and leads to most cancer deaths worldwide. Recently, blue light (BL) cystoscopy-based photodynamic diagnosis was introduced as a unique technology to enhance the detection of bladder cancer, particularly for the detection of flat and small lesions. Here, we aim to demonstrate a BL image-based artificial intelligence (AI) diagnostic platform using 216 BL images, that were acquired in four different urological departments and pathologically identified with respect to cancer malignancy, invasiveness, and grading. Thereafter, four pre-trained convolution neural networks were utilized to predict image malignancy, invasiveness, and grading. The results indicated that the classification sensitivity and specificity of malignant lesions are 95.77% and 87.84%, while the mean sensitivity and mean specificity of tumor invasiveness are 88% and 96.56%, respectively. This small multicenter clinical study clearly shows the potential of AI based classification of BL images allowing for better treatment decisions and potentially higher detection rates.
format	article
author	Nairveen Ali Christian Bolenz Tilman Todenhöfer Arnulf Stenzel Peer Deetmar Martin Kriegmair Thomas Knoll Stefan Porubsky Arndt Hartmann Jürgen Popp Maximilian C. Kriegmair Thomas Bocklitz
author_facet	Nairveen Ali Christian Bolenz Tilman Todenhöfer Arnulf Stenzel Peer Deetmar Martin Kriegmair Thomas Knoll Stefan Porubsky Arndt Hartmann Jürgen Popp Maximilian C. Kriegmair Thomas Bocklitz
author_sort	Nairveen Ali
title	Deep learning-based classification of blue light cystoscopy imaging during transurethral resection of bladder tumors
title_short	Deep learning-based classification of blue light cystoscopy imaging during transurethral resection of bladder tumors
title_full	Deep learning-based classification of blue light cystoscopy imaging during transurethral resection of bladder tumors
title_fullStr	Deep learning-based classification of blue light cystoscopy imaging during transurethral resection of bladder tumors
title_full_unstemmed	Deep learning-based classification of blue light cystoscopy imaging during transurethral resection of bladder tumors
title_sort	deep learning-based classification of blue light cystoscopy imaging during transurethral resection of bladder tumors
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/bd4c6a671406465dae84b54dbf9d938c
work_keys_str_mv	AT nairveenali deeplearningbasedclassificationofbluelightcystoscopyimagingduringtransurethralresectionofbladdertumors AT christianbolenz deeplearningbasedclassificationofbluelightcystoscopyimagingduringtransurethralresectionofbladdertumors AT tilmantodenhofer deeplearningbasedclassificationofbluelightcystoscopyimagingduringtransurethralresectionofbladdertumors AT arnulfstenzel deeplearningbasedclassificationofbluelightcystoscopyimagingduringtransurethralresectionofbladdertumors AT peerdeetmar deeplearningbasedclassificationofbluelightcystoscopyimagingduringtransurethralresectionofbladdertumors AT martinkriegmair deeplearningbasedclassificationofbluelightcystoscopyimagingduringtransurethralresectionofbladdertumors AT thomasknoll deeplearningbasedclassificationofbluelightcystoscopyimagingduringtransurethralresectionofbladdertumors AT stefanporubsky deeplearningbasedclassificationofbluelightcystoscopyimagingduringtransurethralresectionofbladdertumors AT arndthartmann deeplearningbasedclassificationofbluelightcystoscopyimagingduringtransurethralresectionofbladdertumors AT jurgenpopp deeplearningbasedclassificationofbluelightcystoscopyimagingduringtransurethralresectionofbladdertumors AT maximilianckriegmair deeplearningbasedclassificationofbluelightcystoscopyimagingduringtransurethralresectionofbladdertumors AT thomasbocklitz deeplearningbasedclassificationofbluelightcystoscopyimagingduringtransurethralresectionofbladdertumors
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Deep learning-based classification of blue light cystoscopy imaging during transurethral resection of bladder tumors

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