A method of evaluating the integrity of at least a first backup image associated with a protected machine, the method comprising: (a) booting, via an operating system managing a first device (104, 106, 202), a virtual machine based on at least the first backup image; (b) taking, via the operating system managing the first device (104, 106, 202), a plurality of samples (124, 126, 204, 402) of a display output of the virtual machine over a first duration; (c) communicating the plurality of samples (124, 126, 204) to a prediction module located on a second device, the prediction module comprising a trained model; (d) determining, via the trained model of the prediction module, state classifications for one or more samples of the plurality of samples (124, 126, 204, 402), based on one or more determined attributes associated with the one or more samples of the plurality of samples (124, 126, 204), wherein the trained model is an artificial neural network trained using a plurality of training images from one or more operating systems; (e) adding each sample of the plurality of samples (124, 126, 204, 402) and each associated state classifications to a memory array (210, 400); (f) testing at least a first portion of the memory array (210, 400) to determine a consistency class based on the plurality of samples (124, 126, 204, 402) and the state classifications stored in at least the first portion of the memory array (210, 400), wherein the consistency class represents a steady state classification over a particular period of time; (g) based on the consistency class determined based on at least the first portion of the memory array (210, 400), performing at least one of the following: repeating steps (b) through (g) and testing at least a second portion of the memory array (210, 400) to determine a consistency class based on the plurality of samples (124, 126, 204, 402) and state classifications stored in at least the second portion of the memory array (210, 400), wherein the second portion of the memory array (210, 400) includes one or more samples and one or more state classifications not included in the first portion of the memory array (210, 400); and generating a report concerning the consistency class determined in connection with at least the first portion of the memory array (210, 400), the report comprising a representative screenshot selected from the screenshots stored within the memory array (210, 400) and the consistency class. (a) booting, via an operating system managing a first device (104, 106, 202), a virtual machine based on at least the first backup image; (b) taking, via the operating system managing the first device (104, 106, 202), a plurality of samples (124, 126, 204, 402) of a display output of the virtual machine over a first duration; (c) communicating the plurality of samples (124, 126, 204) to a prediction module located on a second device, the prediction module comprising a trained model; (d) determining, via the trained model of the prediction module, state classifications for one or more samples of the plurality of samples (124, 126, 204, 402), based on one or more determined attributes associated with the one or more samples of the plurality of samples (124, 126, 204), wherein the trained model is an artificial neural network trained using a plurality of training images from one or more operating systems; (e) adding each sample of the plurality of samples (124, 126, 204, 402) and each associated state classifications to a memory array (210, 400); (f) testing at least a first portion of the memory array (210, 400) to determine a consistency class based on the plurality of samples (124, 126, 204, 402) and the state classifications stored in at least the first portion of the memory array (210, 400), wherein the consistency class represents a steady state classification over a particular period of time; (g) based on the consistency class determined based on at least the first portion of the memory array (210, 400), performing at least one of the following: repeating steps (b) through (g) and testing at least a second portion of the memory array (210, 400) to determine a consistency class based on the plurality of samples (124, 126, 204, 402) and state classifications stored in at least the second portion of the memory array (210, 400), wherein the second portion of the memory array (210, 400) includes one or more samples and one or more state classifications not included in the first portion of the memory array (210, 400); and generating a report concerning the consistency class determined in connection with at least the first portion of the memory array (210, 400), the report comprising a representative screenshot selected from the screenshots stored within the memory array (210, 400) and the consistency class. repeating steps (b) through (g) and testing at least a second portion of the memory array (210, 400) to determine a consistency class based on the plurality of samples (124, 126, 204, 402) and state classifications stored in at least the second portion of the memory array (210, 400), wherein the second portion of the memory array (210, 400) includes one or more samples and one or more state classifications not included in the first portion of the memory array (210, 400); and generating a report concerning the consistency class determined in connection with at least the first portion of the memory array (210, 400), the report comprising a representative screenshot selected from the screenshots stored within the memory array (210, 400) and the consistency class.