A computer-implemented method for estimating tissue viability of a patient's tissue, the method comprising: receiving a patient-specific vessel model or tissue model of a patient anatomy, the patient-specific vessel model or tissue model being an anatomical model of a biological tissue or system of said patient; receiving one or more patient-specific physiological parameters for one or more physiological states; and estimating one or more characteristics of tissue viability at one or more locations in the target tissue in the one ore more physiological states, by applying a trained machine learning algorithm to the one or more patient-specific physiological parameters, said trained machine learning algorithm being trained to map the one or more physiological parameters at the one or more locations of the vessel model and/or target tissue model to the one or more estimated characteristics of tissue viability at those locations, said trained machine learning algorithm including feature weights defining an extent to which the one or more physiological parameters are predictive of the one or more characteristics of tissue viability; wherein estimating the one or more characteristics of tissue viability involves determining an estimation of supplied blood at one or more vessel locations of the patient-specific vessel model, while the patient is in the one or more physiological states, and/or is based on a measurement or an estimation of blood flow characteristics in the one or more physiological states, and/or includes an estimation of the perfusion territories of the target tissue related to the patient-specific vessel model, wherein the one or more characteristics of tissue viability includes a combination of fractional flow reserve, flow magnitude and flow direction; and the method comprises outputting the estimated one or more characteristics of tissue viability to an electronic storage medium or display. receiving a patient-specific vessel model or tissue model of a patient anatomy, the patient-specific vessel model or tissue model being an anatomical model of a biological tissue or system of said patient; receiving one or more patient-specific physiological parameters for one or more physiological states; and estimating one or more characteristics of tissue viability at one or more locations in the target tissue in the one ore more physiological states, by applying a trained machine learning algorithm to the one or more patient-specific physiological parameters, said trained machine learning algorithm being trained to map the one or more physiological parameters at the one or more locations of the vessel model and/or target tissue model to the one or more estimated characteristics of tissue viability at those locations, said trained machine learning algorithm including feature weights defining an extent to which the one or more physiological parameters are predictive of the one or more characteristics of tissue viability; wherein estimating the one or more characteristics of tissue viability involves determining an estimation of supplied blood at one or more vessel locations of the patient-specific vessel model, while the patient is in the one or more physiological states, and/or is based on a measurement or an estimation of blood flow characteristics in the one or more physiological states, and/or includes an estimation of the perfusion territories of the target tissue related to the patient-specific vessel model, wherein the one or more characteristics of tissue viability includes a combination of fractional flow reserve, flow magnitude and flow direction; and the method comprises outputting the estimated one or more characteristics of tissue viability to an electronic storage medium or display. <EOS>