The main findings of this study are as follows:
The baPWV prediction parameters shown in
Figure 2 were categorized and interpreted as follows: First, parameters designed by humans based on previous knowledge as being relevant to baPWV include (a) normalized age, (d) normalized AA, (e) regression age, and (h) regression AA. Second, parameters that are self-taught and meant to be learned by AI (i.e., via the end-to-end process) are (b) and (f), the single-input predicted baPWVs using CFPs only. Third, the parameters that allow AI to learn being given additional arteriovenous information are (c) and (g), the three-input predicted baPWVs with or without regression correction, both of which used CFPs + arteriolar and venular probability maps as channel attention by humans with appropriate knowledge.
In this study, the accuracy of baPWV prediction parameters was higher in both the (b) single-input and (c) three-input models generated by AI than in (d) AA designated by humans, suggesting that an index that arbitrarily cuts out a certain aspect based on past knowledge is not as robust as an AI-generated index. Numerical values derived from a series of past human observations are traditionally referred to as indexes, such as the body mass index and cardiothoracic ratio. In this study, AA for the (d) and (h) models and age for the (a) and (e) models can be considered in the same category. Human-fixed indexes are useful but have the disadvantage of referring to only certain aspects. In contrast, AI excels at reducing the dimensions of various complex data and creates an index based on the training data in excess of the details. As expected, the AI-based analysis was more accurate than the human-selected indexes in our current study.
Interestingly, (f) single-input regression-predicted baPWV was overtaken by (h) regression AA prepared by humans. This indicates that the single-input model would have already picked up pieces of information about age, sex, and SBP from CFPs, with blurring possibly due to cataract, macular reflection changes, vessel narrowing, fundus coloration, disc shape, etc. Therefore, adding multiple regression analysis of age, sex, and SBP did little to improve the accuracy and was surpassed by human-designated AA by multiple regression. Moreover, AI has a drawback in that it is difficult to eliminate confounding factors; when trying to detect arteriosclerosis from CFPs, a major confounding factor is a feature that is strongly related to age, such as blurring of CFPs due to cataract. The more age-related modifications that are contained in CFPs, the less likely retinal vascular changes are to be detected. Paradoxically, the high predictive accuracy of (a) normalized age, among the non-regressed parameters (a) to (d), confirms that age is strongly correlated with arteriosclerosis. However, predicting baPWV from age implies the same baPWV at a certain age in each individual, which means it is a clinically meaningless indicator, and its strong correlation makes age a major confounding factor when estimating pathological arteriosclerosis. The single-input model, in which AI was self-taught only with the CFPs, which contain more confounding factors and focus less on the retinal vessels, was proven to be a less satisfactory model.
On comparing the three-input model with the single-input model, the (g) three-input regression-predicted baPWV model had the best prediction accuracy, suggesting that providing AI with appropriate attention improves its analytical accuracy. In this study, channel attention to arteriolar and venular probability maps did not mean complete additional information to AI. The retinal arteriovenous information (i.e., probability maps) was generated from CFPs based on another neural network, and the information was essentially embedded in the CFPs. In other words, the new instruction we gave to the AI was merely a flow of ideas to focus on the retinal arterioles and venules. If AI were to find this flow from the CFPs alone, it would have to prepare a much larger neural network and spend considerable time in mining this flow. It would thus be important to incorporate appropriate human knowledge into the design of AI-based neural networks, and appropriate human knowledge (i.e., an expert's channel attention that provides a flow of ideas) can help improve AI accuracy.
The limitations of this study include the fact that the AA and VA obtained from the CFPs may be affected by the CFP alignment, magnification, isotropy, and media opacity. These aspects should be carefully analyzed in future studies to assess the need for AA and VA corrections based on refractive values, axial length, and image alignment. In the present study, the number of subjects with actual PWV values was relatively small to be handled by deep learning; thus, a provisional baPWV index obtained by shallow learning from AA, VA, age, sex, and blood pressure was used for pretraining. Training using true baPWVs from a larger number of cases would yield higher accuracy.
In summary, the three-input model using arteriolar and venular probability maps as channel attention could predict baPWV with augmented accuracy. The three-input model, which incorporated the knowledge that retinal vessels are affected by arteriosclerosis into the neural network design, could improve the prediction accuracy of baPWV without consuming a large amount of time and quantitative resources. The excellent prediction accuracy by channel attention based on the HURVS model, which can precisely identify retinal vessels from CFPs, confirmed that arterioles and venules are relevant regions for arteriosclerosis, and its application to AI successfully improved the prediction accuracy for baPWVs.