A system for determining characteristics of tissue within a body of a patient, comprising: a medical device including: a distal end configured to be advanced within the body of the patient; at least one aperture at the distal end; a laser emitter operable to emit monochromatic light out from the distal end via the at least one aperture and onto target tissue; and at least one photodetector array configured to: receive light incident on the at least one aperture that is one or more of scattered by or reflected from the target tissue; and generate Raman spectroscopy image data based on monochromatic light incident on the at least one aperture, the Raman spectroscopy image data including an array of intensity values represented as a two-dimensional image; and an image processing device that is configured to receive the Raman spectroscopy image data from the medical device, and that includes: a memory storing instructions; and at least one processor operatively connected to the memory and configured to execute the instructions to perform operations, including: receive a three-dimensional representation of at least a portion of anatomy of the patient including the target tissue; generate three-dimensional Raman spectroscopy image data by mapping values of the Raman spectroscopy image data to three-dimensional positions in the three-dimensional representation, use the generated three-dimensional Raman spectroscopy image data as input into a machine-learning model trained to identify one or more tissue characteristics of the target tissue based on the three-dimensional Raman spectroscopy image data. a medical device including: a distal end configured to be advanced within the body of the patient; at least one aperture at the distal end; a laser emitter operable to emit monochromatic light out from the distal end via the at least one aperture and onto target tissue; and at least one photodetector array configured to: receive light incident on the at least one aperture that is one or more of scattered by or reflected from the target tissue; and generate Raman spectroscopy image data based on monochromatic light incident on the at least one aperture, the Raman spectroscopy image data including an array of intensity values represented as a two-dimensional image; and a distal end configured to be advanced within the body of the patient; at least one aperture at the distal end; a laser emitter operable to emit monochromatic light out from the distal end via the at least one aperture and onto target tissue; and at least one photodetector array configured to: receive light incident on the at least one aperture that is one or more of scattered by or reflected from the target tissue; and generate Raman spectroscopy image data based on monochromatic light incident on the at least one aperture, the Raman spectroscopy image data including an array of intensity values represented as a two-dimensional image; and receive light incident on the at least one aperture that is one or more of scattered by or reflected from the target tissue; and generate Raman spectroscopy image data based on monochromatic light incident on the at least one aperture, the Raman spectroscopy image data including an array of intensity values represented as a two-dimensional image; and an image processing device that is configured to receive the Raman spectroscopy image data from the medical device, and that includes: a memory storing instructions; and at least one processor operatively connected to the memory and configured to execute the instructions to perform operations, including: receive a three-dimensional representation of at least a portion of anatomy of the patient including the target tissue; generate three-dimensional Raman spectroscopy image data by mapping values of the Raman spectroscopy image data to three-dimensional positions in the three-dimensional representation, use the generated three-dimensional Raman spectroscopy image data as input into a machine-learning model trained to identify one or more tissue characteristics of the target tissue based on the three-dimensional Raman spectroscopy image data. a memory storing instructions; and at least one processor operatively connected to the memory and configured to execute the instructions to perform operations, including: receive a three-dimensional representation of at least a portion of anatomy of the patient including the target tissue; generate three-dimensional Raman spectroscopy image data by mapping values of the Raman spectroscopy image data to three-dimensional positions in the three-dimensional representation, use the generated three-dimensional Raman spectroscopy image data as input into a machine-learning model trained to identify one or more tissue characteristics of the target tissue based on the three-dimensional Raman spectroscopy image data. receive a three-dimensional representation of at least a portion of anatomy of the patient including the target tissue; generate three-dimensional Raman spectroscopy image data by mapping values of the Raman spectroscopy image data to three-dimensional positions in the three-dimensional representation, use the generated three-dimensional Raman spectroscopy image data as input into a machine-learning model trained to identify one or more tissue characteristics of the target tissue based on the three-dimensional Raman spectroscopy image data.