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Author: Popp, Jürgen × search.filters.applied.f.series: Scientific reports 11 (2021) ×

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    Deep learning-based classification of blue light cystoscopy imaging during transurethral resection of bladder tumors
    ([London] : Macmillan Publishers Limited, part of Springer Nature, 2021) 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
    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.
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    Morpho-molecular signal correlation between optical coherence tomography and Raman spectroscopy for superior image interpretation and clinical diagnosis
    ([London] : Macmillan Publishers Limited, part of Springer Nature, 2021) Schie, Iwan W.; Placzek, Fabian; Knorr, Florian; Cordero, Eliana; Wurster, Lara M.; Hermann, Gregers G.; Mogensen, Karin; Hasselager, Thomas; Drexler, Wolfgang; Popp, Jürgen; Leitgeb, Rainer A.
    The combination of manifold optical imaging modalities resulting in multimodal optical systems allows to discover a larger number of biomarkers than using a single modality. The goal of multimodal imaging systems is to increase the diagnostic performance through the combination of complementary modalities, e.g. optical coherence tomography (OCT) and Raman spectroscopy (RS). The physical signal origins of OCT and RS are distinctly different, i.e. in OCT it is elastic back scattering of photons, due to a change in refractive index, while in RS it is the inelastic scattering between photons and molecules. Despite those diverse characteristics both modalities are also linked via scattering properties and molecular composition of tissue. Here, we investigate for the first time the relation of co-registered OCT and RS signals of human bladder tissue, to demonstrate that the signals of these complementary modalities are inherently intertwined, enabling a direct but more importantly improved interpretation and better understanding of the other modality. This work demonstrates that the benefit for using two complementary imaging approaches is, not only the increased diagnostic value, but the increased information and better understanding of the signal origins of both modalities. This evaluation confirms the advantages for using multimodal imaging systems and also paves the way for significant further improved understanding and clinically interpretation of both modalities in the future.