The major findings of this study are outlined as follows: (1) The mean choroidal thickness reduced significantly for every 20-year increase in age. (2) A negative correlation was demonstrated between age and choroidal thickness. (3) Mask R-CNN model achieved satisfactory prediction accuracy in subjects with choroidal thicknesses of less than 280 µm.
We established that choroidal thickness decreased as age increased in subjects without significant ocular diseases or a history of intraocular operation. This finding is consistent with those of previous studies including Japanese, Korean, Chinese, and Iranian populations [8–10, 16, 17]. As to other factors, refractive error is considered to be negatively correlated with choroidal thickness. However, our study revealed no significant correlation between refractive error and choroidal thickness in our participants, for whom the refractive error was between −6 and +6 D. This finding is in line with the results of Fujiwara et al. (p = 0.10, R2 = 0.1) [19]. Furthermore, on the basis of a multiple regression analysis, Ikuno et al. reported that axial length was not significantly associated with choroidal thickness (p = 0.22) [16].
For our Mask R-CNN model predictions, the mean choroidal thickness was 198.3 ± 58.4 µm, which was lower than that obtained using physician sketches (229.5 ± 70.6 µm). The error observed for our model (8.56 pixels) is slightly higher than the average error (4.56 pixels) observed for a previously proposed model [15]. We noticed found that the prediction error was higher in the 20–39-year group, with thicker choroids, than in the other two groups. Figure 4 also depicts the difficulty in accurately delineating the choroidal outer boundary around the CSI when a thicker choroid is presented. We speculate that prediction accuracy could be affected in cases with thicker choroids. Possible reasons are that the quality and visibility of the CSI could be poorer in images of thicker choroids. In addition, the training and testing datasets comprised images of subjects aged from 21 to 79 years, for whom the choroidal thickness and morphology varied; such variations might engender challenges in accurately predicting the choroidal thickness. Further analysis demonstrated that the average prediction error observed for subjects with choroidal thicknesses of more than 280 µm was significantly higher and more variable than that observed for those with choroidal thicknesses of less than 280 µm. Specifically, the prediction error was 6.49 pixels in subjects with choroidal thicknesses of less than 280 µm, and this value was not related to the change in choroidal thickness. This finding indicates that the proposed deep learning model is reliable and applicable under this condition. However, in subjects with choroidal thicknesses of greater than 280 µm, the prediction error varied, and prediction error was positively correlated with choroidal thickness. This demonstrates that the proposed model is more suitable for images from subjects with choroidal thicknesses that do not exceed 280 µm. Previous studies have demonstrated that the successful measurement rate decreased as SFCT increased. A previous study reported that enhanced depth imaging (EDI)-OCT [20] appeared to improve the successful measurement rate, but obvious differences existed between thinner and thicker choroids [21]. Therefore, for subjects with extremely thick choroids and pachychoroid diseases, such as PCV or CSCR, their findings and data should be interpreted cautiously.
The choroid is stratified into three layers: the choriocapillaris with small vessels in the superficial layer; Sattler’s layer with medium-sized vessels in the middle; and Haller’s layer, the outer layer with large vessels. Choroidal morphology and thickness are influenced by not only physiological changes but also pathological factors [22]. Tissue water content and vascular endothelial growth factor (VEGF) are possible factors contributing to reduced choroidal thickness in healthy elderly people. A previous study that applied a water-drinking test revealed that choroidal thickness expanded when the amount of water in the body increased [23]. Considering that the amount and proportion of water in the body gradually decrease with age, this may explain the findings in our study. In addition, The RPE secretes VEGF toward the basal side of the choroid, and it plays an essential role in choroidal development [24]. The VEGF receptors are located in the choriocapillaris. Previous studies have reported that as age increases, the diameter of the choriocapillaris and thickness of the choroid shrink while the thickness of Burch’s membrane increases [25, 26]. The accumulation of lipid content with age is considered to cause the thickening of Bruch’s membrane, which possibly occludes the movement of water-soluble agents between the RPE and the choroid. Reduced VEGF secretion into the choriocapillaris may lead to the shrinking of the choroid. In addition to normal aging physiology, the alternation of the choroid has been reported in chorioretinal disease; for example, Haller’s layer, Sattler’s layer, and choroidal volume or thickness have been reported to be significantly decreased in certain patients with diabetes mellitus or AMD [27, 28]. Reduced choroidal thickness was also noted in retinitis pigmentosa [29].
Previous studies have reported several approaches to evaluating choroidal thickness in different age groups. Such approaches mainly involve manual measurements of choroidal thickness, including SFCT, at a single site or at sites located at different distances (e.g., 1 or 3 mm) from the fovea superiorly, inferiorly, temporally, and nasally. SFCT is generally the highest among the measured sites [9, 10, 16, 17]. However, the mentioned approaches have several potential limitations; for example, they cannot be used to fully evaluate the appearance and shape of the choroid, and the overall thickness might be miscalculated in a discontinuous or unsmooth choroidal contour. To conduct a thorough examination of choroidal topographic features, Ouyang et al. proposed choroidal thickness mapping under the concept of spatial distribution using SD-OCT [30]. Hirata et al. introduced a swept-source OCT (SS-OCT) approach for choroidal volume mapping [31]. Furthermore, Chhablani et al. reported that EDI-OCT exhibited high repeatability and reproducibility in manual choroidal volume measurements [32]. Choroidal spatial distribution indices (CSDIs), derived from choroidal volume, were proposed for quantifying the choroidal topographic distribution [33].
Some of the aforementioned methods measure only specific sites of the choroid rather than the entire layer, and others require manual choroidal segmentation. This might be a time-consuming and operator-dependent process, making it difficult to build a relatively large population database. Our proposed Mask R-CNN model is advantageous because it is based on deep learning. Our model estimates choroidal thickness by measuring numerous continuous points through automatic segmentation, thus providing an extensive and intact viewpoint for evaluation, in contrast to previously reported approaches. In addition, automatic segmentation and measurement with high accuracy can facilitate the establishment of larger and more extensive databases more rapidly and consistently.
The present study has certain limitations. First, the number of subjects in each group was not high. Further research with a larger sample size might provide more comprehensive analyses, such as analyses stratified by each decade of age. Second, especially in for thicker choroids, precisely delineating the choroidal boundary was challenging. Lower predictive accuracy in thicker choroids has been reported. EDI-OCT and SS-OCT may address this problem [34–36].
The application of the Mask R-CNN model revealed the relationship between choroidal thickness and age in this study. Through this model, future studies can compare choroidal morphologies in different axial length or in various ocular diseases, such as AMD, PCV, and CSCR. In combination with the deep learning model, further evaluation might be examined automatically, such as choroidal volume mapping, CSDI, or choroidal vascularity index assessment [37, 38]. In this way, the choroid can be evaluated using detailed and robust information rapidly and reliably.
Overall, by utilizing the Mask R-CNN model for choroidal boundary depiction, our study revealed that the mean choroidal thickness decreased with age, indicating a negative correlation with age. The model achieved acceptable predictive accuracy, especially in thinner choroids. We anticipate applying this efficient model in further research with a larger sample size and more heterogeneous population.