at Google ScholarTitle data
Ali, Nairveen ; Bolenz, Christian ; Todenhöfer, Tilman ; Stenzel, Arnulf ; Deetmar, Peer ; Kriegmair, Martin ; Knoll, Thomas ; Porubsky, Stefan ; Hartmann, Arndt ; Popp, Jürgen ; Kriegmair, Maximilian C. ; Bocklitz, Thomas:
Deep learning-based classification of blue light cystoscopy imaging during transurethral resection of bladder tumors.
In: Scientific Reports.
Vol. 11
(2021)
Issue 1
.
- 11629.
ISSN 2045-2322
DOI: https://doi.org/10.1038/s41598-021-91081-x
Abstract in another language
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.
Further data
| Item Type: | Article in a journal |
|---|---|
| Refereed: | Yes |
| Institutions of the University: | Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science > Lehrstuhl Künstliche Intelligenz in der Mikroskopie und Spektroskopie > Lehrstuhl Künstliche Intelligenz in der Mikroskopie und Spektroskopie - Univ.-Prof. Dr. Thomas Wilhelm Bocklitz |
| Result of work at the UBT: | No |
| DDC Subjects: | 000 Computer Science, information, general works > 004 Computer science 500 Science > 530 Physics |
| Date Deposited: | 11 May 2023 11:08 |
| Last Modified: | 11 May 2023 11:08 |
| URI: | https://eref.uni-bayreuth.de/id/eprint/76405 |