25 Leonardo Ayala Computer Vision News spectral information by collecting light in many narrow regions of the optical spectrum. This property allows SI to encode functional properties (e.g., oxygenation and ischemia monitoring) in diffuse reflectance data. However, its translation to clinical practice is currently hindered by a number of limitations such as image recording speed, controlled illumination restrictions, and high inter-patient data heterogeneity. During my PhD I worked on the development of systems and methods to translate spectral imaging into clinical practice. More precisely, my team and I addressed three main challenges: 1) slow imaging devices, 2) controlled illumination restrictions, and 3) high inter-patient variability and eliminating the need for contrast agents. Among these challenges, the later one was at the core of my dissertation. Emerging imaging modalities such as SI innately face the challenge of limited data availability. Under conditions of data scarcity, high interpatient data variability substantially impedes the development of clinically usable AI models. The challenge arises from the bias introduced in AI models when the distribution of the deployment population differs significantly from the population on which the models were trained. To mitigate this bias, an out-ofdistribution (OoD) detection approach was developed to monitor ischemia during surgery in a personalized manner, which only requires data from one single patient for model training without the need for contrast agents (Fig. 1). More details about this approach can be found in our Science Advances publication. In summary, my work pioneered an entirely novel functional imaging paradigm based on spectral techniques and specifically removed common roadblocks to clinical translation. In doing so, it opens up new avenues of clinical functional imaging to the benefit of patients in surgery and beyond.
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