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Bladder cancer (BC) is the eleventh most diagnosed cancer worldwide. The age-standardized incidence rate (per 100 000 person/years) is 9.0 for men and 2.2 for women [1]. Urothelial carcinoma (UC) represents about 90% of all bladder tumors, thus carrying an enormous social and economic burden [2]. UCs are classified in different stages and grades, depending on their invasiveness and on their degree of cytological abnormalities. The key aspect for a positive prognosis is the early and accurate diagnosis of the lesion stage, in order to identify the most aggressive disease forms and treat them promptly. It is well known that tissue metabolism constitutes a basic mechanism, which is at the base of many pathologies, especially BC. Being able to detect and characterize tissue metabolism and molecular fingerprints at the cellular level could be a key aspect in characterizing the pathology and enabling both early detection and therapy monitoring. The new European Union Horizon 2020 project called AMPLITUDE, the ‘Advanced Multimodal Photonics Laser Imaging Tool for Urothelial Diagnosis in Endoscopy’, starting in January 2020, proposes the development of an advanced multi-modal imaging tool exploiting new laser technologies in an approach combining confocal and non-linear imaging to fulfil unmet clinical needs in terms of the specificity and accuracy of urothelial cancer diagnosis and therapy monitoring. The project is coordinated by Tampere University (Finland) and carried out in cooperation with leading European research organizations including Aston Insitute of Photonic Technologies—AIPT (UK), Consiglio Nazionale delle Ricerche—CNR (Italy), Institute of Photonic Sciences—ICFO (Spain), University of Milan-Bicocca, Modus Research and Innovation Ltd. (UK) and University of Florence (Italy), as well as industrial partners: Ampliconyx Oy (Finland), Femtonics Ltd. (Hungary), HC Photonics (Taiwan), and LEONI Fiber Optics GmbH (Germany).

Bladder cancer (BC) is the eleventh most diagnosed cancer worldwide. The age-standardized incidence rate (per 100 000 person/years) is 9.0 for men and 2.2 for women [1]. Urothelial carcinoma (UC) represents about 90% of all bladder tumors, thus carrying an enormous social and economic burden [2]. UCs are classified in different stages and grades, depending on their invasiveness and on their degree of cytological abnormalities. The key aspect for a positive prognosis is the early and accurate diagnosis of the lesion stage, in order to identify the most aggressive disease forms and treat them promptly. It is well known that tissue metabolism constitutes a basic mechanism, which is at the base of many pathologies, especially BC. Being able to detect and characterize tissue metabolism and molecular fingerprints at the cellular level could be a key aspect in characterizing the pathology and enabling both early detection and therapy monitoring. The new European Union Horizon 2020 project called AMPLITUDE, the ‘Advanced Multimodal Photonics Laser Imaging Tool for Urothelial Diagnosis in Endoscopy’, starting in January 2020, proposes the development of an advanced multi-modal imaging tool exploiting new laser technologies in an approach combining confocal and non-linear imaging to fulfil unmet clinical needs in terms of the specificity and accuracy of urothelial cancer diagnosis and therapy monitoring. The project is coordinated by Tampere University (Finland) and carried out in cooperation with leading European research organizations including Aston Insitute of Photonic Technologies—AIPT (UK), Consiglio Nazionale delle Ricerche—CNR (Italy), Institute of Photonic Sciences—ICFO (Spain), University of Milan-Bicocca, Modus Research and Innovation Ltd. (UK) and University of Florence (Italy), as well as industrial partners: Ampliconyx Oy (Finland), Femtonics Ltd. (Hungary), HC Photonics (Taiwan), and LEONI Fiber Optics GmbH (Germany).

