Research Paper Volume 10, Issue 11 pp 3249—3259

PhotoAgeClock: deep learning algorithms for development of non-invasive visual biomarkers of aging

Figure 9. Examples of PhotoAgeClock performance. (A) Cases when the trained model produced the lowest errors on the test set. (B) Cases when the trained model overestimated age the most on the test set. (C) Cases when the trained model underestimated the age the most on the test set. True vs. predicted age is labeled. Eye areas were erased for anonymity purposes but were present in the actual dataset pictures.