Part 3 of the algorithm (feature point detection) relies on the assumption that a high-quality representative frame, in general, has a greater number of feature points than other frames that are lower in quality or less suitable to represent the site. In order to test this assumption, we compared the performance of the full algorithm (Parts 1, 2, and 3) to a modified algorithm in which Parts 1 and 2 were carried out normally, but in Part 3, feature point-based selection was replaced by random selection of one of the remaining candidate frames. In the oral data set, random frame selection in Part 3 reduced the sensitivity from 69% to 64%, reduced specificity from 76% to 69%, and reduced the AUC from 0.78 to 0.69. In the esophageal data set, random frame selection in Part 3 resulted in the same sensitivity (84%), reduced specificity from 92% to 86%, and reduced the AUC from 0.93 to 0.91. These results support the utility of feature point-based selection of high-quality representative frames from in high-resolution microendoscopy videos.