Bladder cancer (BC) is the eleventh most diagnosed cancer worldwide. The age-standardized incidence rate (per 100 000 person/years) is 9.0 for men and 2.2 for women [1]. Urothelial carcinoma (UC) represents about 90% of all bladder tumors, thus carrying an enormous social and economic burden [2]. UCs are classified in different stages and grades, depending on their invasiveness and on their degree of cytological abnormalities. The key aspect for a positive prognosis is the early and accurate diagnosis of the lesion stage, in order to identify the most aggressive disease forms and treat them promptly. It is well known that tissue metabolism constitutes a basic mechanism, which is at the base of many pathologies, especially BC. Being able to detect and characterize tissue metabolism and molecular fingerprints at the cellular level could be a key aspect in characterizing the pathology and enabling both early detection and therapy monitoring. The new European Union Horizon 2020 project called AMPLITUDE, the ‘Advanced Multimodal Photonics Laser Imaging Tool for Urothelial Diagnosis in Endoscopy’, starting in January 2020, proposes the development of an advanced multi-modal imaging tool exploiting new laser technologies in an approach combining confocal and non-linear imaging to fulfil unmet clinical needs in terms of the specificity and accuracy of urothelial cancer diagnosis and therapy monitoring. The project is coordinated by Tampere University (Finland) and carried out in cooperation with leading European research organizations including Aston Insitute of Photonic Technologies—AIPT (UK), Consiglio Nazionale delle Ricerche—CNR (Italy), Institute of Photonic Sciences—ICFO (Spain), University of Milan-Bicocca, Modus Research and Innovation Ltd. (UK) and University of Florence (Italy), as well as industrial partners: Ampliconyx Oy (Finland), Femtonics Ltd. (Hungary), HC Photonics (Taiwan), and LEONI Fiber Optics GmbH (Germany).

Bladder cancer (BC) is the eleventh most diagnosed cancer worldwide. The age-standardized incidence rate (per 100 000 person/years) is 9.0 for men and 2.2 for women [1]. Urothelial carcinoma (UC) represents about 90% of all bladder tumors, thus carrying an enormous social and economic burden [2]. UCs are classified in different stages and grades, depending on their invasiveness and on their degree of cytological abnormalities. The key aspect for a positive prognosis is the early and accurate diagnosis of the lesion stage, in order to identify the most aggressive disease forms and treat them promptly. It is well known that tissue metabolism constitutes a basic mechanism, which is at the base of many pathologies, especially BC. Being able to detect and characterize tissue metabolism and molecular fingerprints at the cellular level could be a key aspect in characterizing the pathology and enabling both early detection and therapy monitoring. The new European Union Horizon 2020 project called AMPLITUDE, the ‘Advanced Multimodal Photonics Laser Imaging Tool for Urothelial Diagnosis in Endoscopy’, starting in January 2020, proposes the development of an advanced multi-modal imaging tool exploiting new laser technologies in an approach combining confocal and non-linear imaging to fulfil unmet clinical needs in terms of the specificity and accuracy of urothelial cancer diagnosis and therapy monitoring. The project is coordinated by Tampere University (Finland) and carried out in cooperation with leading European research organizations including Aston Insitute of Photonic Technologies—AIPT (UK), Consiglio Nazionale delle Ricerche—CNR (Italy), Institute of Photonic Sciences—ICFO (Spain), University of Milan-Bicocca, Modus Research and Innovation Ltd. (UK) and University of Florence (Italy), as well as industrial partners: Ampliconyx Oy (Finland), Femtonics Ltd. (Hungary), HC Photonics (Taiwan), and LEONI Fiber Optics GmbH (Germany).

A central area of research in nonlinear science is the study of instabilities that drive the emergence of extreme events. Unfortunately, experimental techniques for measuring such phenomena often provide only partial characterization. For example, real-time studies of instabilities in nonlinear fibre optics frequently use only spectral data, precluding detailed predictions about the associated temporal properties. Here, we show how Machine Learning can overcome this limitation by predicting statistics for the maximum intensity of temporal peaks in modulation instability based only on spectral measurements. Specifically, we train a neural network based Machine Learning model to correlate spectral and temporal properties of optical fibre modulation instability using data from numerical simulations, and we then use this model to predict the temporal probability distribution based on high-dynamic range spectral data from experiments. These results open novel perspectives in all systems exhibiting chaos and instability where direct time-domain observations are difficult.