The normal-CNN results showed variable success at automatically identifying cones in images of CHM (
Fig. 5 top row). When using grader 1A as ground truth, the normal-CNN resulted in a Dice coefficient of 0.71 ± 0.21 (
Table 4). This is lower than both the intra- and intergrader Dice measurements,
P < 0.0001 for both. Calculated bound cone density for the normal-CNN was significantly lower than manual cone density measurements,
P < 0.0001 (
Fig. 6). Retraining the CNN on the CHM images (CHM-1A-CNN) improved automated cone identifications (see
Fig. 5 bottom row). The CHM-1A-CNN yielded a higher true positive rate (0.88 ± 0.14) in comparison to the true positive rate for the normal-CNN (0.64 ± 0.26),
P < 0.0001 (
Fig. 7 and see
Table 4). However, the false negative rate was also higher (
P < 0.0001), resulting in some images showing an improved Dice coefficient, whereas others showed a reduced Dice coefficient (see
Fig. 7). On average, the Dice coefficient did increase for CHM-1A-CNN in comparison to normal-CNN,
P < 0.0001 (see
Table 4). As a result of both higher true and false positive rates, CHM-1A-CNN resulted in higher cone densities for all 204 ROIs in comparison to normal-CNN (see
Fig. 6). There was no statistical difference for the mean cone density measured by grader 1A and CHM-1A-CNN (
P = 0.26). However, CHM-1A-CNN overestimated cone density in images with low manual cone density and underestimated cone density with images of high manual cone density (see
Fig. 6). This resulted from an increasing false positive rate with decreasing manual cone density and a decreasing true positive rate with increasing cone density (
Fig. 8).