An artificial intelligence, AI, platform for detecting and sizing a lesion in real time during a clinical procedure, comprising: a trained classifier that includes a deep learning model trained to detect lesions and reference objects in image data; and a real time video analysis system that receives a video feed during a clinical procedure, uses the trained classifier to determine if a video frame from the video feed has both a lesion and a reference object, calculates an actual size of the lesion based on a pixel size of both the lesion and the reference object, and outputs an indication that the lesion was detected and the actual size of the lesion, wherein the pixel size of the lesion is determined by: capturing a rectangular cut out image containing the lesion; enhancing the cut out image using image smoothing; using an edge detection algorithm to detect contours of the lesion; and calculating new rectangular coordinates of the lesion based on determined coordinates of the contours to provide a pixel size of the lesion, Dpx, wherein the pixel size of the reference object is determined by: capturing a rectangular cut out image containing the reference object; enhancing the cut out image using image smoothing; identifying edges of the reference object and imposing straight lines along the edges; filtering the straight lines based on respective line lengths, line inclinations and coordinates of intersection of the straight lines with boundaries of the cut out image to identify a pair of intersecting lines; and determining a pixel size of the reference object, Fpx, based on the pair of intersecting lines, wherein the actual size of the lesion D is calculated as: D = Dpx * F / Fpx, wherein F is an actual size of the reference object. a trained classifier that includes a deep learning model trained to detect lesions and reference objects in image data; and a real time video analysis system that receives a video feed during a clinical procedure, uses the trained classifier to determine if a video frame from the video feed has both a lesion and a reference object, calculates an actual size of the lesion based on a pixel size of both the lesion and the reference object, and outputs an indication that the lesion was detected and the actual size of the lesion, wherein the pixel size of the lesion is determined by: capturing a rectangular cut out image containing the lesion; enhancing the cut out image using image smoothing; using an edge detection algorithm to detect contours of the lesion; and calculating new rectangular coordinates of the lesion based on determined coordinates of the contours to provide a pixel size of the lesion, Dpx, capturing a rectangular cut out image containing the lesion; enhancing the cut out image using image smoothing; using an edge detection algorithm to detect contours of the lesion; and calculating new rectangular coordinates of the lesion based on determined coordinates of the contours to provide a pixel size of the lesion, Dpx, wherein the pixel size of the reference object is determined by: capturing a rectangular cut out image containing the reference object; enhancing the cut out image using image smoothing; identifying edges of the reference object and imposing straight lines along the edges; filtering the straight lines based on respective line lengths, line inclinations and coordinates of intersection of the straight lines with boundaries of the cut out image to identify a pair of intersecting lines; and determining a pixel size of the reference object, Fpx, based on the pair of intersecting lines, wherein the actual size of the lesion D is calculated as: D = Dpx * F / Fpx, wherein F is an actual size of the reference object. capturing a rectangular cut out image containing the reference object; enhancing the cut out image using image smoothing; identifying edges of the reference object and imposing straight lines along the edges; filtering the straight lines based on respective line lengths, line inclinations and coordinates of intersection of the straight lines with boundaries of the cut out image to identify a pair of intersecting lines; and determining a pixel size of the reference object, Fpx, based on the pair of intersecting lines, wherein the actual size of the lesion D is calculated as: D = Dpx * F / Fpx, wherein F is an actual size of the reference object.